@article {pmid41343567,
year = {2025},
author = {He, Y and Yan, Z and Liang, Y and Yu, Y},
title = {Abnormal brain network reconfiguration in neuropsychiatric disorders across cognitive decline, Depression, and Schizophrenia.},
journal = {PloS one},
volume = {20},
number = {12},
pages = {e0337470},
doi = {10.1371/journal.pone.0337470},
pmid = {41343567},
issn = {1932-6203},
mesh = {Humans ; *Schizophrenia/physiopathology ; *Cognitive Dysfunction/physiopathology ; Male ; Female ; Aged ; *Brain/physiopathology ; *Depressive Disorder, Major/physiopathology ; Electroencephalography ; Middle Aged ; Alzheimer Disease/physiopathology ; *Nerve Net/physiopathology ; Machine Learning ; Case-Control Studies ; },
abstract = {OBJECTIVE: Neuropsychiatric disorders are characterized by high complexity and comorbidity, imposing a substantial burden on both patients and society. However, their elusive pathogenic mechanisms impede accurate clinical diagnosis and effective interventions. To overcome this challenge, the present study proposes a novel framework to quantify and characterize these disorders.

METHODS: Routine electroencephalogram (EEG) recordings are acquired from 236 subjects, including patients with Alzheimer's disease (AD), mild cognitive impairment (MCI), major depressive disorder (MDD), schizophrenia, and healthy controls (HCs). Time-varying functional brain networks are constructed by phase locking value (PLV) analysis on band-pass filtered EEG signals. Subsequently, the nodal behavior characteristics within these dynamic brain networks are quantified by integrating robust dynamic community detection algorithms and network reconfiguration metrics.

RESULTS: Significant intergroup differences in network reconfiguration metrics are identified based on the dynamic community structures (FDR-corrected p < 0.001). Lower cohesion strength is observed across all neuropsychiatric disorders compared to healthy controls, consistent across all frequency bands and recording sites. When six machine learning classifiers are trained on these metrics, the maximum classification accuracies exceeded 80%. Since lower cohesion strength is a prominent potential biomarker for neuropsychiatric disorders, it was then selected as the independent input feature for random forest classifier, and the classification accuracy achieved 0.85 for schizophrenia group, 0.88 for both the MCI and MDD group, and 0.82 for the AD group.

CONCLUSIONS: Our findings indicate that the framework based on dynamic network reconfiguration metrics effectively captures both the shared and disorder-specific alterations in brain network dynamics among neuropsychiatric disorders.

SIGNIFICANCE: Dynamic community structure advances our understanding of the pathological mechanisms underlying neuropsychiatric disorders. This study provides novel insights that may inform the development of more targeted and effective therapeutic strategies.},
}

Humans
*Schizophrenia/physiopathology
*Cognitive Dysfunction/physiopathology
Male
Female
Aged
*Brain/physiopathology
*Depressive Disorder, Major/physiopathology
Electroencephalography
Middle Aged
Alzheimer Disease/physiopathology
*Nerve Net/physiopathology
Machine Learning
Case-Control Studies

@article {pmid41343337,
year = {2025},
author = {Dong, H and Chen, W and Lv, X and Jiang, Y and Cheng, M and Chang, A and Zhou, Y and Wang, T and Zhang, Y and Li, Z and Zhou, Y and Xu, M},
title = {A Turn-On Fluorescent and Ratiometric Electrochemical Dual-Mode Probe for Hydrogen Peroxide Detection in Brain Microdialysates of Alzheimer's Disease Mice.},
journal = {Analytical chemistry},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.analchem.5c05358},
pmid = {41343337},
issn = {1520-6882},
abstract = {Hydrogen peroxide (H2O2), a pivotal reactive oxygen species (ROS), is closely linked to oxidative stress in the pathogenesis of Alzheimer's disease (AD). Herein, we report a dual-mode probe (Re-PS) integrating turn-on fluorescence and ratiometric electrochemistry for the selective detection of H2O2 in brain microdialysates of AD model mice. The probe is constructed using resorufin (Re) as a dual-signal reporter and a pentafluorobenzenesulfonyl (PS) group as the H2O2-responsive unit. Upon reaction with H2O2, the PS group undergoes nucleophilic substitution, leading to the release of Re; this process triggers a fluorescence "turn-on" response and generates a ratiometric electrochemical signal. Compared with ester-based probes, Re-PS shows superior stability due to the strong electron-withdrawing effect of fluorine atoms in the PS group. The fluorescence mode achieves a detection limit (LOD) of 50 nM, while the electrochemical detection mode (using a carbon fiber microelectrode modified with carbon nanotubes (CFME/CNT)) has a detection range of 1.0-50 μM. Both modes exhibit excellent selectivity against other ROS and biomolecules. In vivo microdialysis analysis reveals significantly elevated H2O2 levels in the brains of AD mice (28.6 ± 3.2 μM) compared with wild-type mice (10.3 ± 1.8 μM). This dual-mode strategy enables cross-validation, providing a reliable tool for monitoring oxidative stress in neurodegenerative diseases.},
}

@article {pmid41343238,
year = {2025},
author = {Chérot, H and Pred'homme, T and Thai, R and Théodoro, F and Castelli, F and Theillet, FX},
title = {In-Cell Residue-Resolved NMR of Micromolar α-Synuclein and Tau at 310 K.},
journal = {Journal of the American Chemical Society},
volume = {},
number = {},
pages = {},
doi = {10.1021/jacs.5c15061},
pmid = {41343238},
issn = {1520-5126},
abstract = {Aggregates of nonglobular proteins are associated with several degenerative disorders, e.g., α-synuclein and tau involved in Parkinson's and Alzheimer's diseases. Do these proteins undergo progressive changes in their conformations and interactions in pathologic situations? In-cell NMR provides atomic-scale information in live cells but, until now, only at ∼283 K in the case of unfolded proteins. Here, we report new labeling and acquisition methods enabling in-cell NMR at 310 K to study these proteins at micromolar concentrations, i.e., native cellular abundances. We used stable human cell lines expressing α-synuclein or tau upon induction in a culture medium supplemented with [13]C-labeled amino acids, or precursors thereof. Acquiring [13]Cα-[13]CO spectra permitted an early residue-resolved analysis of α-synuclein and tau at 310 K and <10 μM in HEK cells at 700 MHz. We detected disordered conformations and patterns of extended cellular interactions for α-synuclein wild-type and two mutants (F4A, A30P), which suggests the appearance of a subpopulation binding to lipid membrane at 310 K. Only the disordered N-terminus of tau was observable, even upon microtubule dismantling by colchicine. This shows that supplementary binding partners interfere with tau in cells. Our approach offers an excellent scalability, in signal, and resolution, up to 1.2 GHz. [13]C-labeling and [13]C-detected NMR spectroscopy in live human cells are thus viable techniques for in-cell structural biology.},
}

@article {pmid41343129,
year = {2025},
author = {Chen, Y and Kong, W and Liu, K and Wei, K and Wen, G and Yu, Y and Zhu, Y},
title = {An Improved Deep Semi-supervised JNMF Method for Biomarker Extraction of Alzheimer's Disease.},
journal = {Journal of molecular neuroscience : MN},
volume = {75},
number = {4},
pages = {159},
pmid = {41343129},
issn = {1559-1166},
support = {No.18ZR1417200//Natural Science Foundation of Shanghai Municipality/ ; },
mesh = {*Alzheimer Disease/diagnostic imaging/genetics/diagnosis ; Humans ; Biomarkers ; Polymorphism, Single Nucleotide ; Algorithms ; Brain/diagnostic imaging/metabolism ; Male ; Female ; },
abstract = {Imaging genetics is an approach that explores the underlying mechanisms of brain disorders such as Alzheimer's disease (AD) by analyzing the correlation between neuroimaging and genetic data. Traditional non-negative matrix factorization (NMF) algorithms are based on linear assumptions, which limits the potential of nonlinear feature extraction among multi-omics data. This study proposes a novel joint-connectivity-based deep semi-supervised non-negative matrix factorization (JCB-DSNMF) model to overcome this limitation and incorporate prior knowledge from both within and between different modalities of data. The model effectively integrates physiological constraints such as connectivity to identify regions of interest (ROI), risk genes, and risk SNP loci associated with AD patients. JCB-DSNMF outperformed other NMF-based algorithms, such as JDSNMF and NMF, in identifying and predicting biologically relevant biomarkers closely related to AD from essential modules. The accuracy of the selected features was further validated by constructing a diagnostic model with high classification accuracy, achieving an AUC value of 0.8621 on the test set. In particular, the brain region Putamen_L and the gene RALGAPB achieved AUC values of 0.903 and 0.924, respectively, highlighting the importance of these features in early AD diagnosis.},
}

*Alzheimer Disease/diagnostic imaging/genetics/diagnosis
Humans
Biomarkers
Polymorphism, Single Nucleotide
Algorithms
Brain/diagnostic imaging/metabolism
Male
Female

@article {pmid41343056,
year = {2025},
author = {Irwin, DJ},
title = {Where do PDD and DLB SYNdromes fit in neuronal alpha-SYNuclein biological frameworks?.},
journal = {Journal of neural transmission (Vienna, Austria : 1996)},
volume = {},
number = {},
pages = {},
pmid = {41343056},
issn = {1435-1463},
support = {National Institute on Aging/AG/NIA NIH HHS/United States ; },
abstract = {Lewy body disorders (LBD) are a spectrum of neurodegenerative diseases characterized by the presence of misfolded neuronal alpha-synuclein (aSYN) pathology in the central and peripheral nervous system. LBDs have heterogeneous clinical presentations, which include dementia with Lewy bodies (DLB), Parkinson's disease (PD), and PD with dementia (PDD). Thus, LBD clinical syndromes (PD/PD/DLB) represent clinicopathologic entities (i.e. constellations of symptoms and supportive biomarkers with a high specificity for underlying aSYN pathology), but clinical features between PDD and DLB largely overlap. Indeed, there is longstanding debate over the utility of the clinical designation between PDD and DLB due to shared underlying pathology, genetic risk factors and prodromal features. Recent advances in the ability to detect pathological aSYN from peripheral fluids/tissues in living patients has ushered in a new era of biological classification of LBD, providing opportunity to improve antemortem diagnosis and facilitate disease-modifying therapeutic trials. The clinical interpretation of these and future aSYN-specific biological tests will be complex, and the reconciliation of classic LBD syndromes with emerging biological classification schemes for LBD and other neurodegenerative disorders is a priority. Indeed, varying burden of aSYN is also found postmortem in > 50% of clinical Alzheimer's disease (AD), and to a lesser frequency as co-pathology in other neurodegenerative disorders, and incidentally in adults without neurologic disease. This review summarizes autopsy-confirmed data on the clinical expression of LBDs and the boundaries between PDD, DLB and mixed-pathology AD to inform the interpretation of emerging biological tests for aSYN and biological classification schemes for LBD and AD.},
}

@article {pmid41343028,
year = {2025},
author = {Patil, V and Sharma, A and Parekh, B and Farah, H and Jyothi, SR and Mishra, S and Nanda, A and Al-Hasnaawei, S and Mishra, MK},
title = {Targeting Microglial Activation to Modulate Neuroinflammation in Alzheimer's Disease.},
journal = {Neuromolecular medicine},
volume = {27},
number = {1},
pages = {76},
pmid = {41343028},
issn = {1559-1174},
mesh = {*Alzheimer Disease/drug therapy/immunology/pathology ; *Microglia/physiology/drug effects/immunology ; Humans ; *Neuroinflammatory Diseases/drug therapy ; Animals ; Signal Transduction/drug effects ; Receptors, Immunologic/antagonists & inhibitors/physiology ; *Molecular Targeted Therapy ; Membrane Glycoproteins/physiology/antagonists & inhibitors ; Neuroprotective Agents/therapeutic use/pharmacology ; Plaque, Amyloid ; Amyloid beta-Peptides ; },
abstract = {Alzheimer's disease is a multifaceted neurodegenerative condition marked by the build-up of amyloid plaques and neurofibrillary tangles that lead to progressive cognitive impairment. Neuroinflammation, especially the activation of microglia, plays a pivotal part in driving this pathology. Microglia are the brain's resident immune cells and can adopt a spectrum of activation states that support either neuroprotection or neurodegeneration. Evidence shows that their phenotypes are highly dynamic and shaped by environmental influences and pathological signals. During the early phases of the disease, microglia tend to assume anti-inflammatory roles that facilitate plaque clearance and promote tissue recovery. Prolonged or dysregulated activation, however, shifts them toward a pro-inflammatory state that amplifies neuronal damage. Several molecular pathways including JAK STAT, PI3K AKT, and MAPK are central to regulating these processes and have emerged as promising therapeutic targets. This review summarizes current insights into microglial phenotypic transitions, the signaling mechanisms governing their activation, and the therapeutic potential of modulating neuroinflammation. Enhancing the neuroprotective capacity of microglia, suppressing chronic inflammatory responses, and targeting key receptors such as TREM2 and P2 × 7 represent potential strategies. A deeper understanding of microglial interactions with other glial cells and the molecular drivers of their activation may provide new avenues for slowing or halting the progression of Alzheimer's disease and related neurodegenerative disorders.},
}

*Alzheimer Disease/drug therapy/immunology/pathology
*Microglia/physiology/drug effects/immunology
Humans
*Neuroinflammatory Diseases/drug therapy
Animals
Signal Transduction/drug effects
Receptors, Immunologic/antagonists & inhibitors/physiology
*Molecular Targeted Therapy
Membrane Glycoproteins/physiology/antagonists & inhibitors
Neuroprotective Agents/therapeutic use/pharmacology
Plaque, Amyloid
Amyloid beta-Peptides

@article {pmid41342958,
year = {2025},
author = {Li, X and Liu, Y and Hu, H and Li, S},
title = {Targeting Liquid-Liquid Phase Separation and Autophagy in Alzheimer's Disease: Insights into Molecular Mechanisms and Therapeutic Potential.},
journal = {Neurochemical research},
volume = {51},
number = {1},
pages = {9},
pmid = {41342958},
issn = {1573-6903},
support = {U24A20806//National Natural Science Foundation of China/ ; 82404605//National Natural Science Foundation of China/ ; U23A20510//National Natural Science Foundation of China/ ; BX20220048//National Postdoctoral Program For Innovative Talents/ ; 2022MD723713//China Postdoctoral Science Foundation/ ; },
mesh = {*Alzheimer Disease/metabolism/drug therapy/pathology ; Humans ; *Autophagy/physiology/drug effects ; Animals ; Amyloid beta-Peptides/metabolism ; tau Proteins/metabolism ; Phase Separation ; },
abstract = {Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia, marked by cognitive decline and memory loss. Its multifactorial etiology involves genetic, environmental, and cellular factors, with key pathological features including amyloid-beta (Aβ) plaques and tau tangles. Recent studies have highlighted the roles of liquid-liquid phase separation (LLPS) and autophagy in AD onset and progression. LLPS, an emerging biophysical phenomenon, facilitates protein aggregation and may contribute to early disease stages. Dysregulated autophagy results in the accumulation of toxic proteins, such as Aβ and tau, exacerbating neurodegeneration. This review explores the interplay between LLPS and autophagy in AD, a relationship often overlooked in the literature. It examines their biological mechanisms, synergistic effects on AD pathology, and potential therapeutic strategies. Additionally, we discuss the therapeutic potential of both natural and non-natural compounds in modulating LLPS and autophagy. While compounds like curcumin show promise, a comprehensive framework for their targeted use remains under development. This review provides theoretical support for the advancement of more precise AD therapies.},
}

*Alzheimer Disease/metabolism/drug therapy/pathology
Humans
*Autophagy/physiology/drug effects
Animals
Amyloid beta-Peptides/metabolism
tau Proteins/metabolism
Phase Separation

@article {pmid41342775,
year = {2025},
author = {Jangra, J and Gajanan Bajad, N and Kumar, A and Singh, SK},
title = {Integrated pharmacophore-based virtual screening and molecular modeling approaches for the identification of sigma-2 receptor antagonists as novel therapeutics against Alzheimer's disease.},
journal = {Journal of biomolecular structure & dynamics},
volume = {43},
number = {17},
pages = {10281-10305},
doi = {10.1080/07391102.2024.2446666},
pmid = {41342775},
issn = {1538-0254},
mesh = {*Alzheimer Disease/drug therapy/metabolism ; *Receptors, sigma/antagonists & inhibitors/chemistry/metabolism ; Molecular Docking Simulation ; Humans ; Ligands ; Molecular Dynamics Simulation ; Protein Binding ; *Models, Molecular ; Drug Discovery ; Blood-Brain Barrier/metabolism ; Pharmacophore ; },
abstract = {Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder with a conundrum pathophysiology. Disruption of cholesterol homeostasis in AD is well known. Recently, sigma-2 ligands have been reported for their involvement in abnormal lipid metabolism associated with AD that may lead to disturbance in amyloid-β (Aβ) production, over activity of β- and γ- secretase enzymes, and neuroinflammation. Therefore, targeting sigma-2 receptor inhibition is a plausible mechanism to combat AD. Computational tools aid in screening substantial chemical libraries to unveil potential leads against the desired protein target in less time and cost-effectively. In the present study, five chemical databases (Molport, Mcule, Zinc, ChEMBL, and Enamine) were screened against a pharmacophore model, followed by removing duplicates. The obtained 12,811 hits were subjected to PAINS (Pan assay interference compounds), BRENK (Brenk's rule-based filters), Lipinski's rule of five, structural diversity, and BBB (blood-brain barrier) permeability filters followed by comprehensive molecular docking studies. Further, the top fifteen hits obtained were evaluated based on their predicted pharmacokinetic and toxicity profiles. The binding free energy (ΔG) calculations were carried out for the selected hits by performing an MMPBSA (Molecular mechanics Poisson-Boltzmann surface area) assay followed by regressive MD simulation studies. Finally, ZINC00184959872, ZINC001704299697, and MolPort-046-745-161 were obtained as potential virtual leads against the specific sigma-2 receptor with ΔG values of -34.09, -30.93 and -28.03 kcal/mol, respectively, satisfying all the significant parameters undertaken for the study along with optimum pharmacokinetic properties, minimal toxicity, acceptable RMSD value, and stable protein-ligand complex trajectory throughout the MD simulation run (200 ns).},
}

*Alzheimer Disease/drug therapy/metabolism
*Receptors, sigma/antagonists & inhibitors/chemistry/metabolism
Molecular Docking Simulation
Humans
Ligands
Molecular Dynamics Simulation
Protein Binding
*Models, Molecular
Drug Discovery
Blood-Brain Barrier/metabolism
Pharmacophore

@article {pmid41342719,
year = {2026},
author = {Su, H and Liu, Z and Wei, J and Liu, Y and Zhong, Y and Liu, X and Tan, C and Chen, L},
title = {PDK4 suppresses high glucose-induced microglial ferroptosis by restricting pro-ferroptotic PUFA biosynthesis.},
journal = {Neuroreport},
volume = {37},
number = {1},
pages = {1-10},
doi = {10.1097/WNR.0000000000002234},
pmid = {41342719},
issn = {1473-558X},
mesh = {*Ferroptosis/drug effects/physiology ; Animals ; *Glucose/pharmacology ; Mice ; *Microglia/metabolism/drug effects ; Oxidative Stress/drug effects/physiology ; *Fatty Acids, Unsaturated/biosynthesis/metabolism ; *Pyruvate Dehydrogenase Acetyl-Transferring Kinase/metabolism ; Lipid Peroxidation/drug effects ; Reactive Oxygen Species/metabolism ; Mitochondria/metabolism/drug effects ; },
abstract = {BACKGROUND: Diabetes significantly elevates the risk of neurodegenerative disorders, including Alzheimer's disease and Parkinson's disease, indicating shared pathophysiological mechanisms. While ferroptosis is increasingly implicated in neurodegeneration, microglia - highly vulnerable to ferroptosis - may mediate this link. However, it remains unknown whether high glucose (HG) directly induces microglial ferroptosis.

METHODS: Using HG-treated BV2 microglia, we integrated multiomics profiling (RNA-seq and targeted lipidomics), functional assays, and genetic manipulation of pyruvate dehydrogenase kinase 4 (PDK4) to investigate its role in HG-associated ferroptosis.

RESULTS: HG-induced microglial ferroptosis, characterized by iron overload, elevated malondialdehyde and mitochondrial reactive oxygen species, glutathione peroxidase 4 (GPX4) downregulation, and mitochondrial damage, including loss of membrane potential and ultrastructural disintegration. This was accompanied by upregulated PDK4 expression. PDK4 overexpression attenuated ferroptosis by preserving GPX4, reducing lipid peroxidation, and maintaining mitochondrial integrity; these protective effects were reversed by n-6 polyunsaturated fatty acid (PUFA) supplementation. Conversely, PDK4 knockdown exacerbated ferroptosis via amplified n-6 PUFA synthesis and oxidative stress. Mechanistically, PDK4 acts as a metabolic gatekeeper by restricting acetyl-CoA availability for the synthesis of pro-ferroptotic PUFAs, thereby curtailing iron-dependent lipid peroxidation.

CONCLUSION: PDK4 is a critical regulator of HG-induced microglial ferroptosis, thereby bridging hyperglycemia-induced metabolic dysfunction and neurodegeneration. Our findings nominate PDK4 as a promising therapeutic target for diabetes-linked neurodegenerative diseases.},
}

*Ferroptosis/drug effects/physiology
Animals
*Glucose/pharmacology
Mice
*Microglia/metabolism/drug effects
Oxidative Stress/drug effects/physiology
*Fatty Acids, Unsaturated/biosynthesis/metabolism
*Pyruvate Dehydrogenase Acetyl-Transferring Kinase/metabolism
Lipid Peroxidation/drug effects
Reactive Oxygen Species/metabolism
Mitochondria/metabolism/drug effects

@article {pmid41342684,
year = {2025},
author = {Gierzynski, TF and Reader, JM and Flores, B and Fox-Fuller, JT and Gadwa, R and Bhaumik, A and Heidebrink, J and Giordani, B and Hampstead, BM and Bakulski, KM and Paulson, H and Roberts, JS and Rahman-Filipiak, A},
title = {Experiences of participant burden in an Alzheimer's disease research center longitudinal cohort.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {},
number = {},
pages = {13872877251400787},
doi = {10.1177/13872877251400787},
pmid = {41342684},
issn = {1875-8908},
abstract = {BackgroundParticipant attrition can compromise the statistical power and generalizability of research results. Prior investigations have shown that perceptions of higher research burden are positively associated with participant withdrawal from longitudinal studies.ObjectiveWe measured participants' perceived burden in a cohort of older adult research participants enrolled in a longitudinal study of memory and aging at the Michigan Alzheimer's Disease Research Center (MADRC).MethodsParticipants completed a modified, 22-item version of the Perceived Research Burden Assessment (PeRBA), which quantitatively measures perceptions of research burden. We performed a multiple linear regression analysis to ascertain the associations between individual participant characteristics (e.g., demographic, clinical, and logistical/socioecological factors) and ratings of perceived research burden.ResultsA total of 300 participants completed the PeRBA. Overall burden was relatively low (mean = 36.6, SD = 9.38), with possible scores ranging from 22-110. Participants who self-identified as Black/African American reported significantly higher levels of perceived research burden relative to participants who self-identified as non-Hispanic White (β = 6.91, p < 0.001). Additionally, participants with a dementia diagnosis endorsed significantly higher levels of burden than their cognitively unimpaired counterparts (β = 4.85, p = 0.03). All other independent variables were not significantly associated with burden appraisal (p > 0.05).ConclusionsThe PeRBA is a useful tool for monitoring participant burden, as well as identifying differential levels of self-reported burden within research cohorts. These findings can inform tailored retention strategies that support the sustained engagement of participants, particularly those who may be the most susceptible to research burden.},
}

@article {pmid41342683,
year = {2025},
author = {Pozzi, FE and Spanio, A and Gallo, F and Isgrò, G and Remoli, G and Magi, A and Moscatelli, E and Crisci, E and Negro, G and Appollonio, I and Ferrarese, C and Tremolizzo, L},
title = {Cognitive and social intervention with Go and chess in early and subjective cognitive decline: The COGniChESs study results, with an updated meta-analysis.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {},
number = {},
pages = {13872877251401481},
doi = {10.1177/13872877251401481},
pmid = {41342683},
issn = {1875-8908},
abstract = {BackgroundThere is a growing interest in dementia prevention and scalable cognitive enhancement strategies for individuals at-risk, with or without Alzheimer's disease. Board games have shown potential cognitive and mood benefits, but randomized controlled evidence remains limited and heterogeneous.ObjectiveWe aimed at assessing whether chess and/or Go could improve cognition, mood, and quality of life in individuals with mild cognitive impairment (MCI) and subjective cognitive decline (SCD).MethodsIndividuals with MCI or SCD aged ≥55 years were randomized to one of three arms: chess, Go (each consisting of 12 weekly group sessions), or a waitlist control group. Montreal Cognitive Assessment, digit span, trail making test, categorical fluency, Geriatric Depression Scale, and the World Health Organization Quality of Life scale were administered at baseline and follow-up. We also updated our previously published meta-analysis including these new results.Results69 subjects completed the study. Categorical fluency improved significantly in the games groups (p < 0.05). No between-group differences were found in overall cognition. A significant group × diagnosis × time interaction showed improved quality of life in MCI participants in the games groups (p = 0.002). A group × gender × time interaction revealed reduced depression in females in the games groups (p = 0.013). The updated meta-analysis confirmed a significant effect on depression (standardized mean differences -0.48, p = 0.013), but not on cognition.ConclusionsThe improvements in mood and quality of life, particularly among females and MCI subjects, underscore the psychological value of board games interventions, possibly through their social component. These activities may foster emotional well-being in older adults at risk for Alzheimer's disease, even without cognitive benefits.Clinicaltrials.gov Identifier: NCT06281652.},
}

@article {pmid41342682,
year = {2025},
author = {Stefanova, NA and Maksimova, KY and Tyumentsev, MA and Telegina, DV and Khodunova-Zhukovskaia, II and Rudnitsky, EA and Kolosova, NG},
title = {The early postnatal synapse assembly and expression profiles of synapse-related genes in a sporadic Alzheimer's disease-like pathology.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {},
number = {},
pages = {13872877251396932},
doi = {10.1177/13872877251396932},
pmid = {41342682},
issn = {1875-8908},
abstract = {BackgroundRecent evidence suggests that prerequisites for Alzheimer's disease (AD) can form during prenatal and early postnatal development. These prerequisites have been identified to some extent in OXYS rats: a model of the sporadic form of AD.ObjectiveHere, we continue to study the role of delayed brain maturation in the development of the AD-like pathology much later in OXYS rats.MethodsWe assess synaptic-density changes and gene expression profiles in the prefrontal cortex (PFC) and hippocampus of OXYS and Wistar rats (parental strain; control) between ages "postnatal day 0" (P0) and P20.ResultsWe found that at birth, the synaptic population in the PFC of OXYS rats is half of that in Wistar rats. The proportion of both symmetric (inhibitory) contacts and asymmetric (excitatory) contacts in the hippocampus of OXYS rats at P14 and P20 matched these parameters in Wistar rats at P7 and P14, respectively. The transcriptome analysis of the PFC and hippocampus showed that gene expression profiles related to synapses are different between Wistar and OXYS rats. Next, we identified "age-specific" genes and "brain region-specific" genes whose changes in the expression can obviously contribute to the specific features of synapse formation in OXYS rats. Finally, analyses of cell-specific (neurons, astrocytes, microglia, oligodendrocytes, and endothelial cells) gene expression suggested that at P3-P20 in the PFC and hippocampus, more than 50% of downregulated genes are associated with glia: key regulators of neural-network functioning.ConclusionsCollectively, these data indicate a delay in the formation of interneuronal connections and in their efficiency in the OXYS strain.},
}

@article {pmid41342676,
year = {2025},
author = {Luca, A and Luca, M and Olgiati, P and Ferri, R and Serretti, A},
title = {Sex-specific sleep disturbances worsen psychiatric symptoms and cognitive decline in individuals with Alzheimer's disease.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {},
number = {},
pages = {13872877251400783},
doi = {10.1177/13872877251400783},
pmid = {41342676},
issn = {1875-8908},
abstract = {BackgroundThe possible association between sleep disturbances, neuropsychiatric symptoms and progression of cognitive decline considering sex still need to be clarified.ObjectiveThe aims of the study were to evaluate the possible associations between disturbed sleep (DS) and neuropsychiatric symptoms (NPS) and to evaluate the possible association between DS, cognitive decline progression and caregiver burden focusing on sex-differences.Methods184 participants were collected within the CATIE-AD trial. Based on the response given by the caregiver to the Neuropsychiatric Inventory, patients were classified into AD with disturbed sleep (AD-DS) or AD without disturbed sleep (ADwDS). Cognitive performance and NPS were evaluated. Progression was evaluated with the Δ-Mini-Mental State Examination (MMSE) (baseline MMSE-MMSE at 3-month follow-up). A sex stratified analysis was carried out.ResultsAD-DS performed worse than participants with ADwDS at all the cognitive tests. AD-DS presented more frequently depression, anxiety, aberrant motor behavior, disinhibition and eating disorders. At the sex-stratified analysis, AD-DS women were more frequently disinhibited and depressed than ADwDS. Men with AD-DS presented worse performances at several cognitive tests. Furthermore, various NPS were more frequent in men with AD-DS than in those with ADwDS, including hallucination, agitation, depression, and aberrant motor behavior. The burden was higher in caregivers of men belonging to the AD-DS group. Finally, at the linear regression, adjusting for age, education and MMSE at baseline, the presence of disturbed sleep was related to a more evident decline in MMSE in men (coeff. 2.5; 95%CI 0.72-4.29; p-value 0.006).ConclusionsDS was associated with several NPS, caregiver burden and, in men, faster cognitive decline progression.},
}

@article {pmid41342674,
year = {2025},
author = {Rabl, M and Zullo, L and Wehrli, J and Hössel, K and Lewczuk, P and Petkov Peyneshki, I and Seifritz, E and Klöppel, S and von Gunten, A and Popp, J},
title = {Omics-AD-A multimodal biomarker study on cognitive decline and neuropsychiatric symptoms: Design and cohort characteristics.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {},
number = {},
pages = {13872877251401159},
doi = {10.1177/13872877251401159},
pmid = {41342674},
issn = {1875-8908},
abstract = {BackgroundAlzheimer's disease (AD) clinically manifests in cognitive decline and frequent neuropsychiatric symptoms (NPS).ObjectiveThe Omics-AD study's scope is to perform an in-depth multi-modal and longitudinal characterization of people with early AD to a) better understand pathophysiological changes of AD and b) identify new biomarkers for AD and AD-related clinical manifestation and progression, with a focus on NPS.MethodsParticipants in this prospective study were recruited at four Swiss memory-clinics. Comprehensive cognitive and neuropsychiatric assessments were performed at baseline and follow-up. Paired blood and cerebrospinal fluid (CSF) samples along with structural MRI were obtained at baseline. Established CSF AD biomarkers were analyzed. Untargeted omics and targeted molecular analyses will be performed and integrated in multi-modal, multi-omics data analysis.ResultsWe included 456 participants (mean age 71.2 years, 55.1% female), of which 48.5% were cognitively unimpaired (with no cognitive complains, NC, or with subjective cognitive decline, SCD) and 51.5% cognitively impaired (mild cognitive impairment, MCI, or mild clinical AD dementia). Half of the participants presented with NPS as measured by the Neuropsychiatric Inventory Questionnaire (48.5%) or the Mild Behavioral Impairment Checklist (52.7%). The most common symptoms were irritability (18%) and depression (17%). In total, 41.0% (n = 155) of participants were amyloid positive (20.6% of CN, 21.7% of SCD, 55.4% of MCI, and 72.4% of clinical AD dementia).ConclusionsThis multi-centric well-characterized cohort allows for single- and multi-omics analyses to investigate in depth molecular and biological pathway alterations in AD and their relationships with clinical manifestation and progression, with a particular focus on NPS.},
}

@article {pmid41342670,
year = {2025},
author = {Akaida, S and Katayama, O and Yamaguchi, R and Yamagiwa, D and Tomida, K and Shimada, H},
title = {The impact of involvement in social activities on dementia onset: The role of willingness.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {},
number = {},
pages = {13872877251400782},
doi = {10.1177/13872877251400782},
pmid = {41342670},
issn = {1875-8908},
abstract = {BackgroundImplementing dementia prevention measures is a critical global health objective.ObjectiveThis study investigated the impact of social activity involvement and willingness on dementia onset in community-dwelling older adults, and potential differences across age and sex subgroups.MethodsLongitudinal analysis was conducted on 2247 community-dwelling older adults ≥65 years (mean age: 74) who participated in the National Center for Geriatric and Gerontology-Study of Geriatric Syndromes in Japan (2015-2016 baseline survey) and were followed up at onset of dementia, including Alzheimer's disease, over 60 months. The Lifestyle Activities Questionnaire was used to determine social activity involvement. Willingness to participate in social activities was determined by asking if participants were willing to engage in 12 specific activities. Participants were classified into three groups: low-involvement, high-involvement/low-willingness, and high-involvement/high-willingness. Statistical analysis was conducted using Cox proportional hazards analysis with dementia onset as the outcome variable, involvement and willingness groups as explanatory variables, and adjusted covariates. Subgroup analyses examined differences across age and sex groups.ResultsThe high-involvement/high-willingness group showed a significantly lower dementia incidence (p < 0.001) than the other groups. Cox proportional hazards analysis revealed that the high-involvement/high-willingness group had a significantly lower hazard ratio (HR: 0.73, 95% confidence interval: 0.54-0.97) for dementia onset than the low-involvement group. This result was maintained in men and the age > 75 group.ConclusionsHigher involvement and willingness to participate in social activities lowered dementia risk, while higher involvement but low-willingness showed no protective effect. This result was maintained in men and the age > 75 group.},
}

@article {pmid41342662,
year = {2025},
author = {Roque, NA and Hosgood, HD and Hall, CB},
title = {The importance of accurate indoor air quality assessment in the aging and dementia population.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {},
number = {},
pages = {13872877251396928},
doi = {10.1177/13872877251396928},
pmid = {41342662},
issn = {1875-8908},
abstract = {As most persons spend most of their time indoors (>80% on most days), indoor air quality is potentially an important health concern, particularly in elderly populations at high risk for dementia. It is important to note that until recently, little attention has been given to indoor air quality and related standards; most regulatory thresholds remain limited to outdoor environments. Recognizing that the environments in which we live and work shape health, it is time to place indoor air quality at the center of brain health policy and research agendas, and to take intentional steps towards comprehensive in-home assessment.},
}

@article {pmid41342653,
year = {2025},
author = {Dybing, KM and Gao, S and Saykin, AJ and Risacher, SL and , },
title = {No significant associations between history of head injury and Alzheimer's disease fluid biomarkers in older adults.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {},
number = {},
pages = {13872877251401201},
doi = {10.1177/13872877251401201},
pmid = {41342653},
issn = {1875-8908},
abstract = {BackgroundConcussions are gaining attention as a risk factor for Alzheimer's disease (AD). Previous reports suggest concussion, also called head injury (HI), may be associated with changes to AD biomarkers, including amyloid and tau. However, there has been little characterization of biofluid biomarkers in older adults with remote history of HI.ObjectiveWe investigated whether aging participants at risk for AD with self-reported HI history would demonstrate alterations to cerebrospinal fluid (CSF) and blood plasma biomarkers of AD.MethodsUsing two-way ANCOVAs and linear mixed effects models, we examined both baseline cross-sectional and longitudinal associations between HI history, cognition, and AD biofluid biomarkers in 100 participants with HI history compared to 2411 without HI history from the ADNI dataset.ResultsOn baseline analysis, participants with HI history had higher CSF Aβ42/40 ratios than non-HI participants. There were no other baseline differences in biomarkers between HI and non-HI participants, nor were there any main effects of HI upon longitudinal analysis. We observed consistent main effects of age and cognitive impairment that suggested a pattern of worsened AD biomarker signatures in impaired participants with increasing age.ConclusionsOur findings do not support an association between self-reported HI history and AD fluid biomarkers in older adults from the ADNI dataset. Further characterization of fluid biomarker trajectories both in the acute post-HI period and in participants with remote HI is needed to understand the temporal dynamics of fluid biomarkers after HI and the implications of HI for AD risk.},
}

@article {pmid41342652,
year = {2025},
author = {Talavera-Garza, L and Hirai, M and Hovey, JD},
title = {Social Engagement and Cognitive Function Among Older Mexican Heritage Latinos.},
journal = {Clinical gerontologist},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/07317115.2025.2596782},
pmid = {41342652},
issn = {1545-2301},
abstract = {OBJECTIVES: The current study examined the relationship between social engagement and cognitive function among older Mexican heritage Latinos in the U.S. Although social engagement has been identified as a factor that is protective against cognitive decline and dementia, its association with cognitive health in Mexican heritage Latinos is understudied.

METHODS: Data on cognitive health, social network characteristics, perceived social support, and social engagement were collected in a sample of older Mexican heritage Latinos in South Texas.

RESULTS: Social network characteristics, perceived social support, and social engagement were significantly correlated with cognitive health. A hierarchical multiple regression analysis was used to test the relative strength of these factors in predicting cognitive health, while controlling for relevant covariates. Social engagement was found to be a significant predictor of cognitive function, beyond the effects of perceived social support and social network characteristics.

CONCLUSIONS: Findings highlight social engagement as a modifiable behavioral factor that may support cognitive health in aging Mexican heritage Latinos.

CLINICAL IMPLICATIONS: The results suggest that screening for and enhancing social engagement may be a valuable clinical strategy for preserving cognitive function in older Latinos at risk of poor cognitive health outcomes.},
}

@article {pmid41342372,
year = {2025},
author = {Yuan, R and Liu, F and Yu, J},
title = {Gut microbiota-derived extracellular vesicles in Alzheimer's disease - Immunomodulatory mechanisms, biomarkers, and therapeutic opportunities: A review.},
journal = {Biomolecules & biomedicine},
volume = {},
number = {},
pages = {},
doi = {10.17305/bb.2025.13213},
pmid = {41342372},
issn = {2831-090X},
abstract = {Alzheimer's disease (AD) is a progressive neurodegenerative disorder that poses a growing global health challenge. Beyond traditional hallmarks such as amyloid-β (Aβ) deposition, tau hyperphosphorylation, and neuroinflammation, the gut-brain axis (GBA) has emerged as a significant modulator of AD pathogenesis. Among gut-derived mediators, microbiota-derived extracellular vesicles (mEVs) transport bioactive cargo across epithelial and vascular barriers, thereby linking intestinal dysbiosis to neurodegeneration. This narrative review synthesizes experimental, translational, and early clinical evidence regarding the immunomodulatory roles of gut mEVs in AD. We examine how mEVs may traverse compromised intestinal and blood-brain barriers, activate microglia and astrocytes, and influence Aβ and tau metabolism, thereby integrating peripheral and central immune interactions. Based on this evidence, we propose the "microbiota-EV-immune-neuro axis" as a conceptual framework that connects gut dysbiosis with AD-related neurodegeneration. The review also highlights emerging data on mEV signatures as minimally invasive biomarkers and explores their potential as therapeutic targets or delivery vectors. While current evidence is preliminary and methodologically heterogeneous, mEVs are increasingly recognized as both indicators and potential modulators of AD pathophysiology, emphasizing the need for standardized, longitudinal, and interventional studies.},
}

@article {pmid41342354,
year = {2025},
author = {Mieling, M and Wiskow, C and Bunzeck, N},
title = {Globus Pallidus Iron Relates to Cognitive Impairment in Alzheimer's Disease: Evidence From MRI-Based Meta-Analysis.},
journal = {Annals of the New York Academy of Sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/nyas.70078},
pmid = {41342354},
issn = {1749-6632},
support = {//University of Lübeck/ ; },
abstract = {Iron is essential for brain metabolism and cognitive functioning, but excessive levels during healthy and pathological aging can have detrimental effects. Although this notion was supported by several single studies, meta-analytic evidence in Alzheimer's disease (AD) is still scarce. Therefore, we performed a meta-analysis of 23 MRI experiments with, in total, 715 AD patients and 1130 healthy controls (HC). All studies employed iron sensitive markers in basal ganglia structures, thalamus, and hippocampus, together with the Mini-Mental-Status-Examination (MMSE) to quantify cognitive performance. In all regions of interest, significantly higher iron levels were present in people with AD compared to HC, with the most pronounced effects in the putamen followed by the caudate. Importantly, only globus pallidus iron levels were negatively correlated with MMSE performance in AD patients. Our results provide unique evidence that increases in iron levels, especially within basal ganglia structures, which provide a hub for cognitive information processing, are a characteristic hallmark of AD.},
}

@article {pmid41342337,
year = {2025},
author = {Bose, D and Mukherjee, A and Acharya, M and Choudhury, S and Ghosh, N},
title = {Artificial Intelligence for Detection of Parkinson's Disease From Speech Signals-A Comprehensive Review.},
journal = {BioFactors (Oxford, England)},
volume = {51},
number = {6},
pages = {e70065},
doi = {10.1002/biof.70065},
pmid = {41342337},
issn = {1872-8081},
mesh = {*Parkinson Disease/diagnosis/physiopathology ; Humans ; *Artificial Intelligence ; *Speech/physiology ; Electroencephalography ; Early Diagnosis ; Signal Processing, Computer-Assisted ; },
abstract = {After Alzheimer's disease, Parkinson's disease (PD) is the second most common neuropathological condition. It is a progressive degenerative disease at superannuation that affects the central nervous system (CNS) and slowly disable patients doing regular activities like walking, speaking, and writing. Early diagnosis of this disease helps to manage the patients and provide them therapy effectively. From the past few years, gait, electroencephalogram (EEG) signals and speech signals have been inspected to detect this disease at an early stage, out of which the most frequently considered one is speech signal, as it is reported by the researchers that 90% of the PD patients suffer from speech disorders. Also, speech signal analysis is a non-invasive and cost-effective method to detect PD at an early stage, and it helps to build telediagnosis models for prediction. Classical speech signal processing methodologies adopted in PD detection sometimes suffer from inadequate understanding of the effect of PD speech generation models and how that is reflected on speech signals captured from the PD patients. Artificial intelligence (AI) based methods attempts to learn those models from the given data in the best possible way to distinguish PD patients from the healthy controls. This paper's primary goal is to survey AI methodologies to detect PD using speech signals as reported in the publications between 2020 and 2024. As deep learning (DL) is a subset of machine learning (ML) and ML is a subset of AI, we consider 55 research publications related to ML and DL methods adopted for speech signal-based PD diagnosis. All the articles were published by IEEE and we have considered key words like "Machine learning approaches in Parkinson's disease detection from speech signals," "Application of Deep learning in Parkinson Disease detection from speech signals," "Artificial Intelligence in Parkinson's disease detection"; to find the articles reviewed in this study. This comprehensive review article reveals that both ML and DL algorithms have demonstrated encouraging outcomes, and the need of focused effort on more explainable AI based methods that can be clinically interpreted and hence potentially can be trusted for early diagnosis of Parkinson's disease.},
}

*Parkinson Disease/diagnosis/physiopathology
Humans
*Artificial Intelligence
*Speech/physiology
Electroencephalography
Early Diagnosis
Signal Processing, Computer-Assisted

@article {pmid41342327,
year = {2025},
author = {Su, H and Liu, Z and Wei, J and Liu, Y and Zhong, Y and Liu, X and Tan, C and Chen, L},
title = {PDK4 suppresses high glucose-induced microglial ferroptosis by restricting pro-ferroptotic PUFA biosynthesis.},
journal = {Neuroreport},
volume = {},
number = {},
pages = {},
doi = {10.1097/WNR.0000000000002234},
pmid = {41342327},
issn = {1473-558X},
abstract = {BACKGROUND: Diabetes significantly elevates the risk of neurodegenerative disorders, including Alzheimer's disease and Parkinson's disease, indicating shared pathophysiological mechanisms. While ferroptosis is increasingly implicated in neurodegeneration, microglia - highly vulnerable to ferroptosis - may mediate this link. However, it remains unknown whether high glucose (HG) directly induces microglial ferroptosis.

METHODS: Using HG-treated BV2 microglia, we integrated multiomics profiling (RNA-seq and targeted lipidomics), functional assays, and genetic manipulation of pyruvate dehydrogenase kinase 4 (PDK4) to investigate its role in HG-associated ferroptosis.

RESULTS: HG-induced microglial ferroptosis, characterized by iron overload, elevated malondialdehyde and mitochondrial reactive oxygen species, glutathione peroxidase 4 (GPX4) downregulation, and mitochondrial damage, including loss of membrane potential and ultrastructural disintegration. This was accompanied by upregulated PDK4 expression. PDK4 overexpression attenuated ferroptosis by preserving GPX4, reducing lipid peroxidation, and maintaining mitochondrial integrity; these protective effects were reversed by n-6 polyunsaturated fatty acid (PUFA) supplementation. Conversely, PDK4 knockdown exacerbated ferroptosis via amplified n-6 PUFA synthesis and oxidative stress. Mechanistically, PDK4 acts as a metabolic gatekeeper by restricting acetyl-CoA availability for the synthesis of pro-ferroptotic PUFAs, thereby curtailing iron-dependent lipid peroxidation.

CONCLUSION: PDK4 is a critical regulator of HG-induced microglial ferroptosis, thereby bridging hyperglycemia-induced metabolic dysfunction and neurodegeneration. Our findings nominate PDK4 as a promising therapeutic target for diabetes-linked neurodegenerative diseases.},
}

@article {pmid41342141,
year = {2025},
author = {Desai, R and John, A and Anderson, E and Stafford, J and Patel, AMR and Marchant, NL and Charlesworth, G and Zuber, V and Stott, J},
title = {Evidence for Causal Links Between Known Modifiable Risk Factors and Dementia: A Systematic Review of Mendelian Randomisation Studies.},
journal = {European journal of neurology},
volume = {32},
number = {12},
pages = {e70458},
doi = {10.1111/ene.70458},
pmid = {41342141},
issn = {1468-1331},
mesh = {Humans ; *Mendelian Randomization Analysis ; *Dementia/genetics/epidemiology ; Risk Factors ; Diabetes Mellitus, Type 2/epidemiology/genetics ; },
abstract = {BACKGROUND: We aimed to systematically review the evidence for associations between the known modifiable risk factors and dementia based on Mendelian randomisation (MR) studies.

METHOD: Five databases were searched from inception to April 2024 investigating the association between the 12 risk factors identified in the Lancet Commission and dementia. Evaluable analyses were categorized into one of four levels (robust, probable, suggestive, insufficient) based on estimate significance level and concordance of direction of effect between main and sensitivity analyses. Evidence from clinically diagnosed dementia outcomes was synthesized separately from proxy outcomes. A post hoc sensitivity analysis excluded estimates with concerns over construct validity.

RESULTS: A total of 47 studies were included, representing 240 MR associations (185 unique and evaluable). Over half (73.5%) of evaluable analyses were graded as providing insufficient evidence for a causal association. Among clinically diagnosed outcomes, the strongest evidence was for educational attainment (mainly probable evidence in a protective direction) and type 2 diabetes-related dysfunction (probable evidence in the risk direction). Smoking showed probable evidence of a protective association. Other risk factors, produced inconclusive or insufficient evidence. Proxy outcome analyses yielded weaker findings; in particular, the association between education and Alzheimer's disease reversed direction.

CONCLUSION: MR evidence for most Lancet Commission risk factors remains insufficient or inconclusive. The most consistent support for causal associations was observed for lower educational attainment and type 2 diabetes. Null findings should be interpreted cautiously given limitations in GWAS phenotyping, sample composition, and MR methodology.},
}

Humans
*Mendelian Randomization Analysis
*Dementia/genetics/epidemiology
Risk Factors
Diabetes Mellitus, Type 2/epidemiology/genetics

@article {pmid41342073,
year = {2025},
author = {Liu, Z and Meng, X and Lu, R and Meng, X and Li, S and Wang, Y and Liu, X and Liu, X and Liu, J},
title = {PBX1 Improves Cognition and Reduces Amyloid-β Pathology in APP/PS1 Mice by Transcriptionally Activating the CRTC2-CREB Pathway.},
journal = {Aging cell},
volume = {},
number = {},
pages = {e70311},
doi = {10.1111/acel.70311},
pmid = {41342073},
issn = {1474-9726},
support = {82073581//National Natural Science Foundation of China/ ; 82273673//National Natural Science Foundation of China/ ; 101832020CX277//Jilin University/ ; JJKH20250213BS//Jilin University/ ; 24GNYZ44//Jilin University/ ; MJR202510105//Medjaden Inc./ ; },
abstract = {Alzheimer's disease (AD) is characterized by progressive cognitive decline, amyloid β (Aβ) deposition, and synaptic dysfunction. However, the mechanisms underlying neurodegeneration remain poorly understood. In this study, we investigated the therapeutic potential of PBX1, a transcriptional regulator implicated in neurodevelopment and neuroprotection, against AD. PBX1 expression was significantly downregulated in postmortem hippocampal tissues from patients with AD and in the APP/PS1 mouse model. In vitro, PBX1a knockdown reduced neurite complexity and increased apoptosis. PBX1a overexpression reversed these effects and reduced soluble Aβ1-40 and Aβ1-42 levels. In vivo, hippocampal overexpression of PBX1a restored spatial learning and memory, reduced Aβ burden by 41%, and increased neurite length by 1.5-fold. These behavioral and structural improvements were accompanied by reduced levels of hyperphosphorylated Tau and toxic Aβ oligomers. Mechanistically, PBX1 directly activated the transcription of CRTC2-a coactivator of CREB, thereby increasing CRTC2 expression and its nuclear colocalization with phosphorylated CREB. Restoration of the PBX1-CRTC2-CREB axis enhanced neuronal survival and synaptic integrity. Notably, CRTC2 knockdown blocked PBX1-mediated reductions in Aβ deposition, apoptosis, and hyperphosphorylated Tau expression, confirming the role of the PBX1-CRTC2-CREB axis in conferring neuroprotection. Together, our findings indicate that PBX1 is a key modulator of neuronal resilience in AD and that it functions through transcriptional activation of the CRTC2/CREB pathway. By unraveling a mechanism that links transcriptional regulation to amyloid clearance and cognitive function, this study highlights PBX1 as a promising therapeutic target for AD.},
}

@article {pmid41342072,
year = {2025},
author = {Diniz, BS and Zhao, S and Drouard, G and Vuoksimaa, E and Ollikainen, M and Lenze, EJ and Xu, M and Fortinsky, RH and Kuchel, GA and Kaprio, J and Kuo, CL},
title = {Biological Aging Acceleration in Major Depressive Disorder: A Multi-Omics Analysis.},
journal = {Aging cell},
volume = {},
number = {},
pages = {e70310},
doi = {10.1111/acel.70310},
pmid = {41342072},
issn = {1474-9726},
support = {P30AG067988/AG/NIA NIH HHS/United States ; 100499//Academy of Finland/ ; 205585//Academy of Finland/ ; 118555//Academy of Finland/ ; 141054//Academy of Finland/ ; 264146//Academy of Finland/ ; 308248//Academy of Finland/ ; 307339//Academy of Finland/ ; 328685//Academy of Finland/ ; //Health Data Research UK/ ; //Office for National Statistics/ ; //UK Research and Innovation/ ; },
abstract = {Major depressive disorder (MDD) is linked to a higher risk of premature aging, but the mechanisms underlying this association remain unclear. Using data from two population cohorts (UK Biobank and Finnish Twin Cohort), we evaluate the relationship between systemic and organ-specific proteomic and epigenetic aging acceleration and MDD. A lifetime history of MDD was associated with accelerated proteomic aging at both systemic and organ-specific levels-including the brain-in both cohorts, with stronger associations than those observed with systemic epigenetic aging. Systemic and brain-specific proteomic aging acceleration were linked to higher risks of incident MDD and a greater risk of Alzheimer's disease, related dementia, and mortality among individuals with MDD in the UK Biobank. Evidence of depressive episode remission attenuated the association between MDD and systemic and brain-specific proteomic aging acceleration. Finally, Mendelian randomization analyses revealed a causal effect of MDD on systemic and brain-specific proteomic aging acceleration. Our results suggest a strong bidirectional association between MDD and biological aging acceleration. Biological aging acceleration, assessed by proteomic systemic and organ-specific clocks, can serve as a novel therapeutic target for treating MDD and for mitigating the long-term risks of adverse health outcomes associated with this condition.},
}

@article {pmid41342013,
year = {2025},
author = {Ortega, BA and Weisstaub, NV and Katche, C},
title = {Retrosplenial cortex 5-HT2A receptors critically contribute to recognition memory processing.},
journal = {Frontiers in cellular neuroscience},
volume = {19},
number = {},
pages = {1711777},
pmid = {41342013},
issn = {1662-5102},
abstract = {The anterior retrosplenial cortex (aRSC) functions as a hub that integrates multimodal sensory inputs into associative recognition memories. Although the aRSC receives dense serotonergic projections from the raphe nuclei, the role of serotonin in its function remains poorly understood. Among serotonergic receptors, 5-HT2A receptors (5-HT2ARs) are highly expressed in cortical regions, including the aRSC, and have been implicated in the modulation of cognitive processes. Based on our previous work demonstrating the involvement of the aRSC in recognition memory, here we investigated the contribution of 5-HT2ARs (memory) during different phases of the object recognition (OR) task in rats. We found that selective blockade of 5-HT2ARs in the aRSC differentially affected acquisition, consolidation, and retrieval. These findings identify 5-HT2ARs in the aRSC as critical modulators of recognition memory processing and suggest that their dysregulation could contribute to cognitive impairments observed in conditions such as Alzheimer's disease.},
}

@article {pmid41341968,
year = {2026},
author = {Hong, J and An, HK and Nam, H and Choi, J and Yu, SW},
title = {Presenilin 2 regulates corticosterone-induced autophagic death of adult hippocampal neural stem cells.},
journal = {Animal cells and systems},
volume = {30},
number = {1},
pages = {35-46},
pmid = {41341968},
issn = {1976-8354},
abstract = {Chronic psychological stress is a well-known risk factor for neurodegenerative diseases including Alzheimer disease (AD), yet the underlying mechanisms remain unclear. We previously showed that chronic stress impairs adult hippocampal neurogenesis by triggering autophagic cell death of adult hippocampal neural stem (HCN) cells. Impairment of adult hippocampal neurogenesis is widely observed in the brains of human AD patients and animal models. However, it remains unknown whether stress-induced death of HCN cells is related to the pathogenesis of AD. In this study, we investigated whether the stress hormone, corticosterone (CORT) induces HCN cell death through presenilin 2 (Psen2), a gene associated with familial AD. Using CRISPR/Cas9-based knockout models and in vitro CORT treatment, we found that Psen2 expression is upregulated by CORT and Psen2 deletion prevents CORT-induced death in HCN cells. However, the Psen2 N141I mutation, despite its pathogenicity in AD, did not exacerbate CORT-induced cell death in vitro and hippocampus-dependent behavioral deficits in vivo. These findings indicate that while Psen2 is essential for stress-induced death of HCN cells, the Psen2 N141I mutation alone may not be sufficient to link chronic stress to AD pathogenesis.},
}

@article {pmid41341742,
year = {2025},
author = {Ji, Q and Tang, E and Liao, Y and Yang, J and Wang, Y and Zhan, Y},
title = {Associations Between Nicotine Metabolites and Serum Neurofilament Light Chain Levels in the General Population.},
journal = {Chronic diseases and translational medicine},
volume = {11},
number = {4},
pages = {284-292},
pmid = {41341742},
issn = {2589-0514},
abstract = {BACKGROUND: Nicotine has been associated with cognitive functions such as memory and attention, with serum neurofilament light chain (sNfL) as a biomarker for neurological diseases such as Alzheimer's disease (AD) and multiple sclerosis (MS). However, the associations between nicotine and its metabolites and sNfL levels remain underexplored. This study aims to investigate the associations of serum and urine levels of cotinine and trans-3'-hydroxycotinine (hydroxycotinine) with sNfL levels in a broad population.

METHODS: Employing data from the National Health and Nutrition Examination Survey (NHANES) 2013-2014, this cross-sectional study applied multivariable linear regression models and restricted cubic splines to examine the links between cotinine, hydroxycotinine (both in serum and urine), and sNfL levels.

RESULTS: A total of 2052 participants were included in the serum analysis (mean age, 46.8 years; SD, 15.3; weighted 52.1% women) and 661 participants in the urine analysis (weighted 49.5% women). sNfL levels were positively associated with both serum and urine concentrations of cotinine and hydroxycotinine. Adjusted analyses revealed increases in sNfL levels in association with these substances, noting nonlinear associations for serum and urine cotinine and hydroxycotinine with sNfL levels.

CONCLUSION: These findings demonstrate robust positive associations between nicotine metabolites and sNfL levels and identify novel U-shaped associations at lower exposure levels. The results raise the hypothesis that very low nicotine metabolite levels may be associated with lower axonal injury markers, warranting further longitudinal and mechanistic studies to clarify causality.},
}

@article {pmid41341704,
year = {2025},
author = {Vishwasrao, P and Zengin, G and Sinan, KI and Minceva, M and Luca, SV},
title = {Supercritical CO2 extraction of hemp seeds: A multivariate perspective on the influence of processing parameters on oil composition, antioxidant activity, and enzyme inhibition.},
journal = {Food chemistry: X},
volume = {32},
number = {},
pages = {103296},
pmid = {41341704},
issn = {2590-1575},
abstract = {Hemp seeds are valued for their unique nutritional and health benefits. This study examined the impact of supercritical (sc)CO2 extraction conditions on hemp seed oil yield, composition, antioxidant activity, and enzyme inhibition using a multivariate approach. While pressure (300-500 bar) had minimal effects, temperature (40-60 °C) and ethanol addition (0.6-1.5 %) significantly influenced oil yield. The levels of fatty acids, tocopherols, carotenoids, chlorophylls, phenolics, and flavonoids varied independently of extraction pressure and temperature, but their extractability generally increased with ethanol concentration. The co-solvent addition also enhanced radical scavenging activity but diminished the metal-reducing and chelating properties. Hemp seed oils inhibited enzymes linked to chronic diseases like diabetes, skin disorders, and Alzheimer's. Multivariate analysis grouped samples by fatty acid profile, pigment content, and bioactivity. This work provides novel insights into how scCO2 conditions affect the chemical and biological properties of hemp seed oils.},
}

@article {pmid41341539,
year = {2025},
author = {Zhou, C and Peng, Y},
title = {Network Pharmacology and Molecular Docking Reveal Neuroprotective Potential of Ligusticum wallichii in Alzheimer's Disease Therapy.},
journal = {Neuropsychiatric disease and treatment},
volume = {21},
number = {},
pages = {2603-2622},
pmid = {41341539},
issn = {1176-6328},
abstract = {PURPOSE: In traditional Chinese medicine, Ligusticum wallichii is a prominent herb, acclaimed for its therapeutic roles, including anti-tumor, antioxidant, and anti-inflammatory benefits. Studies conducted recently suggest it may help reduce cognitive deficits linked to Alzheimer's disease. However, the precise neuroprotective pathways through which Ligusticum wallichii exerts its effects on Alzheimer's disease are not yet fully understood. Network pharmacology is utilized in this research to understand the mechanisms through which Ligusticum wallichii's active ingredient might protect against Alzheimer's disease.

METHODS: The TCMSP database was utilized to extract the bioactive compounds of Ligusticum wallichii, and their related molecular targets were identified. By querying the GeneCards and OMIM databases, targets associated with Alzheimer's disease were identified. Using Cytoscape 3.8.2, a regulatory network mapping the interactions between active compounds and their respective targets was constructed. A protein-protein interaction network was generated by analyzing the target genes influenced by Ligusticum wallichii in Alzheimer's disease using the String database. The DAVID database was utilized to perform functional enrichment analysis, encompassing Gene Ontology (GO) and KEGG pathway analyses, to identify possible biological pathways related to these targets. Following this, molecular docking studies were carried out to confirm the interaction strength of the active compounds to the pivotal targets. Finally, in vitro experimental validation was performed to corroborate the findings.

RESULTS: Seven bioactive compounds were identified from Ligusticum wallichii, interacting with 269 potential targets. Molecular docking revealed that Myricanone, Mandenol, and Sitosterol exhibited stable binding affinities with STAT3, HSP90AA1, and EGFR, with binding energies ranging from -4.04 to -5.87 kcal/mol. In vitro studies demonstrated that these compounds significantly downregulated the expression of STAT3, EGFR, and HSP90AA1 in Neuro 2A cells.

CONCLUSION: In conclusion, the results indicate that Ligusticum wallichii significantly downregulated STAT3, EGFR, and HSP90AA1 expression in Neuro 2A cells, providing mechanistic evidence that targeting these proteins may ameliorate neurodegenerative processes in Alzheimer's disease and highlighting Ligusticum wallichii's promising therapeutic potential.},
}

@article {pmid41341510,
year = {2025},
author = {Yang, J and Yang, F and Chen, G and Liu, M and Yuan, S and Zhang, TE},
title = {Receptor-mediated mitophagy: a new target of neurodegenerative diseases.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1665315},
pmid = {41341510},
issn = {1664-2295},
abstract = {Neurodegenerative diseases are a category of neurological conditions with high prevalence that pose major treatment challenges. Common pathologies involve protein accumulation and mitochondrial damage. Mitophagy maintains cellular homeostasis by removing defective mitochondria, which are associated with the pathogenesis of neurodegenerative diseases. Although the ubiquitin-dependent mitophagy mediated by the PINK1-Parkin pathway has been extensively studied, growing evidence indicates that receptor-mediated mitophagy plays a crucial compensatory role in neurons, particularly when the PINK1-Parkin pathway is impaired. This review focuses on the emerging field of receptor-mediated mitophagy, systematically elaborating its role as a key homeostatic mechanism operating independently of the canonical PINK1/Parkin pathway. It provides a focused analysis of the specific functions and activation mechanisms of key receptors-including BNIP3, NIX, FUNDC1, and AMBRA1-in models of Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Furthermore, this review explores the clinical potential of targeting these specific receptors for precise intervention, aiming to provide a new theoretical foundation and direction for developing therapeutic strategies against neurodegenerative diseases.},
}

@article {pmid41341423,
year = {2025},
author = {Varghese, A and Fajardo, AR and Gowda Parameshwara, P and Thankappan, M and Kannan, B},
title = {Improving Alzheimer Disease Diagnosis via a Custom Convolutional Neural Network and Pretrained Models: Development and Evaluation of a Diagnostic Tool (NeuroFusionNet).},
journal = {JMIR neurotechnology},
volume = {4},
number = {},
pages = {e68839},
pmid = {41341423},
issn = {2817-092X},
abstract = {BACKGROUND: Alzheimer disease (AD) is a progressive neurodegenerative disorder that impairs cognitive function, memory, and behavior. Early and accurate diagnosis is essential for effective management; however, traditional cognitive tests often lack the sensitivity and specificity required for early detection.

OBJECTIVE: This study aims to develop and evaluate NeuroFusionNet-a diagnostic tool that integrates a custom convolutional neural network (CNN) with a pretrained VGG16 model-to improve the accuracy and reliability of AD diagnosis from neuroimaging data across multiple cognitive classes.

METHODS: A comprehensive preprocessing pipeline, including brain region segmentation, was implemented to isolate regions of interest and reduce noise. NeuroFusionNet extracts multilevel features by combining a custom CNN with VGG16, while Local Interpretable Model-Agnostic Explanations enhances interpretability. Data were obtained from the Alzheimer's Disease Neuroimaging Initiative database, comprising 600 test samples (120 per class for AD, cognitively normal, early mild cognitive impairment, late mild cognitive impairment, and mild cognitive impairment). Given the multiclass nature of the study, odds ratios were not applied. Statistical significance was assessed using the McNemar test for paired predictions.

RESULTS: NeuroFusionNet achieved an overall accuracy of 0.81 (95% CI 0.779-0.841; P<.001). Per-class performance metrics were as follows: AD: precision 0.90 (95% CI 0.85-0.95), recall 0.78 (95% CI 0.72-0.84), F 1-score 0.84; cognitively normal: precision 0.67 (95% CI 0.60-0.74), recall 0.97 (95% CI 0.94-1.00), F 1-score 0.79; early mild cognitive impairment: precision 0.90 (95% CI 0.84-0.96), recall 0.82 (95% CI 0.76-0.88), F 1-score 0.86; late mild cognitive impairment: precision 0.95 (95% CI 0.90-1.00), recall 0.87 (95% CI 0.81-0.93), F 1-score 0.90; and mild cognitive impairment: precision 0.71 (95% CI 0.64-0.78), recall 0.61 (95% CI 0.53-0.69), F 1-score 0.65. Training and validation curves over 50 epochs indicated robust learning with minimal overfitting.

CONCLUSIONS: NeuroFusionNet demonstrated robust performance in a multiclass diagnostic setting, achieving high accuracy and balanced per-class performance. The combination of a custom CNN and fine-tuned VGG16, along with the interpretability provided by Local Interpretable Model-Agnostic Explanations, yields a reliable tool for early AD detection with significant potential to enhance clinical decision-making. Further validation on larger datasets is warranted.},
}

@article {pmid41341281,
year = {2025},
author = {Li, C and Sheng, Z and Cohen, T and Pakhomov, S},
title = {"Is There Anything Else?": Examining Administrator Influence on Linguistic Features from the Cookie Theft Picture Description Cognitive Test.},
journal = {... Workshop on Cognitive Modeling and Computational Linguistics. Workshop on Cognitive Modeling and Computational Linguistics},
volume = {2025},
number = {},
pages = {91-103},
pmid = {41341281},
abstract = {Alzheimer's Disease (AD) dementia is a progressive neurodegenerative disease that negatively impacts patients' cognitive ability. Previous studies have demonstrated that changes in naturalistic language samples can be useful for early screening of AD dementia. However, the nature of language deficits often requires test administrators to use various speech elicitation techniques during spontaneous language assessments to obtain enough propositional utterances from dementia patients. This could lead to the "observer's effect" on the downstream analysis that has not been fully investigated. Our study seeks to quantify the influence of test administrators on linguistic features in dementia assessment with two English corpora the "Cookie Theft" picture description datasets collected at different locations and test administrators show different levels of administrator involvement. Our results show that the level of test administrator involvement significantly impacts observed linguistic features in patient speech. These results suggest that many of significant linguistic features in the downstream classification task may be partially attributable to differences in the test administration practices rather than solely to participants' cognitive status. The variations in test administrator behavior can lead to systematic biases in linguistic data, potentially confounding research outcomes and clinical assessments. Our study suggests that there is a need for a more standardized test administration protocol in the development of responsible clinical speech analytics frameworks.},
}

@article {pmid41341242,
year = {2024},
author = {Gutman, B and Shmilovitch, AH and Aran, D and Shelly, S},
title = {Twenty-Five Years of AI in Neurology: The Journey of Predictive Medicine and Biological Breakthroughs.},
journal = {JMIR neurotechnology},
volume = {3},
number = {},
pages = {e59556},
pmid = {41341242},
issn = {2817-092X},
abstract = {Neurological disorders are the leading cause of physical and cognitive disability across the globe, currently affecting up to 15% of the world population, with the burden of chronic neurodegenerative diseases having doubled over the last 2 decades. Two decades ago, neurologists relying solely on clinical signs and basic imaging faced challenges in diagnosis and treatment. Today, the integration of artificial intelligence (AI) and bioinformatic methods is changing this landscape. This paper explores this transformative journey, emphasizing the critical role of AI in neurology, aiming to integrate a multitude of methods and thereby enhance the field of neurology. Over the past 25 years, integrating biomedical data science into medicine, particularly neurology, has fundamentally transformed how we understand, diagnose, and treat neurological diseases. Advances in genomics sequencing, the introduction of new imaging methods, the discovery of novel molecular biomarkers for nervous system function, a comprehensive understanding of immunology and neuroimmunology shaping disease subtypes, and the advent of advanced electrophysiological recording methods, alongside the digitalization of medical records and the rise of AI, all led to an unparalleled surge in data within neurology. In addition, telemedicine and web-based interactive health platforms, accelerated by the COVID-19 pandemic, have become integral to neurology practice. The real-world impact of these advancements is evident, with AI-driven analysis of imaging and genetic data leading to earlier and more accurate diagnoses of conditions such as multiple sclerosis, Parkinson disease, amyotrophic lateral sclerosis, Alzheimer disease, and more. Neuroinformatics is the key component connecting all these advances. By harnessing the power of IT and computational methods to efficiently organize, analyze, and interpret vast datasets, we can extract meaningful insights from complex neurological data, contributing to a deeper understanding of the intricate workings of the brain. In this paper, we describe the large-scale datasets that have emerged in neurology over the last 25 years and showcase the major advancements made by integrating these datasets with advanced neuroinformatic approaches for the diagnosis and treatment of neurological disorders. We further discuss challenges in integrating AI into neurology, including ethical considerations in data use, the need for further personalization of treatment, and embracing new emerging technologies like quantum computing. These developments are shaping a future where neurological care is more precise, accessible, and tailored to individual patient needs. We believe further advancements in AI will bridge traditional medical disciplines and cutting-edge technology, navigating the complexities of neurological data and steering medicine toward a future of more precise, accessible, and patient-centric health care.},
}

@article {pmid41341187,
year = {2025},
author = {Wang, Z and Xia, K and Huang, X and Fan, D and Yang, Q},
title = {Amyloid marker levels and the risk of developing cerebral small vessel disease: A Mendelian randomization study.},
journal = {Journal of Alzheimer's disease reports},
volume = {9},
number = {},
pages = {25424823251403565},
pmid = {41341187},
issn = {2542-4823},
abstract = {BACKGROUND: Previous observational studies have suggested a potential association between amyloid marker levels and the risk of developing cerebral small vessel disease (CSVD), but this relationship remains incompletely understood.

OBJECTIVE: This study was conducted to assess the impact of amyloid marker levels on the risk of developing CSVD via Mendelian randomization (MR) design.

METHODS: Using the latest genome-wide association study summary statistics for 5 plasma amyloid markers and 4 CSVD traits, a two-sample MR study was conducted to assess the genetic relationship between amyloid marker levels and CSVD risk. Furthermore, reverse MR analysis was utilized to establish the causal relationship between CSVD traits and the levels of the identified plasma amyloid markers to explore potential bidirectional causality.

RESULTS: After FDR correction, greater amyloid-β (Aβ) 42 levels were associated with an increased risk of developing lobar cerebral microbleeds (CMBs) (odds ratio = 2.311, 95% confidence interval 1.403-3.809, p = 0.001, p FDR = 0.040). Potential positive correlations were detected between Aβ40 levels and the risk of developing intracerebral hemorrhage, between Aβ42 levels and the risk of developing all CMBs, and between serum amyloid P component levels and white matter fractional anisotropy status. In reverse MR analysis, no effect of CSVD traits on amyloid marker levels was detected.

CONCLUSIONS: Our study suggests potential causal relationships between amyloid marker levels and different CSVD traits. Our results contribute to a greater understanding of the pathophysiology of CSVD, particularly in relation to amyloid deposition.},
}

@article {pmid41341151,
year = {2025},
author = {Bouter, Y and Glasnek, RM and Wenzel, JM and Bouter, C},
title = {[18]F-FDG-PET and Multimodal Biomarker Integration: A Powerful Tool for Alzheimer's Disease Diagnosis.},
journal = {Nuclear medicine and molecular imaging},
volume = {59},
number = {6},
pages = {453-471},
pmid = {41341151},
issn = {1869-3474},
abstract = {UNLABELLED: An early, biomarker-based diagnosis of Alzheimer's Disease (AD) is crucial, especially with the emerging availability of novel therapeutic options. However, the role of [18]F-FDG-PET and its relationship to other PET and CSF biomarkers remains unclear. Therefore, the aim of this study was the evaluation of the role of [18]F-FDG-PET in AD diagnosis and its relationship to other commonly used fluid and PET biomarkers and their individual and multimodal accuracy in AD diagnosis. We included n = 157 AD patients, n = 603 MCI patients, and n = 380 cognitively normal participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) that underwent PET imaging with [18]F-FDG or [18]F-Florbetapir. Clinical and imaging data including patient characteristics, CSF biomarkers, cognition tests, [18]F-FDG-PET, [18]F-Florbetapir-PET, and [18]F-Flortaucipir-PET were retrospectively analyzed. PET images were quantified in several brain regions. The uptake of [18]F-FDG was inversely correlated with [18]F-Florbetapir and positively correlated with CSF Aβ42 in several brain regions commonly affected by AD. Additionally, [18]F-FDG uptake showed an inverse correlation with both forms of CSF tau, t-tau and p-tau, in various brain regions, but did not correlate with [18]F-Flortaucipir uptake. Moreover, regional [18]F-FDG uptake was positively correlated with cognitive function. Diagnostic accuracies were similarly high for [18]F-FDG uptake in the PCC/Precuneus region, [18]F-Florbetapir uptake, CSF Aβ42, CSF p-tau, and [18]F-Flortaucipir uptake in differentiating AD from cognitively normal individuals. [18]F-FDG-PET and its combination with CSF p-tau/ Aβ42 ratio showed the highest predictive power for disease severity. The study underscores the potential of integrating [18]F-FDG-PET with CSF biomarkers to enhance the diagnosis, prognosis, and monitoring of AD, highlighting the complexity and regional specificity of biomarker interactions in neurodegeneration.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13139-025-00932-2.},
}

@article {pmid41341139,
year = {2025},
author = {Wang, L and Sun, Q},
title = {Construction and verification of prediction model for Alzheimer's disease in diabetic patients.},
journal = {Frontiers in endocrinology},
volume = {16},
number = {},
pages = {1699771},
pmid = {41341139},
issn = {1664-2392},
mesh = {Humans ; *Alzheimer Disease/epidemiology/diagnosis/etiology ; Female ; Male ; Aged ; *Nomograms ; Middle Aged ; Risk Factors ; *Diabetes Mellitus, Type 2/complications ; ROC Curve ; Blood Glucose ; Prognosis ; },
abstract = {OBJECTIVE: This study aimed to construct a prediction model for Alzheimer's disease (AD) in diabetic patients and evaluate its clinical application value.

METHODS: A total of 322 patients was included and randomly divided into a training set (n=225) and a validation set (n=97) at a ratio of 7:3. Clinical characteristic data of the patients were collected. In the training set, univariate analysis and multivariate logistic regression analysis were used to identify the relevant risk factors for AD onset, and a nomogram prediction model was constructed accordingly. The receiver operating characteristic (ROC) curve and calibration curve were plotted and validated in an independent validation dataset. In addition, decision curve analysis (DCA) was used to further evaluate the application value and significance of the nomogram model in clinical practice.

RESULTS: The incidence of AD in the training set was 18.67% (42/225), and that in the validation set was 18.56% (18/97). Multivariate regression analysis showed that age, duration of diabetes, fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c), triglyceride (TG), and homeostasis model assessment of insulin resistance (HOMA-IR) were all independent risk factors for AD onset (all P < 0.05). In the training set and validation set, the nomogram prediction model showed good predictive performance, with the concordance index (C-index) reaching 0.868 and 0.710 respectively. Calibration curve analysis showed a high degree of agreement between the predicted values and the observed values. The mean absolute errors in the training set and validation set were 0.103 and 0.116 respectively. The results of the Hosmer-Lemeshow test were χ² = 10.515, P = 0.230 and χ² = 5.987, P = 0.648 respectively. The ROC curve showed that the AUCs of the nomogram model for predicting occurrence of AD in the training set and validation set were 0.866 (95% CI: 0.794- 0.939) and 0.718 (95% CI: 0.517-0.920) respectively.

CONCLUSION: The prediction model for AD in diabetic patients can assist in the early prediction of the risk of AD onset, laying a solid foundation for formulating effective clinical intervention strategies. This is crucial for delaying the progression of AD and significantly improving the quality of life of patients.},
}

Humans
*Alzheimer Disease/epidemiology/diagnosis/etiology
Female
Male
Aged
*Nomograms
Middle Aged
Risk Factors
*Diabetes Mellitus, Type 2/complications
ROC Curve
Blood Glucose
Prognosis

@article {pmid41341074,
year = {2026},
author = {Chapalain, X and Huet, O and Rowan, KM and Mouncey, PR and Langeron, O and Menon, DK and Harrison, DA},
title = {Hospital mortality, withdrawal of life-sustaining therapy decisions and early secondary brain insults for critically ill traumatic brain injury patients in England, Wales and Northern Ireland (2009-2024): an observational cohort study.},
journal = {The Lancet regional health. Europe},
volume = {61},
number = {},
pages = {101538},
pmid = {41341074},
issn = {2666-7762},
abstract = {BACKGROUND: Recent epidemiological studies reported conflicting results regarding mortality trends for traumatic brain injury (TBI) patients. Mortality trends for the critically ill TBI population, and their drivers of changes, remains understudied. Particularly, withdrawal of life-sustaining therapy (WLST) decisions were rarely evaluated concurrently. In this study, we aimed to describe hospital mortality and WLST trends over the past 15 years in England, Wales and Northern Ireland for TBI patients admitted to an intensive care unit (ICU).

METHODS: Observational cohort study, involving 235 adult ICUs participating in the Intensive Care National Audit & Research Centre (ICNARC) Case Mix Programme (CMP). From April 1, 2009 to March 31, 2024, all TBI patients were included. Comparator cohorts consisted of patients with trauma, sepsis, and vascular brain injury recorded in the CMP. The primary outcome was hospital mortality. The secondary outcome was the incidence of WLST decisions. We also examined the proportion of patients experiencing predefined early secondary brain insults.

FINDINGS: Of the 2,324,961 ICU admissions, we identified 45,684 unique TBI patients. Over the study period, hospital mortality for TBI patients increased from 25.6% (1021/3988) to 35.0% (1306/3727). The proportion of WLST decisions rose from 7.5% (301/4024) to 19.7% (759/3850). After adjustment for main confounders, multivariable analyses confirmed these trends. No similar trends were observed among the comparator cohorts. TBI patients were exposed to hypotension, hypocapnia, hypercapnia and hyperglycaemia in 49.8% (22,559/45,298), 29.9% (12,356/41,262), 33.6% (13,869/41,262) and 29.2% (12,127/41,505) of cases, respectively. Half of patients (50.3%, 20,747/41,265) were exposed to hypoxaemia, and this proportion increased markedly from 36.9% (1359/3684) to 61.2% (2186/3572) over time.

INTERPRETATION: For critically ill TBI patients, hospital mortality and WLST decisions rates increased over time. These findings raise important questions regarding the processes and ethical frameworks underpinning WLST decisions.

FUNDING: UKRI, NIHR, UK Ministry of Defence, Alzheimer's Research UK, French Society of Anaesthesiology and Critical Care, Gueules Cassées Foundation, INNOVEO donation fund.},
}

@article {pmid41340885,
year = {2026},
author = {Zhang, J and Shi, Y},
title = {Surface-based Multi-Axis Longitudinal Disentanglement Using Contrastive Learning for Alzheimer's Disease.},
journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
volume = {15974},
number = {},
pages = {585-594},
pmid = {41340885},
abstract = {Accurate modeling of disease progression is essential for comprehending the heterogeneous neuropathologies such as Alzheimer's Disease (AD). Traditional neuroimaging analysis often confound disease effects with normal aging, complicating the differential diagnosis. Recent advancements in deep learning have catalyzed the development of disentanglement techniques in Autoencoder networks, aiming to segregate longitudinal changes attributable to aging from those due to disease-specific alterations within the latent space. However, existing longitudinal disentanglement methods usually model disease as a single axis factor which ignores the complexity and heterogeneity of Alzheimer's Disease. In response to this issue, we propose a novel Surface-based Multi-axis Disentanglement framework.This framework posits multiple disease axes within the latent space, enhancing the model's capacity to encapsulate the multifaceted nature of AD, which includes various disease trajectories. To assign axes to data trajectories without explicit ground truth labels, we implement a longitudinal contrastive loss leveraging self-supervision, thereby refining the separation of disease trajectories. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (N = 1321), our model demonstrates superior performance in delineating between cognitively normal (CN), mild cognitive impairment (MCI), and AD subjects,classification of stable MCI vs converting MCI and Amyloid status, compared to the single-axis model. This is further substantiated through an ablation study on the contrastive loss, underscoring the utility of our multi-axis approach in capturing the complex progression patterns of AD. The code is available at: https://github.com/jianweizhang17/MultiAxisDisentanglement.git.},
}

@article {pmid41340156,
year = {2025},
author = {Fischer, L and Parker, D and Maboudian, S and Fonseca, C and Tato-Fernández, C and Annen, L and Arunachalam, P and Bacci, JR and Barboure, M and Capelli, S and Karagianni, S and Collij, LE and Edison, P and Fox, NC and Franzmeier, N and Grothe, MJ and Jagust, WJ and Maass, A and Malpetti, M and Paterson, RW and Sogorb-Esteve, A and Schöll, M},
title = {Longitudinal biomarker studies in human neuroimaging: capturing biological change of Alzheimer's pathology.},
journal = {Alzheimer's research & therapy},
volume = {},
number = {},
pages = {},
doi = {10.1186/s13195-025-01920-6},
pmid = {41340156},
issn = {1758-9193},
}

@article {pmid41340152,
year = {2025},
author = {Cardona, CL and Wei, L and Kim, J and Angeles, E and Singh, G and Chen, S and Patel, R and Ifediora, N and Canoll, P and Teich, AF and Hargus, G and Chavez, A and Sproul, AA},
title = {High throughput identification of genetic regulators of microglial inflammatory processes in Alzheimer's disease.},
journal = {Journal of neuroinflammation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12974-025-03562-9},
pmid = {41340152},
issn = {1742-2094},
support = {R01AG073360/AG/NIA NIH HHS/United States ; R01AG073360/AG/NIA NIH HHS/United States ; P30AG066462-02/AG/NIA NIH HHS/United States ; P30AG066462-02/AG/NIA NIH HHS/United States ; P30AG066462-02/AG/NIA NIH HHS/United States ; DP2NS131566-02/NH/NIH HHS/United States ; TAME-AD//Thompson Family Foundation/ ; },
}

@article {pmid41339994,
year = {2025},
author = {Sivalingam, AM and Sureshkumar, DD},
title = {The Central Role of m6A as Epigenetic Regulator in Metabolic Disorders of Therapeutic Potential and Clinical Implications.},
journal = {Molecular neurobiology},
volume = {63},
number = {1},
pages = {247},
pmid = {41339994},
issn = {1559-1182},
mesh = {Humans ; *Epigenesis, Genetic ; *Metabolic Diseases/genetics/therapy/metabolism ; *Adenosine/analogs & derivatives/metabolism/genetics ; Animals ; Aging/genetics ; },
abstract = {N6-methyladenosine (m6A) is the most common reversible mRNA modification, regulating fundamental cellular processes. It plays a vital role in aging and age-related diseases by influencing gene expression, RNA splicing, and stability. Growing evidence suggests that m6A modifications orchestrate key hallmarks of aging, including cellular senescence, stem cell exhaustion, and chronic inflammation factors that contribute to neurodegeneration, cardiovascular disease, and cancer. The intricate crosstalk between m6A and chromatin modifications is now recognized as a fundamental mechanism shaping age-associated epigenetic landscapes and influencing disease susceptibility. Core m6A regulators, such as METTL3, FTO, and ALKBH5, are implicated in age-related metabolic decline, neurodegeneration, and impaired tissue regeneration, making them promising therapeutic targets. Dysregulated m6A patterns are linked to aberrant RNA metabolism, protein aggregation, and synaptic dysfunction in Alzheimer's and Parkinson's diseases, while in cardiovascular and metabolic disorders, m6A modifications contribute to endothelial dysfunction, inflammation, and oxidative stress. Recent breakthroughs in computational modeling and RNA-editing technologies have revolutionized m6A research. High-precision deep-learning models (e.g., m6A-DCR) and CRISPR-based m6A editing tools provide powerful platforms to decode m6A's role in aging and disease progression. These advances pave the way for novel therapeutic strategies, offering opportunities for early diagnostics, precision medicine, and personalized interventions. Despite these promising developments, challenges remain in translating m6A-targeted therapies into clinical applications. Future research must enhance treatment specificity, minimize off-target effects, and elucidate the broader implications of m6A in aging. Advancing our understanding of m6A's functional landscape is essential for developing next-generation RNA-based therapeutics to combat aging and its associated diseases.},
}

Humans
*Epigenesis, Genetic
*Metabolic Diseases/genetics/therapy/metabolism
*Adenosine/analogs & derivatives/metabolism/genetics
Animals
Aging/genetics

@article {pmid41339948,
year = {2025},
author = {Honey, MIJ and van Maurik, IS and van Harten, AC and Gouda, M and van Leeuwenstijn, M and Mank, A and Trieu, C and Bouteloup, V and Chêne, G and Pellegrin, I and Dufouil, C and Doecke, JD and Fowler, CJ and Masters, CL and Pijnenburg, Y and Wilson, D and van der Flier, WM and Teunissen, CE and Verberk, IMW},
title = {Individualized prediction of clinical progression to dementia using plasma biomarkers in non-demented elderly.},
journal = {Alzheimer's research & therapy},
volume = {},
number = {},
pages = {},
doi = {10.1186/s13195-025-01925-1},
pmid = {41339948},
issn = {1758-9193},
support = {WE.09-20921-04//Alzheimer Nederland/ ; },
abstract = {BACKGROUND: We aimed to develop individualized predictions for risk of developing any-cause dementia and Alzheimer's disease (AD) dementia, in individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI), using plasma phosphorylated-tau-181 (pTau181), phosphorylated-tau-217 (pTau217; in a subset), amyloid beta1-42/1-40 (Aβ42/40), glial fibrillary acidic protein (GFAP) and/or neurofilament light (NfL).

METHODS: From the Amsterdam Dementia Cohort we included 314 individuals with SCD (age 61 ± 9 years, n = 184 (59%) male, MMSE 29 ± 1) and 253 individuals with MCI (age 65 ± 7 years, n = 165 (65%) male, MMSE 27 ± 2), who had annual follow-up (median duration 2.4 years). Cox proportional hazards regression models were used to calculate probabilities for progression to dementia and were externally validated in MEMENTO and AIBL cohorts.

RESULTS: During follow-up 20 SCD and 99 MCI patients developed dementia. For MCI patients who progressed to any form of dementia, plasma GFAP contributed on top of age, sex, and MMSE score in the parsimonious individualized prognostic model (C-index = 0.69 [95%CI = 0.63; 0.76]). With AD-dementia as the outcome, GFAP and pTau181 were selected in the parsimonious model on top of the demographic variables (C-index = 0.71 [95%CI = 0.65; 0.76]). In the subset of 197 MCI individuals with pTau217 measurements, pTau217 was selected in the parsimonious model on top of the demographic variables (C-index = 0.75 [95%CI = 0.69; 0.79]). External validation demonstrated that the models are robust in a memory clinic setting.

CONCLUSIONS: Our prediction models have utility for clinical practice to calculate progression probabilities for development of dementia in individual patients living with MCI over a 1-, 3- and 5-year time period.},
}

@article {pmid41339920,
year = {2025},
author = {Richardson, TE and Kandoi, S and Almeida, FC and Rohde, SK and Marx, GA and Canbeldek, L and Hiya, S and Maldonado-Díaz, C and Samanamud, J and Clare, K and Slocum, CC and Kulumani Mahadevan, LS and Chiu, LY and Farrell, K and Crary, JF and Daoud, EV and White, CL and Espinoza, SE and Gonzales, MM and Oliveira, TG and Walker, JM},
title = {Longitudinal assessment of cognitive decline and resilience in high-level Alzheimer disease neuropathologic change.},
journal = {Alzheimer's research & therapy},
volume = {17},
number = {1},
pages = {257},
pmid = {41339920},
issn = {1758-9193},
}

@article {pmid41339846,
year = {2025},
author = {Moriyoshi, H and Akagi, A and Riku, Y and Sone, J and Miyahara, H and Yoshida, M and Katsuno, M and Iwasaki, Y},
title = {Contribution of comorbid pathologies to amyotrophic lateral sclerosis with cognitive or behavioral abnormalities.},
journal = {BMC neurology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12883-025-04556-z},
pmid = {41339846},
issn = {1471-2377},
abstract = {BACKGROUND: Amyotrophic lateral sclerosis (ALS) often presents with cognitive or behavioral abnormalities. The cortical involvement of TAR DNA-binding protein-43 (TDP-43) pathology is considered a major cause of these abnormalities. However, the contribution of underlying comorbid pathologies remains unclear.

METHODS: We investigated the clinicopathological characteristics of 29 autopsy cases of ALS with cognitive or behavioral abnormalities and evaluated the association between clinical symptoms and comorbid pathologies such as Alzheimer's disease (AD), argyrophilic grain disease (AGD), dementia with Lewy bodies (DLB), and primary age-related tauopathy (PART), as well as the presence of cortical TDP-43 pathology.

RESULTS: Of the 29 patients, 17 exhibited comorbid pathologies (AD, AGD, or PART), which may contribute to cognitive or behavioral abnormalities. None of the cases met the pathological criteria for DLB. The group with comorbid pathologies was significantly older, but clinical symptoms did not differ between the groups. Behavioral abnormalities and memory impairment were frequently observed in both groups. All six subjects without cortical TDP-43 pathology had comorbid pathologies, which had a notable effect on cognitive or behavioral abnormalities. Hippocampal sclerosis and memory impairment were observed in ALS cases without comorbid pathologies.

CONCLUSION: A high frequency of comorbid pathologies is observed in elderly patients with ALS presenting with cognitive or behavioral abnormalities. There are cases of ALS in which comorbid pathologies such as AD, AGD, and PART may contribute to cognitive or behavioral abnormalities, even in the absence of cortical TDP-43 pathology. Hippocampal sclerosis of ALS may contribute to memory impairment independently of comorbid pathologies.},
}

@article {pmid41339629,
year = {2025},
author = {Yu, C and Wang, W and Shi, L and Ma, R and Zhang, Y and Zhang, C and Wang, X and Wang, T and Zheng, Y and Tian, J},
title = {27-Hydroxycholesterol triggers microglial senescence subsequent to iron over-loading contributes to brain aging, suppressed by Deferoxamine.},
journal = {npj aging},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41514-025-00303-3},
pmid = {41339629},
issn = {2731-6068},
support = {QMSI2024B-06//Item of scientific research fund for young doctor of Qiqihar Academy of Medical Sciences/ ; 2024-KYYWF-0350//Basic scientific research business expenses of provincial undergraduate universities in Heilongjiang Province in 2024/ ; LSFGG-2025123//The Qiqihar City Science and Technology Bureau project of China/ ; 2024AAC03254//Natural Science Foundation of Ningxia/ ; 2020BEG03048//Ningxia key research and development project/ ; },
abstract = {Brain aging is a major factor in cognitive decline and Alzheimer's disease (AD) progression. Aging-induced microglial senescence critically drives inflammaging and brain aging processes. Nevertheless, the underlying reasons and mechanisms that promote microglial aging remain unclear. This study explores how 27-hydroxycholesterol (27-OHC), a key oxysterol, accelerates brain aging by promoting microglial senescence, iron overload, and neuroinflammation. Clinically, we observed a significant inverse correlation between plasma 27-OHC levels and Mini-Mental State Examination (MMSE) scores in AD patients, accompanied by reduced 24S-OHC concentrations. Experimental studies revealed that 27-OHC administration in mice induced hippocampal-dependent cognitive impairment and anxiety-like behaviors, concurrent with elevated expression of cellular senescence markers (P21, P16, SA-β-Gal) and M1 microglial polarization. In BV-2 cells, 27-OHC disrupted iron homeostasis (DMT1/ferritin/GPX4 dysregulation), elevating ROS and impairing mitochondrial function. Deferoxamine (DFX) mitigated microglial senescence and ferroptosis. These findings establish the 27-OHC-iron axis as a novel therapeutic target for combating cholesterol-driven neurodegeneration.},
}

@article {pmid41339508,
year = {2025},
author = {Abbott, A},
title = {The 'silent' brain cells that shape our behaviour, memory and health.},
journal = {Nature},
volume = {648},
number = {8092},
pages = {23-25},
pmid = {41339508},
issn = {1476-4687},
}

@article {pmid41339497,
year = {2025},
author = {Babu, T and Mahendran, R and Ajitha, P and Rajendran, S},
title = {Intra-scale interaction and cross-scale fusion network for detecting the progression of neurodegeneration in Alzheimer's disease.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-31179-8},
pmid = {41339497},
issn = {2045-2322},
}

@article {pmid41339486,
year = {2025},
author = {Badina, AM and Tahiri, H and Ouarour, A and Ceyzériat, K and Millet, P and Tournier, BB and Chakir, I},
title = {Glial cells are involved in day-night protein dysregulation in the hippocampus of a mouse model of Alzheimer's disease.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-30965-8},
pmid = {41339486},
issn = {2045-2322},
support = {310030_212322//Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung/ ; 310030_212322//Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung/ ; },
abstract = {The hippocampus regulates memory and cognition, both of which are influenced by circadian rhythms. These rhythms also drive time-dependent gene expression in neurons and glial cells, affecting hippocampal function. In Alzheimer's disease (AD), alterations in these rhythms, contributing to cognitive decline, is little understood. This study examined hippocampal proteomes from 7-month-old wild-type (WT) and 3xTgAD mice at two time points (Zeitgeber 2, ZT2 and ZT14) to detect early pathological changes. In WT mice, 199 proteins (8%) showed diurnal variation, particularly those linked to energy metabolism and neuronal/glial functions. In 3xTgAD mice, this rhythmic variation dropped by half (3.6%), with only five proteins shared across genotypes. Moreover, significant differences in protein expression emerged between WT and 3xTgAD at both time points, notably involving mitochondrial function, oxidative phosphorylation, and ATP production. Functional clustering revealed disrupted bioenergetic pathways, especially affecting complex I of the electron transport chain and astrocytic metabolism. These early alterations suggest a breakdown in daily control of hippocampal energy regulation in AD. The results highlight the critical role of sampling time in research and suggest that circadian disruption in astrocytic and neuronal metabolism may play a central role in AD progression and could inform future chronotherapeutic approaches.},
}

@article {pmid41339459,
year = {2025},
author = {Ms, R and Horng, MF and S, SS and G, N},
title = {Fusion-ADiNet: a multi-level framework for enhanced diabetes and Alzheimer's disease detection using chimp-whale fusion estimation.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-29904-4},
pmid = {41339459},
issn = {2045-2322},
abstract = {Rising cases of diabetes and AD are the two biggest health-related issues world-wide; this greatly hampers the quality of life among the afflicted persons. Early identification of diabetes most especially correlated with neurodegenerative conditions such as Alzheimer's guarantees early intervention and hence proper management. However, the existing approaches for disease detection have been suffering from a series of limitations in poor diagnostic accuracy, high computational complexity, and the lack of an effective model that could properly handle the intricate correlations between diabetes and AD. Most of the existing methods for diabetes and AD detection rely on traditional machine-learning algorithms or heuristic optimization approaches, which are not capable of handling high dimensionality and complex clinical data. The models also find it extremely difficult to represent the subtle relationship between the two diseases, leading to unsatisfying performance in practical applications. It is therefore imminent to develop much more advanced methodologies with integration that could improve accuracy in prediction, enabling better decision-making in clinics. To overcome the limitations of existing methods, in this paper, we propose a new approach called Fusion-Alzheimer's Diabetes Network (Fusion-ADiNet) by introducing a multi-level fusion framework for disease detection. The main novelty in this method lies in the newly designed Chimp-Whale Fusion Estimator (CWFE) optimization algorithm. Furthermore, the Fusion-ADiNet framework is quite flexible, so extending it to other diseases or datasets in the future will be very easy. This work contributes much to the field of healthcare analytics and opens new perspectives toward more effective diagnostic tools for timely detection of diabetes and Alzheimer's disease.},
}

@article {pmid41339324,
year = {2025},
author = {Pachicano, M and Mehta, S and Hurtado, A and Ard, T and Stanis, J and Breningstall, B and Bienkowski, MS},
title = {Laminar organization of pyramidal neuron cell types defines distinct CA1 hippocampal subregions.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {10604},
pmid = {41339324},
issn = {2041-1723},
support = {K01GR1057173//U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ ; R36AG087310//U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ ; P30-AG066530-03S1//U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ ; S10OD032285//U.S. Department of Health & Human Services | NIH | NIH Office of the Director (OD)/ ; OIA-2121164//National Science Foundation (NSF)/ ; },
mesh = {Animals ; *Pyramidal Cells/metabolism/cytology ; *CA1 Region, Hippocampal/cytology/metabolism ; Mice ; Male ; Mice, Inbred C57BL ; Female ; Gene Expression Profiling ; },
abstract = {Investigating the cell type organization of hippocampal CA1 is essential for understanding its role in memory and cognition and its susceptibility to neurological disorders like Alzheimer's disease and epilepsy. Multiple studies have identified different organizational principles for gene expression and how it reflects cell types within the CA1 pyramidal layer including gradients or mosaic. Here, we identify sublaminar gene expression patterns within the mouse CA1 pyramidal layer that span across the entire hippocampal axis. Our findings reveal that CA1 subregions (CA1d, CA1i, CA1v, CA1vv) contain differentially distributed layers of constituent cell types and can be identified by regional gene expression signatures. This work offers a new perspective on the organization of CA1 cell types that can be used to further explore hippocampal cell types across species.},
}

Animals
*Pyramidal Cells/metabolism/cytology
*CA1 Region, Hippocampal/cytology/metabolism
Mice
Male
Mice, Inbred C57BL
Female
Gene Expression Profiling

@article {pmid41339080,
year = {2025},
author = {Chani, AR and Khan, AU and Khan, A and Minhas, AM},
title = {Repurposing of Cefotaxime for Its Therapeutic Potential in Alzheimer's Disease: An Explanation of the Possible Mechanism of Action.},
journal = {ACS chemical neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1021/acschemneuro.5c00714},
pmid = {41339080},
issn = {1948-7193},
abstract = {Alzheimer's disease (AD) is the leading cause of dementia, characterized by progressive oxidative stress, neuroinflammation, and cognitive decline. Current pharmacological therapies are largely symptomatic, underscoring the need for new disease-modifying strategies. Drug repurposing provides an efficient approach to exploiting clinically approved compounds with established safety. Hence, in the current study, we investigated the neuroprotective potential of cefotaxime (CTX), a third-generation cephalosporin antibiotic, in an intracerebroventricular streptozotocin (ICV-STZ) rat model of AD. Adult male Sprague-Dawley rats were administered CTX (100-300 mg/kg, intraperitoneal, 28 days) and compared with donepezil (5 mg/kg). Behavioral performance was assessed using the Morris water maze, Y-maze, elevated plus maze, and open field tests. Biochemical assays (oxidative stress markers), histopathology, immunohistochemistry, ELISA, and RT-PCR were employed to examine molecular and cellular changes. CTX significantly ameliorated STZ-induced cognitive deficit, anxiety-like behaviors, oxidative stress, and neuroinflammation. While CTX reduced mRNA expression of β-amyloid and tau, it did not lower their protein levels as determined by ELISA, suggesting selective modulation at transcriptional rather than post-translational levels. Together, these findings suggest a potential role for CTX as a promising repurposed candidate for alleviating AD-related neurobehavioral deficits through the modulation of oxidative stress and inflammatory pathways.},
}

@article {pmid41338979,
year = {2025},
author = {Nakamura, K and Kanou, M and Ito, S and Jimbo, T and Kouzaki, K and Nakazato, K and Nakamjima, R and Yamanouchi, K and Ueda, H and Yamana, K},
title = {Beta-nicotinamide mononucleotide attenuates creatine kinase release in Duchenne muscular dystrophy model rats.},
journal = {The Journal of veterinary medical science},
volume = {},
number = {},
pages = {},
doi = {10.1292/jvms.25-0258},
pmid = {41338979},
issn = {1347-7439},
abstract = {Beta-nicotinamide mononucleotide (beta-NMN) is a direct precursor of nicotinamide adenine dinucleotide (NAD[+]), a coenzyme essential for maintaining homeostasis in living organisms. NMN administration has attracted attention as a potential treatment for aging and age-related conditions, including diabetes, Alzheimer's disease, and chronic kidney disease. Duchenne muscular dystrophy (DMD) is a progressive, degenerative muscle disease caused by X-linked frameshift mutations in the Dmd gene. NAD[+] levels in skeletal muscle decline in DMD pathology. In this study, we explored the therapeutic potential of NMN as an NAD[+] booster for muscular dystrophy by administering NMN to DMD rats, which exhibit severe phenotypes comparable to those of human DMD patients, for 2 months. Although NMN administration did not improve muscle function in DMD rats, it did reduce the release of creatine kinase in their blood. RNA-seq analysis revealed that NMN administration could reverse DMD-related gene expression changes associated with skeletal muscle homeostasis. These results suggest that NMN can protect skeletal muscle against degeneration in DMD and may hold therapeutic potential for DMD patients.},
}

@article {pmid41338951,
year = {2025},
author = {Gu, F and Zhu, J and Zhu, P and Zou, Z},
title = {Effects of plant-derived dietary antioxidants on Alzheimer's disease: Focus on ferroptosis.},
journal = {Asia Pacific journal of clinical nutrition},
volume = {34},
number = {6},
pages = {922-940},
doi = {10.6133/apjcn.202512_34(6).0007},
pmid = {41338951},
issn = {1440-6047},
support = {2023XY026//Zhejiang Province Medical and Health Scientific and Technological Project/ ; 2022J019//Ningbo Natural Science Foundation Project/ ; 2023S137//Ningbo Public Welfare Science and Technology Plan Project/ ; 2025JK272//Zhejiang Province Scientific and Technological Project for Disease prevention and Control/ ; },
mesh = {*Alzheimer Disease/drug therapy ; *Ferroptosis/drug effects ; Humans ; *Antioxidants/pharmacology ; Animals ; *Diet ; },
abstract = {BACKGROUND AND OBJECTIVES: Alzheimer's disease (AD) is the most prevalent form of dementia in older individuals. Ferroptosis, a programmed cell death characterized by iron-dependent membrane lipid peroxida-tion is implicated in AD pathology. Increasing evidences have shown that plant-derived dietary antioxidants exhibit their anti-ferroptosis activity. However, the anti-AD mechanism of plant-derived dietary antioxidants remains elusive. Therefore, this review aims to explore the anti-AD effects of plant-derived dietary antioxidants via ferroptosis regulation.

METHODS AND STUDY DESIGN: This review examines the available published data from all peer-reviewed original research articles on following topics: ferroptosis mechanisms, the role of ferroptosis in AD, the preclinical or clinical studies of plant-derived dietary antioxidants in cell, animal models of AD or patients with AD.

RESULTS: Ferroptosis is involved in AD pathology. Importantly, we clarify why docosahexaenoic acid (DHA)-rich brain phospholipids are extremely susceptible to lipid peroxidation. In addition, plant-derived dietary antioxidants such as vitamin E (VE), resveratrol, epigallocatechin-3-gallate (EGCG), curcumin, quercetin, baicalein and alpha-lipoic acid (ALA) show the anti-AD effects in preclinical AD models and prevent decline of cognition in healthy elderly population. Clinical studies show that ALA prevents decline of cognition of AD patients although most plant-derived dietary antioxidants exhibit con-flicting results.

CONCLUSIONS: It suggests that a plant-based diet may lead to potential health benefits in preventing cognitive decline in healthy elderly population. In regard to ALA, further clinical studies are highly recommended to evaluate its therapeutic potential that could optimize its dietary intake for preventing and alleviating decline of cognition of patients with AD.},
}

*Alzheimer Disease/drug therapy
*Ferroptosis/drug effects
Humans
*Antioxidants/pharmacology
Animals
*Diet

@article {pmid41338440,
year = {2025},
author = {Zarei, O and Talebi Moghadam, M and Vastegani, SM},
title = {Machine Learning and Deep Learning in Clinical Practice: Advancing Neurodegenerative Disease Diagnosis with Multimodal Markers.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111667},
doi = {10.1016/j.brainresbull.2025.111667},
pmid = {41338440},
issn = {1873-2747},
abstract = {Neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and multiple sclerosis, present major global health challenges due to their progressive and incurable nature. Early and accurate diagnosis is critical to slow disease progression and optimize therapeutic interventions, yet conventional diagnostic approaches-such as neuroimaging, cerebrospinal fluid biomarker analysis, and clinical evaluation-are often inadequate at the prodromal stage. Recent advances in artificial intelligence, particularly machine learning (ML), have provided new opportunities for precision diagnosis and treatment in neurology, using large data and multimodal biomarkers. Applications of ML to data from neuroimaging, electrophysiology, behavioral functions, speech and handwriting analysis, and molecular biomarkers have shown promising improvements in diagnostic accuracy, patient classification, and therapeutic recommendations. However, significant challenges remain, including data heterogeneity, model interpretability, population diversity, and ethical concerns surrounding patients' privacy. The purpose of this review is to examine current applications of ML in the diagnosis and management of neurodegenerative diseases through various data, highlight its strengths and limitations, and discuss future directions for using these approaches in clinical practice. We also outline emerging directions, including multimodal fusion with longitudinal data, federated and privacy-preserving learning, and the potential of explainable AI (XAI) and large language models (LLMs) in clinical decision support.},
}

@article {pmid41338363,
year = {2025},
author = {Reyes, H and Mondragon, J and Frank, C and Murphy, C},
title = {Examining functional connectivity in metabolic syndrome and small vessel disease: A novel approach to understanding olfactory dysfunction and Alzheimer's disease.},
journal = {Brain research},
volume = {},
number = {},
pages = {150084},
doi = {10.1016/j.brainres.2025.150084},
pmid = {41338363},
issn = {1872-6240},
abstract = {INTRODUCTION: The prevalence of Alzheimer's Disease (AD) is projected to triple by 2050, highlighting the need to identify early markers and underlying mechanisms associated with its progression. Olfactory dysfunction has emerged as an early indicator of AD, with its neural basis linked to changes in the medial temporal lobe and associated networks. Cardiovascular risk factors, including metabolic syndrome (MetS) and cerebral small vessel disease (SVD), have been implicated in neurodegenerative processes, yet their impact on olfactory network connectivity remains underexplored. This study aimed to investigate task-based functional connectivity in the olfactory network, medial temporal lobe, and default mode network among cognitively unimpaired individuals with MetS or SVD.

METHOD: Participants were grouped based on MetS and SVD status. Using fMRI, functional connectivity patterns during olfactory processing were analyzed for group differences.

RESULTS: Functional connectivity revealed hypoconnectivity between the right angular gyrus and secondary olfactory cortex between MetS groups and altered connectivity between the hippocampus and frontoparietal control network between SVD groups. Both conditions demonstrated increased within-network cerebellar connectivity compared to controls.

DISCUSSION: These findings highlight distinct neural alterations in olfactory and cognitive control networks associated with cardiovascular risk factors, providing novel insights into early brain changes linked to cardiovascular risk factors for dementia.},
}

@article {pmid41338343,
year = {2025},
author = {Wang, T and Feng, L and Tian, Z},
title = {The therapeutic potential of early exercise in Alzheimer's disease: Focus on the brain-spleen axis.},
journal = {Ageing research reviews},
volume = {114},
number = {},
pages = {102980},
doi = {10.1016/j.arr.2025.102980},
pmid = {41338343},
issn = {1872-9649},
abstract = {Alzheimer's disease (AD) is the predominant cause of cognitive dysfunction, with global prevalence increasing annually. AD progression is principally driven by the accumulation of amyloid-β (Aβ) and hyperphosphorylated microtubule-associated protein tau (p-Tau), which trigger a subsequent cascade of neuroinflammatory responses within the central nervous system (CNS). This pathological cascade is regulated by reciprocal CNS-peripheral immune crosstalk. The brain-spleen axis has emerged as a critical conduit that orchestrates splenic immune activity and CNS-peripheral immune crosstalk during AD progression. Notably, through the brain-spleen axis, early-life and preclinical exercise may restore splenic vagal-sympathetic homeostasis, re-establish immune equilibrium, and then mitigate neuroinflammation. This review advances a testable hypothesis that early exercise prevents or attenuates AD pathology through the brain-spleen axis, potentially accelerating the development of innovative therapeutic targets such as non-invasive brain stimulation.},
}

@article {pmid41338191,
year = {2025},
author = {Xie, M and Eyting, M and Bommer, C and Ahmed, H and Geldsetzer, P},
title = {The effect of shingles vaccination at different stages of the dementia disease course.},
journal = {Cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cell.2025.11.007},
pmid = {41338191},
issn = {1097-4172},
abstract = {Using natural experiments, we have previously reported that live-attenuated herpes zoster (HZ) vaccination appears to have prevented or delayed dementia diagnoses in both Wales and Australia. Here, we find that HZ vaccination also reduces mild cognitive impairment diagnoses and, among patients living with dementia, deaths due to dementia. Exploratory analyses suggest that the effects are not driven by a specific dementia type. Our approach takes advantage of the fact that individuals who had their eightieth birthday just after the start date of the HZ vaccination program in Wales were eligible for the vaccine for 1 year, whereas those who had their eightieth birthday just before were ineligible and remained ineligible for life. The key strength of our natural experiments is that these comparison groups should be similar in all characteristics except for a minute difference in age. Our findings suggest that live-attenuated HZ vaccination prevents or delays mild cognitive impairment and dementia and slows the disease course among those already living with dementia.},
}

@article {pmid41338186,
year = {2025},
author = {Ralhan, I and Do, AD and Bae, JY and Feringa, FM and Cai, W and Chang, J and Chik, K and Lee, NYJ and Gerry, CJ and van der Kant, R and Jackson, J and Ricq, EL and Ioannou, MS},
title = {Protective ApoE variants support neuronal function by effluxing oxidized phospholipids.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.10.040},
pmid = {41338186},
issn = {1097-4199},
abstract = {Apolipoprotein E (ApoE) mediates the bidirectional transport of lipids between cells. In the brain, this includes the transfer of lipids from neurons to glia. ApoE4, a major risk factor for Alzheimer's disease, impairs this transport pathway, increasing risk for neurodegeneration. ApoE2 and ApoE3 Christchurch (ApoE3Ch) confer resistance to disease, yet little is known regarding how these variants affect lipid trafficking. Here, we explored how lipoprotein particles containing different ApoE isoforms affect neuronal health. We demonstrate that ApoE2 and ApoE3Ch particles protect neurons from ferroptosis by extracting oxidized unsaturated lipids through the ABCA7 transporter. ApoE4 particles, on the other hand, exacerbate the effects of these toxic lipids, leading to endolysosomal dysfunction. By reducing the oxidized lipid burden in ApoE4 neurons, ApoE2 and ApoE3Ch particles rescue endolysosomal function and restore defects in neuronal activity caused by excitotoxicity. Our findings reveal how ApoE2 and ApoE3Ch help protect neurons from neurodegeneration.},
}

@article {pmid41338180,
year = {2025},
author = {Weng, Y and Huang, Y and Zhang, J and Wu, Y and He, Q and Litchev, S and Wu, A and Ling, Z and Zhao, L and Liao, R and Shao, L and Wang, M and Xu, Y and Gong, R and Zhang, Z and Wang, Y and Wu, TT and Lu, S and Kong, Q and Lv, H},
title = {Immunogenicity of a recombinant subunit COVID-19 vaccine: A prospective, multi-centre, longitudinal study in Chinese Alzheimer's disease patients.},
journal = {Human vaccines & immunotherapeutics},
volume = {21},
number = {1},
pages = {2591504},
doi = {10.1080/21645515.2025.2591504},
pmid = {41338180},
issn = {2164-554X},
mesh = {Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; *Alzheimer Disease/immunology ; Antibodies, Neutralizing/blood/immunology ; Antibodies, Viral/blood/immunology ; China ; *COVID-19/prevention & control/immunology ; *COVID-19 Vaccines/immunology/administration & dosage ; Cytokines/blood ; *Immunogenicity, Vaccine ; Immunoglobulin G/blood ; Longitudinal Studies ; Prospective Studies ; SARS-CoV-2/immunology ; Vaccines, Subunit/immunology/administration & dosage ; Vaccines, Synthetic/immunology/administration & dosage ; East Asian People ; },
abstract = {Vaccination is an essential strategy against COVID-19 in the current era of emerging variants. This study evaluates the immunogenicity of the recombinant subunit COVID-19 vaccine (Zifivax) in Alzheimer's disease (AD) patients. A total of 249 patients with Alzheimer's disease (AD) were enrolled in an eight-month, prospective study conducted across three medical centers in Hangzhou, Zhejiang Province, from May 2022 to January 2023, Zhe Jiang province, and were categorized into unvaccinated (AD-UV) and vaccinated groups (AD-V). Levels of RBD-IgG, neutralization antibody activity, and cytokines were identified to evaluate the immune responses. Clinical outcomes were assessed within one month following breakthrough infection (BTI) with the Omicron variant. Following three doses, the vaccine induced a robust immune response, elevating neutralizing antibodies and activating T-cells in AD-V cohort. AD-V patients exhibited significantly higher humoral immune responses compared to AD-UV counterparts. The anti-RBD antibodies level and pseudoviral neutralizing activity demonstrated an increase concurrent with the onset of immunity and infection, and the seroresponse rate exhibited a similar trend in AD-V cohort. RBD-IgG antibody levels against WT, DELTA, and BA.5 variants in AD-V cohort showed significantly higher compared to AD-UV cohort. Following Omicron infection, unvaccinated patients experienced higher levels of Th1/Th2-type cytokines than vaccinated individuals. Vaccination correlated with reduced severe illness, increased survival rates and extended survival times after Omicron BTI. The findings highlight the immunogenicity and suggest a certain degree of protective effectiveness of the recombinant subunit COVID-19 vaccine (Zifivax) in AD patients.},
}

Aged
Aged, 80 and over
Female
Humans
Male
Middle Aged
*Alzheimer Disease/immunology
Antibodies, Neutralizing/blood/immunology
Antibodies, Viral/blood/immunology
China
*COVID-19/prevention & control/immunology
*COVID-19 Vaccines/immunology/administration & dosage
Cytokines/blood
*Immunogenicity, Vaccine
Immunoglobulin G/blood
Longitudinal Studies
Prospective Studies
SARS-CoV-2/immunology
Vaccines, Subunit/immunology/administration & dosage
Vaccines, Synthetic/immunology/administration & dosage
East Asian People

@article {pmid41338169,
year = {2025},
author = {Li, S and Li, X and Li, S and Chen, D and Xia, C},
title = {Discovery of novel hybrids of coumarin and quinoline as potential anti-Alzheimer's disease agent.},
journal = {Bioorganic & medicinal chemistry},
volume = {133},
number = {},
pages = {118499},
doi = {10.1016/j.bmc.2025.118499},
pmid = {41338169},
issn = {1464-3391},
abstract = {The multifaceted nature of Alzheimer's disease (AD) spurred growing interest in developing multi-target-directed ligands (MTDLs) for its prevention and treatment. Coumarin and quinoline scaffolds, recognized for their broad spectrum of AD-related biological activities including amyloid-β (Aβ) aggregation regulation, cholinesterase (ChE) inhibition, β-secretase 1 (BACE1) inhibition and neuroprotection, were identified as potential building blocks. Here in this study, 24 novel coumarin-quinoline hybrid compounds were rationally designed and synthesized. Inhibition studies targeting Aβ, ChE and BACE1 identified compound B8 as a promising lead compound. B8 exhibited effective binding to Aβ, and significantly attenuated Aβ-induced SH-SY5Y cell death by lowering oxidative stress and decreasing cellular apoptosis. Crucially, B8 demonstrated excellent blood-brain barrier (BBB) permeability, and intragastric administration of B8 to 7-month-old APP/PS1 transgenic mice resulted in improved cognitive function. This improvement was supported by the protection of hippocampal and cortical neurons from necrosis, attenuation of oxidative stress and inflammation in these brain regions, as well as a reduction in Aβ deposition. These findings highlight the potential of coumarin-quinoline hybrids as a novel class of AD therapeutics, with B8 emerging as a promising lead candidate warranting further investigation.},
}

@article {pmid41338134,
year = {2025},
author = {Makhaeva, GF and Grishchenko, MV and Rudakova, EV and Kovaleva, NV and Boltneva, NP and Skornyakova, TS and Khudina, OG and Shchegolkov, EV and Zhilina, EF and Astakhova, TY and Pronkin, PG and Timokhina, EN and Lapshina, MA and Dubrovskaya, ES and Radchenko, EV and Palyulin, VA and Burgart, YV and Saloutin, VI and Charushin, VN and Richardson, RJ},
title = {Synthesis, biological evaluation, and in silico studies of pyridoxal-amiridine hybrids as multitargeting anti-Alzheimer's disease agents.},
journal = {European journal of medicinal chemistry},
volume = {303},
number = {},
pages = {118397},
doi = {10.1016/j.ejmech.2025.118397},
pmid = {41338134},
issn = {1768-3254},
abstract = {New conjugates of an anticholinesterase drug, amiridine, linked to vitamin B6 derivatives pyridoximines 3 and pyridoxamines 4 with different lengths of alkylene spacers, were synthesized and assessed as potential multifunctional anti-Alzheimer's disease (anti-AD) agents. All conjugates demonstrated high acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibition (IC50: AChE, 0.386-2.53 μM; BChE, 0.031-1.45 μM), but poor activity against off-target carboxylesterase. Conjugates were mixed-type reversible inhibitors of AChE and BChE and displaced propidium from the AChE peripheral anionic site at the level of donepezil. All conjugates inhibited Aβ42 self-aggregation in the thioflavin test, wherein conjugates 3 were more active; inhibition increased with spacer elongation, being greatest for (CH2)8. Results agreed with molecular docking to AChE, BChE and Aβ42. Conjugates exhibited high ABTS[•+]-scavenging activity comparable to Trolox and the starting pyridoxal. Moreover, compounds 4 were three times more active than their imine analogues 3, which agreed with quantum chemical analysis. Using the example of imine 3c, the possibility of conjugates of this type to bind biogenic metal ions was shown by UV-Vis spectroscopy. Pyridoxamines 4a,b with spacers n = 4,6 were less toxic in general than imines 3a,b toward HEK293T, HepG2, and SHY5Y cell lines. Additionally, conjugates demonstrated neuroprotection in models of hydrogen peroxide and glutamate-induced oxidative stress in neuroblastoma SH-SY5Y cells, where compounds 4a,b were more active than 3a,b. Altogether, the results indicated that the new conjugates possessed potential for further development as multifunctional anti-AD drug candidates.},
}

@article {pmid41338085,
year = {2025},
author = {Foukarakis, I and Androni, X and Sampatakakis, SN and Mamalaki, E and Ntanasi, E and Yannakoulia, M and Chatzipanagiotou, S and Papandreou, C and Kyrozis, A and Tsapanou, A and Scarmeas, N},
title = {"Objective sleep apnea severity, cognitive impairment and AD pathology: Insights from the ALBION cohort".},
journal = {Sleep medicine},
volume = {138},
number = {},
pages = {108691},
doi = {10.1016/j.sleep.2025.108691},
pmid = {41338085},
issn = {1878-5506},
abstract = {OBJECTIVES: Obstructive sleep apnea (OSA) appears to be closely related to cognitive status. This study explored the association between the Apnea-Hypopnea Index (AHI), cognitive status, and cerebrospinal fluid (CSF) biomarker pTau181/Aβ42 (AD pathology) in non-demented individuals.

METHODS: In this cross-sectional study, 115 non-demented participants from the ALBION cohort (mean age 64.4 ± 8.9 years, 70 % female, median education 14 years, median BMI 25.7 kg/m[2]) underwent a one-night WatchPAT evaluation to determine AHI (blood oxygen desaturation ≥3 %). Participants were categorized based on both cognitive status (patients with Mild Cognitive Impairment (MCI)[n = 27] or cognitively normal individuals (CN) [n = 88]) and AD pathology (AD+ [n = 25] or AD- [n = 90]). Binary logistic regression analysis, adjusted for age, sex, years of education, and BMI was used to assess the association of cognitive status and AD pathology with AHI. Participants were further divided into low (<15/h) and high (≥15/h) AHI levels and a joint analysis with AD pathology was performed with cognitive status as the outcome.

RESULTS: After adjusting for confounders, for each unit increase in AHI, the odds of being classified as MCI were 7 % higher (OR = 1.07, p = 0.003) and the odds of being classified as AD+ were 4 % higher (OR = 1.04, p = 0.043). Compared to the reference group [AD (-)/AHI(low)], the odds ratio of being classified as MCI was 4.48 (p = 0.022) in the AD (-)/AHI(high) and 15.30 (p = 0.0017) in the AD (+)/AHI(high) group.

CONCLUSIONS: We find that higher AHI levels may contribute to cognitive impairment, either independently or alongside AD pathology. Further longitudinal studies are warranted to clarify causality and potential therapeutic benefits of OSA management on cognitive health.},
}

@article {pmid41338025,
year = {2025},
author = {Wagdy, M and Ibrahim, AA and Yahia, AM and Maher, RM and Abo-Elwafa, AH and Salah, A and Heikal, YM},
title = {Neurobiotech innovative strategies targeting Alzheimer's disease through therapeutic micro and macroalgae potentials.},
journal = {Journal of neuroimmunology},
volume = {411},
number = {},
pages = {578821},
doi = {10.1016/j.jneuroim.2025.578821},
pmid = {41338025},
issn = {1872-8421},
abstract = {Alzheimer's disease (AD) is a progressive neurodegenerative disorder identified by cognitive decline, memory loss, and behavioral changes, affecting approximately 50 million people worldwide. Genetic predisposition, environmental variables, and aging all play a role in the development of AD. Current therapeutic approaches primarily focus on alleviating symptoms through drugs such as donepezil and memantine. However, these treatments offer limited efficacy and may be accompanied by adverse effects. In contrast, natural therapies derived from algae present a promising alternative. Microalgae, including Chlorella and Spirulina, and macroalgae such as Fucus vesiculosus, Ecklonia cava, Sargassum, Laminaria japonica, and Fucus species, are rich in bioactive molecules having antioxidant and anti-inflammatory characteristics. These substances demonstrated potential in addressing the pathological features of AD, such as oxidative stress and neuroinflammation. Furthermore, advances in biotechnological tools like CRISPR-Cas9 gene editing are poised to enhance the efficacy of these natural therapies by targeting and modifying disease-associated genes. This review aims to bridge the fields of neurobiotechnology and marine bioresources by examining the synergistic potential of algal compounds and gene-editing strategies in combating Alzheimer's disease. Algal-derived compounds are utilized in pharmaceuticals, nutraceuticals, and dietary supplements, and may offer neuroprotective benefits that could aid in the prevention or treatment of AD.By integrating insights from molecular biology, pharmacology, and genomics, we seek to illuminate a novel, multidisciplinary framework for future therapeutic innovation.},
}

@article {pmid41337883,
year = {2025},
author = {Cho, S and El Gaamouch, F and Upadhyay, S and Nada, H and Kuncewicz, K and Gabr, MT},
title = {As48, a first-in-class dual-function TREM2 modulator: Receptor activation and shedding inhibition.},
journal = {Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie},
volume = {193},
number = {},
pages = {118862},
doi = {10.1016/j.biopha.2025.118862},
pmid = {41337883},
issn = {1950-6007},
abstract = {Triggering receptor expressed on myeloid cells 2 (TREM2) dysfunction contributes to Alzheimer's disease pathogenesis, yet current therapeutics cannot prevent ADAM-mediated receptor shedding that diminishes signaling efficacy. Using Affinity Selection-Mass Spectrometry (AS-MS) screening, we identified As48, a novel small molecule that binds TREM2 with high affinity. Biophysical validation confirmed 7-fold selectivity over TREM1. Cellular assays demonstrated that As48 functions as a TREM2 agonist, activating SYK phosphorylation and enhancing microglial phagocytosis. Molecular docking and molecular dynamics simulations revealed that As48 binds near the cleavage region, establishing hydrogen bonds with Gly68 and reducing conformational flexibility in regions 58-102. Based on this structural insight, we investigated the effect of As48 on TREM2 ectodomain shedding and discovered inhibition of receptor shedding without affecting ADAM10/17 protease activities, representing the first small molecule with anti-shedding properties through conformational restriction of protease accessibility. Importantly, As48 displayed favorable pharmacokinetics with potential for brain permeability, supporting its translational relevance. Through its dual and simultaneous promotion of receptor activation and prevention of shedding, As48 represents a paradigm shift in TREM2 modulation and neuroinflammatory drug discovery.},
}

@article {pmid41341243,
year = {2024},
author = {Kokorelias, KM and McMurray, J and Chu, C and Astell, A and Grigorovich, A and Kontos, P and Babineau, J and Bytautas, J and Ahuja, A and Iaboni, A},
title = {Technology-Enabled Recreation and Leisure Programs and Activities for Older Adults With Cognitive Impairment: Rapid Scoping Review.},
journal = {JMIR neurotechnology},
volume = {3},
number = {},
pages = {e53038},
pmid = {41341243},
issn = {2817-092X},
abstract = {BACKGROUND: Recreational and leisure activities significantly contribute to the well-being of older adults, positively impacting physical, cognitive, and mental health. However, limited mobility and cognitive decline often impede access to these activities, particularly for individuals living with dementia. With the increasing availability of digital technologies, there is a rising interest in using technology to deliver recreation and leisure activities for cognitively impaired individuals, acknowledging its potential to provide diverse experiences. The COVID-19 pandemic further highlighted the need for virtual program delivery, especially for individuals in long-term care settings, leading to the development of tools like the Dementia Isolation Toolkit aimed at supporting compassionate isolation. To better support future implementations of the DIT, our rapid scoping review explores evidence-based, technology-enabled recreation programs for older adults with cognitive impairments, which promote well-being.

OBJECTIVE: We conducted a rapid scoping review of published peer-reviewed literature to answer the following research question: What recreation and leisure programs or activities are being delivered using technology to adults living with dementia or another form of cognitive impairment?

METHODS: In total, 6 databases were searched by an Information Specialist. Single reviewers performed title or abstract review, full-text screening, data extraction, and study characteristic summarization.

RESULTS: A total of 92 documents representing 94 studies were identified. The review identified a variety of technology-enabled delivery methods, including robots, gaming consoles, tablets, televisions, and computers, used to engage participants in recreational and leisure activities. These technologies impacted mood, cognition, functional activity, and overall well-being among older adults with cognitive impairments. Activities for socializing were the most common, leveraging technologies such as social robots and virtual companions, while relaxation methods used virtual reality and digital reminiscence therapy. However, challenges included technological complexity and potential distress during reminiscing activities, prompting recommendations for diversified research settings, and increased sample sizes to comprehensively understand technology's impact on leisure among this demographic.

CONCLUSIONS: The findings suggest that technology-enabled recreational activities, such as socializing, relaxation and self-awareness activities, music and dance, exergaming, and art, can positively impact the mood and overall well-being of older adults with cognitive impairment. Future research should embrace a more inclusive approach, integrating design, diverse settings, and a broader sample of older adults to develop technology-driven leisure activities tailored to their unique needs and promote their effective use.},
}

@article {pmid41341247,
year = {2024},
author = {Lefkovitz, I and Walsh, S and Blank, LJ and Jetté, N and Kummer, BR},
title = {Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review.},
journal = {JMIR neurotechnology},
volume = {3},
number = {},
pages = {e51822},
pmid = {41341247},
issn = {2817-092X},
abstract = {BACKGROUND: Natural language processing (NLP), a branch of artificial intelligence that analyzes unstructured language, is being increasingly used in health care. However, the extent to which NLP has been formally studied in neurological disorders remains unclear.

OBJECTIVE: We sought to characterize studies that applied NLP to the diagnosis, prediction, or treatment of common neurological disorders.

METHODS: This review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) standards. The search was conducted using MEDLINE and Embase on May 11, 2022. Studies of NLP use in migraine, Parkinson disease, Alzheimer disease, stroke and transient ischemic attack, epilepsy, or multiple sclerosis were included. We excluded conference abstracts, review papers, as well as studies involving heterogeneous clinical populations or indirect clinical uses of NLP. Study characteristics were extracted and analyzed using descriptive statistics. We did not aggregate measurements of performance in our review due to the high variability in study outcomes, which is the main limitation of the study.

RESULTS: In total, 916 studies were identified, of which 41 (4.5%) met all eligibility criteria and were included in the final review. Of the 41 included studies, the most frequently represented disorders were stroke and transient ischemic attack (n=20, 49%), followed by epilepsy (n=10, 24%), Alzheimer disease (n=6, 15%), and multiple sclerosis (n=5, 12%). We found no studies of NLP use in migraine or Parkinson disease that met our eligibility criteria. The main objective of NLP was diagnosis (n=20, 49%), followed by disease phenotyping (n=17, 41%), prognostication (n=9, 22%), and treatment (n=4, 10%). In total, 18 (44%) studies used only machine learning approaches, 6 (15%) used only rule-based methods, and 17 (41%) used both.

CONCLUSIONS: We found that NLP was most commonly applied for diagnosis, implying a potential role for NLP in augmenting diagnostic accuracy in settings with limited access to neurological expertise. We also found several gaps in neurological NLP research, with few to no studies addressing certain disorders, which may suggest additional areas of inquiry.

TRIAL REGISTRATION: Prospective Register of Systematic Reviews (PROSPERO) CRD42021228703; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=228703.},
}

@article {pmid41340464,
year = {2023},
author = {Sharma, S and Saini, A and Dhawan, D},
title = {Lipid Metabolic Disorders in Neurodegenerative Diseases - Role of Androgen Receptor.},
journal = {The Eurasian journal of medicine},
volume = {55},
number = {Suppl 1},
pages = {S1-S8},
doi = {10.5152/eurasianjmed.2023.23024},
pmid = {41340464},
issn = {1308-8734},
abstract = {The challenge of managing neurodegenerative disorders is a worldwide concern, especially in the aging population. Neurodegenerative diseases are a varied group of disorders that are characterized by progressive degeneration of the structure and function of the nerve cells. Neurodegenerative diseases are increasing at an alarming rate, and hence there is an urgent need for an in-depth analysis of various metabolic malfunctions that alter the proper functioning of a cell. Lipid metabolism is a process that involves the synthesis and simultaneous degradation of lipids and encompasses a balance that is essential to maintain the structural and functional ability of a cell. Androgen receptor (AR) plays a critical role in regulating cellular functions. Recent studies have expanded our knowledge regarding direct or indirect interactions that occur among mitochondria, peroxisome, and androgen receptors, which play a crucial role in lipid homeostasis. Unusual levels of lipids and cholesterol due to receptor excitation or inhibition are associated with multiple diseases and have been a cause of concern. The androgen receptor, along with other receptors and proteins, forms an important metabolic cascade that, if altered, may cause the accumulation of lipids and result in neurodegenerative disorders. In this review, we underscore the role of the androgen receptor in regulating lipid and cholesterol levels during neurodegenerative disorders (Alzheimers, Parkinson's, multiple sclerosis, and Huntington's disease).},
}

@article {pmid41337804,
year = {2025},
author = {Tang, J and Feng, D and Guan, L and Xu, Z and Zou, Y},
title = {Dissecting the atomic effects of S305 phosphorylation on the aggregation of the core tau peptides of the first intermediate filament.},
journal = {Colloids and surfaces. B, Biointerfaces},
volume = {259},
number = {},
pages = {115326},
doi = {10.1016/j.colsurfb.2025.115326},
pmid = {41337804},
issn = {1873-4367},
abstract = {Tauopathies, containing aging-related Alzheimer's disease (AD) and contact sports-related chronic traumatic encephalopathy (CTE), etc., are neuropathologically characteristic of the tau protein aggregates in the brain. Targeting the oligomeric species formed in the route of tau fibrilization has been considered a promising therapeutic approach to prevent or treat tauopathies. In vitro experiment reported that S305 phosphorylation (Pho-S305) exerted a protective role in tau aggregation. However, the atomic effect and molecular mechanisms of Pho-S305 on tau aggregation are largely elusive. In this study, we performed replica exchange molecular dynamics (REMD) and classical molecular dynamics (MD) simulations, with a total simulation time of 57 μs, on tau peptides without and with Pho-S305. The protein model was a novel tau fragment (G302-S316), constituting the ordered core of a first intermediate amyloid (FIA) filament of AD- and CTE-specific tau filament. The REMD results revealed that Pho-S305 suppressed the β-sheet formation and weakened the peptide-peptide interaction, thus inhibiting the aggregation of the peptides. Additional MD simulations indicated that the oligomerization dynamics of the peptides were disturbed by Pho-S305. Our findings excavate the mechanistic information underlying the phosphorylation-induced inhibitive effects on tau302-316 aggregation, which may provide potential useful clues for the development of therapeutic avenues for tauopathies.},
}

@article {pmid41337685,
year = {2026},
author = {Gasparini, F and Valletta, M and Vetrano, DL and Beridze, G and Rizzuto, D and Calderón-Larrañaga, A and Fredolini, C and Dale, M and Winblad, B and Fratiglioni, L and Grande, G},
title = {Kidney Function, Alzheimer Disease Blood Biomarkers, and Dementia Risk in Community-Dwelling Older Adults.},
journal = {Neurology},
volume = {106},
number = {1},
pages = {e214446},
doi = {10.1212/WNL.0000000000214446},
pmid = {41337685},
issn = {1526-632X},
mesh = {Humans ; Female ; Male ; Biomarkers/blood ; Aged ; *Alzheimer Disease/blood/epidemiology ; *Glomerular Filtration Rate/physiology ; tau Proteins/blood ; Amyloid beta-Peptides/blood ; Independent Living ; Aged, 80 and over ; Longitudinal Studies ; Sweden/epidemiology ; *Dementia/blood/epidemiology ; Neurofilament Proteins/blood ; Cross-Sectional Studies ; Glial Fibrillary Acidic Protein/blood ; Peptide Fragments/blood ; },
abstract = {BACKGROUND AND OBJECTIVES: Impaired kidney function has been linked to altered concentrations of blood biomarkers of Alzheimer disease (AD), but the underlying mechanisms and its potential role in dementia development remain poorly understood. We explored the associations between estimated glomerular filtration rate (eGFR), blood-based biomarkers of AD, and dementia development.

METHODS: Data were extracted from the Swedish National Study on Aging and Care in Kungsholmen, an ongoing longitudinal population-based study. Kidney function was assessed using eGFR based on serum creatinine. AD biomarkers (amyloid beta [Aβ42/40], phosphorylated tau [p-tau181 and p-tau217] and total tau [t-tau] proteins, neurofilament light chain [NfL], and glial fibrillary acidic protein [GFAP]) were measured from peripheral blood samples using the Simoa platform. Dementia was diagnosed according to DSM-IV criteria. Quantile regression models assessed the cross-sectional associations between eGFR and AD biomarkers; Cox regression models were used to examine the association of kidney function and biomarkers with incident dementia.

RESULTS: At baseline, 2,279 dementia-free participants with available blood samples were included (median age 72 (interquartile range, 61-81) years; 62% female). Lower eGFR was associated with higher median z-score levels of all examined AD blood biomarkers, except Aβ42/40, following a nonlinear relationship. At eGFR = 30 mL/min/1.73 m[2], estimated differences were as follows: p-tau181: β, 0.22 [95% CI 0.09-0.35]; p-tau217: β, 0.20 [95% CI 0.10-0.31]; t-tau: β, 0.24 [95% CI 0.05-0.42]; NfL: β, 0.88 [95% CI 0.80-0.95]; GFAP: β, 0.10 [95% CI 0.03-0.16]. During a mean follow-up period of 8.3 (SD, 4.3) years, 362 participants developed dementia. In multivariable-adjusted models, impaired kidney function (eGFR < 60 mL/min/1.73 m[2]) was not associated with an increased hazard of dementia compared with preserved kidney function (eGFR ≥ 60 mL/min/1.73 m[2]) (hazard ratio [HR], 0.93 [95% CI 0.72-1.21]). The relationship between increased (high vs low) NfL and dementia was stronger among individuals with impaired (vs preserved) kidney function (HR, 3.85 [95% CI 1.87-7.95] vs HR, 1.84 [95% CI 1.34-2.53], respectively).

DISCUSSION.: Impaired kidney function was associated with elevated circulating level of most AD blood biomarkers. However, the presence of impaired kidney function did not independently increase the risk of dementia but rather seemed to accelerate the clinical expression of underlying neurodegenerative pathology.},
}

Humans
Female
Male
Biomarkers/blood
Aged
*Alzheimer Disease/blood/epidemiology
*Glomerular Filtration Rate/physiology
tau Proteins/blood
Amyloid beta-Peptides/blood
Independent Living
Aged, 80 and over
Longitudinal Studies
Sweden/epidemiology
*Dementia/blood/epidemiology
Neurofilament Proteins/blood
Cross-Sectional Studies
Glial Fibrillary Acidic Protein/blood
Peptide Fragments/blood

@article {pmid41337541,
year = {2025},
author = {Weerakkody, TN and Sabelström, H and Andrews, SV and Chadarevian, JP and Chin, MY and Tatarakis, D and Propson, NE and Kim, DJ and Theolis, R and Parico, GCG and Misker, H and Kung, JE and Bandyopadhyay, A and Robles Colmenares, Y and Jackson, TN and Qerqez, AN and Balasundar, S and Davis, SS and Ha, C and Ghosh, R and Ravi, R and Rana, A and Germain, K and Tao, A and Xiong, K and Braun, D and Raju, K and Huang, KC and Zhan, L and Guo, JL and Safari Yazd, H and Sarrafha, L and Capocchi, JK and Hasselmann, J and Chadarevian, AL and Tu, C and Mansour, K and Eskandari-Sedighi, G and Tesi, N and van der Lee, S and Hulsman, M and Oshegov, G and Pijnenburg, Y and Calvert, M and Holstege, H and Suh, JH and Di Paolo, G and Davtyan, H and Lewcock, JW and Blurton-Jones, M and Monroe, KM},
title = {Loss of PILRA promotes microglial immunometabolism to reduce amyloid pathology in cell and mouse models of Alzheimer's disease.},
journal = {Science translational medicine},
volume = {17},
number = {827},
pages = {eadw7428},
doi = {10.1126/scitranslmed.adw7428},
pmid = {41337541},
issn = {1946-6242},
mesh = {*Alzheimer Disease/pathology/metabolism/immunology ; Animals ; *Microglia/metabolism ; Humans ; Disease Models, Animal ; Mice ; Mice, Knockout ; *Receptors, Immunologic/metabolism/deficiency/genetics ; *Amyloid/metabolism ; Mitochondria/metabolism ; Induced Pluripotent Stem Cells/metabolism ; Inflammation/pathology ; },
abstract = {The Alzheimer's disease (AD) genetic landscape identified microglia as a key disease-modifying cell type. Paired immunoglobulin-like type 2 receptor alpha (PILRA) is an immunoreceptor tyrosine-based inhibitory motif domain-containing inhibitory receptor, expressed by myeloid cells such as microglia. The known protective PILRA G78R gene variant reduces AD risk in apolipoprotein E4 (APOE4) carriers and is enriched in a cohort of healthy centenarians. However, mechanisms underlying protective effects in microglia are undefined. Here, we identified biological functions of PILRA in human induced pluripotent stem cell-derived microglia (iMG) and chimeric AD mice. PILRA knockout (KO) in iMG rescued ApoE4-mediated immunometabolic deficits and prevented lipotoxicity through increased lipid storage, improved mitochondrial bioenergetics, and antioxidant activity. PILRA KO also enhanced microglial chemotaxis and attenuated inflammation. With pharmacological inhibitor studies, we showed that peroxisome proliferator-activated receptor and signal transducer and activator of transcription 1/3 mediated PILRA-dependent microglial functions. AD mice transplanted with human PILRA KO microglia exhibited reduced amyloid pathology and rescued synaptic markers. A high-affinity ligand blocking PILRA antibody phenocopied PILRA KO iMG. These findings suggest that PILRA is a pharmacologically tractable therapeutic target for AD.},
}

*Alzheimer Disease/pathology/metabolism/immunology
Animals
*Microglia/metabolism
Humans
Disease Models, Animal
Mice
Mice, Knockout
*Receptors, Immunologic/metabolism/deficiency/genetics
*Amyloid/metabolism
Mitochondria/metabolism
Induced Pluripotent Stem Cells/metabolism
Inflammation/pathology

@article {pmid41337426,
year = {2025},
author = {Li, J and Gao, Y and Yang, P and Guan, Z and Wang, T and Ma, G and Lei, B},
title = {Multi-Modal Feature Fusion Using Transformer for Early Alzheimer's Disease Diagnosis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11253151},
pmid = {41337426},
issn = {2694-0604},
mesh = {*Alzheimer Disease/diagnosis/diagnostic imaging ; Humans ; Deep Learning ; Algorithms ; Early Diagnosis ; },
abstract = {Alzheimer's disease (AD) is a common neurodegenerative disorder. Early and accurate diagnosis of AD is essential for effective treatment. However, due to the class imbalance problem, there is a significant data gap between different categories. Moreover,the data feature differences of AD are relatively small, which poses challenges for its application in the early diagnosis of AD. To tackle these problems, we propose an intelligent early AD diagnosis model based on Transformer. The deep learning diagnosis model utilizes Transformer to integrate image features and non-image features. Furthermore, it incorporates a class imbalance loss function to optimize the performance of early AD diagnosis, thereby enhancing the model's ability to recognize underrepresented classes. In order to alleviate the problem of class imbalance and test the model performance, we used stratified 5-fold cross validation to verify the model effect.Experimental results demonstrate that our model can significantly improve the accuracy of AD diagnosis, which is markedly better than traditional methods. Additionally, loss function we used more effectively mitigates the problem of class imbalance. We believe this work can effectively reduce the burden on medical staff to diagnose early AD.},
}

*Alzheimer Disease/diagnosis/diagnostic imaging
Humans
Deep Learning
Algorithms
Early Diagnosis

@article {pmid41337392,
year = {2025},
author = {Hamrick, WC and King, TJ and Olsen, CD and Nelson, AE and Mauchley, M and Curd, BC and Cliatt Brown, C and Frost, NA and Iversen, MM and George, JA},
title = {Improved Prediction of Activities of Daily Living from Wrist Electromyography Using Intermediate Gesture Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11252749},
pmid = {41337392},
issn = {2694-0604},
mesh = {Humans ; *Activities of Daily Living ; *Electromyography/methods ; *Wrist/physiology ; *Gestures ; Algorithms ; Male ; Female ; },
abstract = {The long-term goal of this research is to automatically, remotely, and noninvasively identify Activities of Daily Living (ADLs) using muscle activity at the wrist. Alzheimer's Dementia is a widespread condition affecting patients' memory and ability to care for themselves. A wrist-worn device capable of monitoring precise ADLs could increase patient independence and provide caretakers with detailed information regarding the patient's wellbeing. Here, we investigate the use of an intermediate hand gesture classifying algorithm to predict ADLs using wrist electromyography (EMG). We show that predicted gestures are well-represented within the span of ADLs, and that the repertoire of predicted gestures appears distinct among ADLs. Importantly, we show that a simple linear discriminant analysis of predicted gestures provides better and more efficient classification accuracy relative to a state-of-the-art neural network that predicts ADLs directly from EMG. Accurate and efficient classification of ADLs from a wrist-worn device can provide a foundation for remote monitoring of patients in a socially acceptable formfactor. More broadly, a better understanding of human behavior via ADL tracking can enable new assistive technologies that improve quality of life.Clinical Relevance-Monitoring activities of daily living with an electromyographic smart watch can provide insights into patient behavior and wellbeing.},
}

Humans
*Activities of Daily Living
*Electromyography/methods
*Wrist/physiology
*Gestures
Algorithms
Male
Female

@article {pmid41337360,
year = {2025},
author = {Lee, HP and Arginteanu, T and Kudela, P and Wyse-Sookoo, K and Anderson, WS and Salimpour, Y},
title = {Phase-Dependent Neuromodulation in a Computational Hippocampal Model.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254724},
pmid = {41337360},
issn = {2694-0604},
mesh = {*Hippocampus/physiology ; Humans ; *Models, Neurological ; Computer Simulation ; Theta Rhythm/physiology ; Neurons/physiology ; Animals ; },
abstract = {The critical role of phase-amplitude coupling (PAC) between oscillations of differing frequencies highlights the promise of phase-dependent neuromodulation as a therapeutic strategy for various neurological conditions. In the hippocampus, theta-gamma PAC is linked to key memory processes and information transfer. Computational models avoid technical challenges in in vivo and in vitro experiments and offer a practical alternative for exploring the mechanisms behind phase-dependent effects. In this study, we built on a published CA3 hippocampal computational model implemented in the NEURON-Python environment. We used a closed-loop autoregressive (AR) forward prediction model that sampled the network's local field potential (LFP) to achieve real-time calculation of stimulus time points locked to a target phase of the theta oscillation. Our approach enabled the delivery of current injections to all neuronal populations at either the peak or the trough of the theta rhythm. Analysis of the resulting network LFP showed distinct phase-dependent changes in the theta band during stimulation. The peak-phase stimulation significantly enhanced theta-gamma coupling. Further study on a large-scale human-based model is needed to better capture these phase-dependent effects. Overall, the results underscored the importance of closed-loop stimulation systems and the potential of phase-targeted neuromodulation to influence PAC. These findings offer new avenues for treating disorders marked by disrupted oscillations, including Alzheimer's disease and other memory disorders.Clinical Relevance- This study provides investigations of the origins of neuronal oscillations and the development of a brain stimulation technique for modulating the level of oscillations, possibly contributing to the development of novel treatment methods for neurological disorders associated with abnormal oscillations..},
}

*Hippocampus/physiology
Humans
*Models, Neurological
Computer Simulation
Theta Rhythm/physiology
Neurons/physiology
Animals

@article {pmid41337354,
year = {2025},
author = {Kang, MK and Elisabet, A and Hong, KS},
title = {Time-frequency deep metric learning of resting-state fNIRS signals for staging Alzheimer's disease.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254651},
pmid = {41337354},
issn = {2694-0604},
mesh = {Humans ; *Alzheimer Disease/diagnosis/diagnostic imaging/physiopathology ; Spectroscopy, Near-Infrared/methods ; *Deep Learning ; Male ; Aged ; Female ; Cognitive Dysfunction ; Rest ; Signal Processing, Computer-Assisted ; },
abstract = {This study proposes a deep metric learning framework designed to embed resting-state functional near-infrared spectroscopy (fNIRS) signals into a feature space using a continuous wavelet transform layer in the time-frequency domain for classifying stages of Alzheimer's disease (AD). Early AD pathology, particularly working memory impairment, is closely linked to changes in frontal-lobe networks. Classifying AD patients based on the low-frequency components (0.008-0.15 Hz) of resting-state fNIRS signals presents a significant research challenge. We collected resting-state fNIRS data from individuals with AD, mild cognitive impairment (MCI), and healthy controls (HCs). Due to the limited size of the dataset and the challenges in quantifying individual signal characteristics, conventional data-intensive deep learning models show constrained performance and generalizability. To address these limitations, our proposed architecture generates robust signal embeddings and evaluates inter-sample similarity using a learned distance metric. Experimental results demonstrate high classification accuracy between AD and HCs in the time- frequency domain. Additionally, our findings indicate that i) frequency-domain metric learning effectively captures brain-signal complexity, and ii) differences in inter-regional activation play a crucial role in the progression of neurodegeneration.},
}

Humans
*Alzheimer Disease/diagnosis/diagnostic imaging/physiopathology
Spectroscopy, Near-Infrared/methods
*Deep Learning
Male
Aged
Female
Cognitive Dysfunction
Rest
Signal Processing, Computer-Assisted

@article {pmid41337319,
year = {2025},
author = {Walsh, A and Shanker, S and Valdes, AL},
title = {Classifying Neurodegenerative Diseases from Selected Temporal EEG Electrodes: Towards Ear-EEG Applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254741},
pmid = {41337319},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/instrumentation/methods ; Electrodes ; *Alzheimer Disease/diagnosis/physiopathology ; Male ; Female ; Aged ; *Neurodegenerative Diseases/diagnosis/physiopathology/classification ; *Ear ; Signal Processing, Computer-Assisted ; Middle Aged ; Machine Learning ; Frontotemporal Dementia/diagnosis/physiopathology ; },
abstract = {Dementia is the seventh leading cause of death globally, with over 55 million people affected. As there is no cure to dementia, early detection is essential. Machine learning applied to EEG has been proposed for enabling early dementia detection, with multiple studies demonstrating successful binary classification of dementias. As a step towards the use of a more accessible tool, ear-EEG, this study investigates the extent to which two common forms of dementia, Alzheimer's Disease (AD) and Fronto-temporal Dementia (FTD), can be classified from temporal electrode EEG using a multiclass classification approach. EEG recordings from 88 participants (29 control, 36 AD and 23 FTD) at rest with eyes closed were used for classification. Features extracted from temporal electrodes (T3, T4, T5, T6, F7, F8) included sub-band power characteristics and band power ratios. Binary classification of healthy vs. AD achieved 81.8% accuracy, while multiclass classification of healthy, AD and FTD achieved 63.8% accuracy. The low accuracy in multiclass classification suggests challenges in differentiating between types of dementia using the proposed features.Clinical relevance- Our results demonstrate the potential of temporal EEG data in developing dementia classification models, highlighting the promise of more accessible tools such as ear-EEG for brain health monitoring. Further advancement of such tools and improvement of multiclass classification models could facilitate early detection of dementia, enabling timely interventions and improved patient outcomes.},
}

Humans
*Electroencephalography/instrumentation/methods
Electrodes
*Alzheimer Disease/diagnosis/physiopathology
Male
Female
Aged
*Neurodegenerative Diseases/diagnosis/physiopathology/classification
*Ear
Signal Processing, Computer-Assisted
Middle Aged
Machine Learning
Frontotemporal Dementia/diagnosis/physiopathology

@article {pmid41337291,
year = {2025},
author = {Wu, K and Sarrigiannis, PG and Blackburn, DJ and He, F},
title = {Enhanced Functional Connectivity for EEG Classification with a Modified Maximum Entropy Model: a Case Study of Alzheimer's Disease.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11253453},
pmid = {41337291},
issn = {2694-0604},
mesh = {*Alzheimer Disease/physiopathology/diagnosis ; Humans ; *Electroencephalography/methods ; Entropy ; Signal Processing, Computer-Assisted ; Algorithms ; Brain/physiopathology ; },
abstract = {Accurate characterization of functional connectivity (FC) in neural signals is critical for investigating a wide range of brain disorders and cognitive processes. Conventional approaches typically rely on correlation-based metrics to capture pairwise interactions; however, such measures may fail to capture more global patterns of connectivity. Pairwise Maximum Entropy Models (pMEM) a complementary global perspective by jointly modeling all pairwise interactions, but they can diverge significantly from traditional linear FC measures and are often prone to numerical instability. In this study, we propose a penalized pMEM framework that incorporates correlation-based FC into pMEM as a prior constraint, aiming to balance the global coupling captured by pMEM with the interpretability and simplicity of correlation-based approaches. By regulating the adherence of pMEM parameters to traditional FC, our method achieves more stable learning and better preserves key linear relationships. We evaluate the proposed framework using a public available EEG dataset comprising individuals with Alzheimer's disease and healthy controls. The proposed hybrid modelling of FC achieves a classification accuracy of 83.13%, outperforming both standalone pMEM and correlation-based classifiers. Furthermore, the penalized pMEM reveals more pronounced group difference in network small-worldness, offering improved interpretability alongside enhanced classification performance.},
}

*Alzheimer Disease/physiopathology/diagnosis
Humans
*Electroencephalography/methods
Entropy
Signal Processing, Computer-Assisted
Algorithms
Brain/physiopathology

@article {pmid41337282,
year = {2025},
author = {Uhm, J and Kim, HY and Choe, B and Agrawal, H and Yoon, S and Yun, JH},
title = {Smartphone Keystroke-based Cognitive Impairment Diagnostic Methodology.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253467},
pmid = {41337282},
issn = {2694-0604},
mesh = {Humans ; *Smartphone ; *Cognitive Dysfunction/diagnosis ; Male ; Aged ; Female ; },
abstract = {With the global population rapidly aging, dementia has emerged as a major public health concern. Significant efforts are being made to address the health and other problems associated with dementia. Although several advances have been made in the diagnosis and treatment of dementia, many challenges remain unresolved. Existing dementia diagnostic methods are often not performed in a timely manner due to poor accessibility, leading to delays in initiation of treatment. In this study, we propose a smartphone keystroke-based diagnostic method as a solution to overcome the limitations associated with the early diagnosis and treatment of dementia. As compared to other existing methods, the proposed diagnostic method is easier to develop and maintain, promoting commercialization and widespread use. The diagnostic method has been designed to extract language-agnostic keystroke data features from smartphone keyboard input logs. Rather than simply extracting features assessing motor skills from keystroke data or features demonstrating language usage patterns from text, this study focused on features that can assess cognitive abilities without using linguistic characteristics. Clinical trials were conducted in patients with mild cognitive impairment and early Alzheimer's dementia were conducted, and a series of experiments and validation tests were performed using the trial data. The results demonstrated that the proposed smartphone keystroke-based diagnostic method is effective in diagnosing cognitive impairment. The proposed method does not require the use of any special equipment except smartphones, which facilitates low-cost commercialization. This study presents a diagnostic method that addresses the problem of people who avoid tests for the diagnosis of dementia due to economic and psychological burdens.Clinical RelevanceThis study provides an approach for early detection of dementia using ordinary smartphone keystroke logs. The proposed method has the potential to improve the quality of life of patients with dementia. Following large-scale clinical research and the integration of more digital biomarkers, the methodology proposed in this study can potentially facilitate the development of an early diagnostic platform for dementia.},
}

Humans
*Smartphone
*Cognitive Dysfunction/diagnosis
Male
Aged
Female

@article {pmid41337161,
year = {2025},
author = {Wang, Y and Butala, AA and Thebaud, T and Villalba, J and Moro-Velazquez, L},
title = {Sampling Rate Guidelines for Eye-Tracking in Neurological Disorders: A Task-Specific Multimetric Analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253777},
pmid = {41337161},
issn = {2694-0604},
mesh = {Humans ; *Eye-Tracking Technology ; *Eye Movements ; *Nervous System Diseases/physiopathology/diagnosis ; Saccades ; Male ; Reproducibility of Results ; Female ; },
abstract = {Variations in eye-tracking sampling rates across devices pose challenges for research characterizing Neurodegenerative Disorders (ND) where precise measurement of eye movements is essential for detection of disease-specific features. This study proposes three metrics-TOST Test Equivalence (TTE), Mean Absolute Error Percentage (MAE%), and Kullback-Leibler Divergence (KLD)-to systematically evaluate how downsampling impacts data quality and feature integrity. Applied to three common tasks (prosaccade, text reading, picture description), results revealed task-specific sampling rate requirements: ⩾400Hz preserved saccadic dynamics in prosaccade, while cognitively complex reading and picture description tasks demanded ⩾600Hz to maintain fidelity. Downsampling degraded key features that are salient to differentiating neurological disorders. ⩾200Hz is required to maintain the discriminative power of key saccade and fixation features. We propose task- and feature-specific recommendations for sampling selection to balance analytical rigor with practical constraints. These findings provide actionable guidelines for standardizing eye-tracking protocols in behavioral research, enhancing reproducibility and robust cross-study comparisons and biomarker validation.Clinical relevanceThis study provides guidelines for selecting eye-tracking sampling rates to ensure reliable detection of disease-specific eye movement features in Alzheimer's, Parkinson's, and related disorders.},
}

Humans
*Eye-Tracking Technology
*Eye Movements
*Nervous System Diseases/physiopathology/diagnosis
Saccades
Male
Reproducibility of Results
Female

@article {pmid41337160,
year = {2025},
author = {G, SP and Gupta, A},
title = {Computational Modeling of NMDAR-Mediated Calcium Dysregulation in Hippocampal Dendritic Spines in Alzheimer's Disease.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253866},
pmid = {41337160},
issn = {2694-0604},
mesh = {*Alzheimer Disease/metabolism/pathology/physiopathology ; *Receptors, N-Methyl-D-Aspartate/metabolism ; *Hippocampus/metabolism/pathology ; Humans ; *Dendritic Spines/metabolism/pathology ; *Calcium/metabolism ; Computer Simulation ; Calcium Signaling ; Animals ; *Models, Neurological ; },
abstract = {Disruptions in calcium (Ca[2][+]) signaling play a central role in neurodegenerative processes, particularly in Alzheimer's Disease (AD), where Aβ pathology alters synaptic function. This study presents a computational framework integrating an NMDAR model into an existing astrocyte calcium signaling model adapted for hippocampal dendritic spine to examine the effects of Aβ-induced Ca[2][+] dysregulation. The model incorporates NMDAR-mediated Ca[2][+] influx, ER calcium dynamics (IP3Rs, RyRs, SERCA pumps, buffering), to simulate pathological Ca[2][+] signaling. Results indicate that Aβ increases NMDAR burst probability and conductance, leading to sustained cytosolic Ca[2][+] elevation, further influenced by ER-mediated Ca[2][+] release, which contributes to calcium imbalance. Elevated glutamate levels are also observed to exacerbate these effects. The study further examines possible modulatory mechanisms, suggesting that changes in NMDAR kinetics and calcium-buffering capacity may influence intracellular Ca[2][+] stabilization. Findings highlight the complex interplay between glutamate signaling, ER stress, and NMDAR dysfunction in synaptic calcium dysregulation. The model is constrained by physiological values and the range of values for the model outputs are validated where possible with experimental studies. The model provides a framework for investigating potential mechanisms underlying disruptions in calcium homeostasis in AD and could be extended to study CaMKII activation pathways, synaptic remodeling dynamics, and calcium-binding proteins to enhance its predictive capabilities and broader applicability in understanding AD pathology.Clinical Relevance- Aβ-driven calcium dysregulation is implicated in early synaptic dysfunction in AD, making it a potential target for therapeutic strategies. Insights from this model may help in exploring how modulating NMDAR activity, ER calcium regulation, and calcium-buffering proteins could influence synaptic stability offering directions for future research into neuroprotective strategies for AD.},
}

*Alzheimer Disease/metabolism/pathology/physiopathology
*Receptors, N-Methyl-D-Aspartate/metabolism
*Hippocampus/metabolism/pathology
Humans
*Dendritic Spines/metabolism/pathology
*Calcium/metabolism
Computer Simulation
Calcium Signaling
Animals
*Models, Neurological

@article {pmid41337145,
year = {2025},
author = {He, S and Yang, B and Chen, Y and Zhou, L},
title = {A Novel Graph Neural Network Framework for Brain Age Prediction.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254083},
pmid = {41337145},
issn = {2694-0604},
mesh = {Humans ; *Brain/diagnostic imaging/physiopathology ; Magnetic Resonance Imaging ; *Alzheimer Disease/diagnostic imaging/physiopathology ; *Neural Networks, Computer ; Aged ; *Aging ; Male ; Female ; Algorithms ; Machine Learning ; Neuroimaging ; Graph Neural Networks ; },
abstract = {Alzheimer's disease (AD) is a neurodegenerative disorder that causes cognitive decline, and early detection remains a challenge. Resting-state functional MRI (rs-fMRI) has shown potential for identifying early AD signs by analyzing brain connectivity. In this study, we propose a Hierarchical GCN-Transformer Network (HGTNet) for brain age prediction using rs-fMRI data. Through experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we demonstrate that, compared to traditional machine learning and deep learning methods, the combination of Graph Convolutional Networks (GCN) and Transformer architecture enhances the model's ability to capture complex brain interactions. Our model's more accurate brain age predictions provide a valuable step in identifying early neurodegenerative changes, aiding in the better intervention and management of Alzheimer's disease.},
}

Humans
*Brain/diagnostic imaging/physiopathology
Magnetic Resonance Imaging
*Alzheimer Disease/diagnostic imaging/physiopathology
*Neural Networks, Computer
Aged
*Aging
Male
Female
Algorithms
Machine Learning
Neuroimaging
Graph Neural Networks

@article {pmid41337121,
year = {2025},
author = {Sharan, RV and Xiong, H and Tseng, YJ},
title = {Fusion of Traditional and Large Language Models for Linguistic Analysis in Alzheimer's Disease Detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11254116},
pmid = {41337121},
issn = {2694-0604},
mesh = {*Alzheimer Disease/diagnosis ; Humans ; *Linguistics/methods ; *Language ; Algorithms ; Speech ; Female ; Male ; Large Language Models ; },
abstract = {Language-based markers hold potential for the detection of Alzheimer's disease (AD), offering a non-invasive and accessible alternative to traditional methods. Previous studies have leveraged traditional language models (TLM) or large language models (LLM) for AD detection, but their effectiveness is limited by the lack of annotated datasets for training. To address this limitation, this work proposes a fusion of TLM and LLM for classification. Transcripts of speech from 156 AD and non-AD subjects are preprocessed and used to extract linguistic features for TLM classifiers, including random forest (RF), which achieves a test accuracy of 89.6%. In parallel, fine-tuning a pre-trained BERT model on the same dataset results in a test accuracy of 87.5%. The outputs from RF and BERT are then fused using a multilayer perceptron, which achieves a test accuracy of 91.7%. Additionally, word cloud analysis highlights distinct patterns in filler word usage, such as increased reliance on "uh" in AD speech versus "um" in non-AD speech, reflecting linguistic differences linked to cognitive decline. The proposed fusion of traditional and large language models improves AD detection and presents a scalable framework for language-based diagnostic systems.},
}

*Alzheimer Disease/diagnosis
Humans
*Linguistics/methods
*Language
Algorithms
Speech
Female
Male
Large Language Models

@article {pmid41337029,
year = {2025},
author = {Guo, Y and Wang, Y and Moro-Velazquez, L and Dehak, N},
title = {Leveraging Eye-Tracking Signals for Neurodegenerative Disease Classification with Deep Learning Models.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11254272},
pmid = {41337029},
issn = {2694-0604},
mesh = {Humans ; *Deep Learning ; *Neurodegenerative Diseases/diagnosis/physiopathology/classification ; Male ; Female ; Aged ; *Eye-Tracking Technology ; Parkinson Disease/diagnosis/physiopathology ; Alzheimer Disease/diagnosis/physiopathology ; *Eye Movements ; Middle Aged ; },
abstract = {Neurodegenerative diseases (NDs) can induce subtle changes in eye movements, which can be used for diagnosis or evaluation. This study employs deep learning models to classify NDs using raw eye-tracking data from 133 participants: 51 healthy controls (CTL), 25 with Alzheimer's Disease (AD), 39 with Parkinson's Disease (PD), and 18 with Parkinson's Disease Mimics (PDM)-PD-like diseases often misdiagnosed as PD. Eye movements were recorded during smooth pursuit, text reading, and picture description tasks. Results suggest the text reading task, being structured and cognitively demanding, best distinguished CTL from PD and AD, while the motor-oriented smooth pursuit task differentiated PD from PDM. Ablation studies suggest pupil size improved classification accuracy, especially for CTL vs. PD, and binocular data was crucial for distinguishing PD from PDM. This study shows deep learning models can classify NDs using raw eye-tracking data, becoming a potential tool for objective evaluation in neurology and primary care.Clinical relevance-This project illustrates the efficacy of deep learning methods in leveraging patients' eye movement to accurately predict the presence of neurodegenerative diseases.},
}

Humans
*Deep Learning
*Neurodegenerative Diseases/diagnosis/physiopathology/classification
Male
Female
Aged
*Eye-Tracking Technology
Parkinson Disease/diagnosis/physiopathology
Alzheimer Disease/diagnosis/physiopathology
*Eye Movements
Middle Aged

@article {pmid41336985,
year = {2025},
author = {Salinas-Medina, A and Gonzalez-Mitjans, A and Toussaint, PJ and Liu, X and Evans, A},
title = {HISRON: AI-Driven GPU-Accelerated Framework for Scalable High-Resolution Neuroimaging Analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11253703},
pmid = {41336985},
issn = {2694-0604},
mesh = {Humans ; *Neuroimaging/methods ; *Image Processing, Computer-Assisted/methods ; *Computer Graphics ; Algorithms ; *Artificial Intelligence ; Brain/diagnostic imaging ; },
abstract = {Ultra-high-resolution imaging, particularly cellular neuroimaging, poses challenges from terabyte-scale data and computational complexity. We present HISRON (High-Resolution Scalable Neuroimaging) a GPU-accelerated framework enabling scalable multidimensional analysis, combining unsupervised learning for adaptive feature extraction, benchmarked anisotropic diffusion for noise reduction, and promptable segmentation models with a zero-shot generalization feature. Dynamic overlap-aware tiling maximizes parallelization while preserving spatial context, enabling real-time processing of complex structures. Built on NVIDIA CUDA and CuPy, the framework achieves transformative efficiency: 10x faster noise reduction and detection of 200,000 neuron centroids in 30 seconds (40% pipeline improvement). This advances integration with AI-driven segmentation/classification pipelines, overcoming bottlenecks in high-dimensional computer vision. By emphasizing scalability, our method accelerates analysis of biomedical imaging data, directly supporting translational healthcare innovations in neuroscience. The tool's adaptability underscores its potential for clinical research, enhancing precision in neuroanatomical studies and fostering discoveries in brain function and pathology.Clinical relevance-The proposed framework directly addresses critical challenges in modern clinical neuroimaging, where the analysis of high-resolution data is essential for diagnosing and monitoring neurological disorders such as Alzheimer's, Parkinson's diseases, and epilepsy. By enabling real-time processing of terabyte-scale datasets, this technology reduces delays in image interpretation, facilitating faster decision-making in time-sensitive scenarios, such as intraoperative imaging during neurosurgery or stroke assessment. The zero-shot segmentation model's adaptability ensures robust performance across heterogeneous imaging protocols, which is vital for multicenter clinical studies and personalized treatment planning. Additionally, the framework's efficiency in detecting neuron populations at scale supports large-scale neuroanatomical studies, enhancing our understanding of brain connectivity abnormalities in psychiatric and neurodegenerative conditions. By lowering computational barriers, this tool democratizes access to advanced imaging analytics, empowering clinics with limited resources to adopt precision medicine approaches. These advancements align with the growing demand for AI-driven scalable solutions to improve diagnostic accuracy, accelerate therapeutic discovery, and optimize patient outcomes in neurology and neurorehabilitation.},
}

Humans
*Neuroimaging/methods
*Image Processing, Computer-Assisted/methods
*Computer Graphics
Algorithms
*Artificial Intelligence
Brain/diagnostic imaging

@article {pmid41336968,
year = {2025},
author = {Wardynski, M and Iacopini, I and Petri, G and Latora, V and Crimi, A},
title = {Modeling the Spread of Misfolded Proteins in Alzheimer's Disease using Higher-Order Simplicial Complex Contagion.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11253716},
pmid = {41336968},
issn = {2694-0604},
mesh = {*Alzheimer Disease/metabolism ; Humans ; *Protein Folding ; Amyloid beta-Peptides/metabolism/chemistry ; Brain/metabolism ; },
abstract = {Neurodegenerative diseases are characterized by complex proteins misfolded that propagate within the brain. For instance, current findings highlight the role of 2 specific misfolded proteins in Alzheimer which are believed to spread using brain fibers as highways. Previous studies investigated such spreading by simulation models or machine learning-based predictors which adopt the brain connectome as the underlying spreading network. However, the structural connectome by construction only describes pairwise connections between nodes in a graph. High-order interaction complex networks offer significant advantages over normal graphs because they can capture interactions that go beyond simple pairwise relationships. Protein misfolding and aggregation often involve cooperative behaviors or group dynamics that normal graphs, with their focus on individual edges, cannot adequately represent. The non-linear and multiscale nature of protein misfolding might be better suited to a richer representation of higher-order models. In this study we investigate whether higher-order networks can provide improved fits and explanatory power in this context. More specifically, we employ a simplicial complex contagion model for amyloid beta to predict protein misfolding spread. The simplicial contagion complex produced a mean reconstruction error of 0.030 for Alzheimer's patients regarding the predicted protein deposition across all brain regions in a 2-year horizon and other results, outperforming previous studies, especially for cases in which the misfolded proteins were non-increasing steadily. Despite the limited time span, this study highlights the potential of combining advanced network analysis to capture the intricate dynamics of protein aggregation across neural networks.Clinical relevance- This study highlights the potential of high-order networks to improve predictions of misfolded protein spread in Alzheimer's, offering better insight into protein aggregation dynamics.},
}

*Alzheimer Disease/metabolism
Humans
*Protein Folding
Amyloid beta-Peptides/metabolism/chemistry
Brain/metabolism

@article {pmid41336848,
year = {2025},
author = {Zhang, S and Chattopadhyay, T and Thomopoulos, SI and Ambite, JL and Thompson, PM and Ver Steeg, G},
title = {Diffusion Bridge Models for 3D Medical Image Translation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11252839},
pmid = {41336848},
issn = {2694-0604},
mesh = {Humans ; *Imaging, Three-Dimensional/methods ; *Diffusion Tensor Imaging/methods ; *Brain/pathology ; Male ; Female ; Alzheimer Disease/pathology ; Algorithms ; },
abstract = {Diffusion tensor imaging (DTI) provides crucial insights into the microstructure of the human brain, but it can be time-consuming to acquire compared to more readily available T1-weighted (T1w) magnetic resonance imaging (MRI). To address this challenge, we propose a diffusion bridge model for 3D brain image translation between T1w MRI and DTI modalities. Our model learns to generate high-quality DTI fractional anisotropy (FA) images from T1w images and vice versa, enabling cross-modality data augmentation and reducing the need for extensive DTI acquisition. We evaluate our approach using perceptual similarity, pixel-level agreement, and distributional consistency metrics, demonstrating strong performance in capturing anatomical structures and preserving information on white matter integrity. The practical utility of the synthetic data is validated through sex classification and Alzheimer's disease classification tasks, where the generated images achieve comparable performance to real data. Our diffusion bridge model offers a promising solution for improving neuroimaging datasets and supporting clinical decision-making, with the potential to significantly impact neuroimaging research and clinical practice.},
}

Humans
*Imaging, Three-Dimensional/methods
*Diffusion Tensor Imaging/methods
*Brain/pathology
Male
Female
Alzheimer Disease/pathology
Algorithms

@article {pmid41336827,
year = {2025},
author = {LaGrow, TJ and Itkyal, V and Watters, H and Jensen, KM and Ballem, R and Pan, WJ and Iraji, A and Calhoun, VD and Keilholz, S},
title = {Widespread Spatiotemporal Patterns of Functional Brain Networks in Longitudinal Progression of Alzheimer's Disease.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11251603},
pmid = {41336827},
issn = {2694-0604},
mesh = {Humans ; *Alzheimer Disease/physiopathology ; Disease Progression ; Magnetic Resonance Imaging/methods ; *Brain/physiopathology ; Male ; *Nerve Net/physiopathology ; Female ; Aged ; Longitudinal Studies ; },
abstract = {Alzheimer's Disease (AD) is characterized by progressive functional network disruptions that precede cognitive decline, yet traditional functional connectivity analyses often fail to capture transient network instabilities critical for early diagnosis. This study investigates the role of Quasi-Periodic Patterns (QPPs) in identifying disease-related connectivity changes across longitudinal stable disease stages (sNC, sMCI, sDAT) and transitioning (uNC, pMCI) AD cohorts using resting-state fMRI data from the Alzheimer's Disease Neuroimaging Initiative. By integrating QPP occurrences with intrinsic connectivity networks (ICNs), we assessed network integrity across disease stages, with statistical significance evaluated using the Kruskal-Wallis test and Dunn's test for post-hoc analysis. Results revealed a progressive decline in functional connectivity integrity, with early impairments in subcortical and executive function networks in stable groups, followed by widespread disconnection in higher cognition, sensorimotor, and visual networks at later stages. Transitioning AD groups exhibited earlier disruptions in visual and cerebellar networks, suggesting their potential as early biomarkers for disease onset. The occurrence of QPPs decreased significantly with disease progression, indicating an increase in functional disconnection. These findings highlight the synergy between QPPs and ICNs as a dynamic and sensitive biomarker framework for AD progression. Future research should further explore this integration within multimodal imaging and clinical diagnostic frameworks to enhance early detection and intervention strategies.},
}

Humans
*Alzheimer Disease/physiopathology
Disease Progression
Magnetic Resonance Imaging/methods
*Brain/physiopathology
Male
*Nerve Net/physiopathology
Female
Aged
Longitudinal Studies

@article {pmid41336754,
year = {2025},
author = {Zhang, M and Liu, H and Hao, X and Liu, H and Cong, F and Tommi, K},
title = {Time-Frequency Analysis of Frontal EEG Channels for Alzheimer's Disease Detection Using Deep Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11254406},
pmid = {41336754},
issn = {2694-0604},
mesh = {*Alzheimer Disease/diagnosis/physiopathology ; Humans ; *Electroencephalography/methods ; *Deep Learning ; Signal Processing, Computer-Assisted ; Wavelet Analysis ; Algorithms ; },
abstract = {Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects the elderly population. Electroencephalography (EEG) offers a non-invasive and cost-effective approach for real-time monitoring of brain activity in AD patients. This study proposes a novel EEG-based framework for AD detection, in which frontal EEG signals are transformed into time-frequency representations using continuous wavelet transform (CWT) and encoded as red-green-blue (RGB) spectrograms. A ResNet18 neural network, enhanced with a parallel convolutional block attention module (CBAM), is employed for feature extraction and classification. The proposed method achieves an accuracy of 89.23% using leave-one-subject-out cross-validation (LOSOCV) on a publicly available dataset, outperforming previously reported methods under the same validation protocol. To evaluate the generalization ability of the model, we further test it on a related neurodegenerative condition-frontotemporal dementia (FTD)-and observe promising classification performance. These results suggest that the proposed approach not only provides accurate AD detection but also holds potential for broader applications in neurodegenerative disorder analysis.Clinical Relevance: This study highlights the practicality and effectiveness of utilizing non-invasive EEG signals from frontal brain regions for AD detection. The proposed framework has the potential to assist clinicians in developing timely intervention strategies, thereby improving patient outcomes and quality of life.},
}

*Alzheimer Disease/diagnosis/physiopathology
Humans
*Electroencephalography/methods
*Deep Learning
Signal Processing, Computer-Assisted
Wavelet Analysis
Algorithms

@article {pmid41336726,
year = {2025},
author = {Sanmartin, J and Leon-Guijarro, JL and Romero-Martin, JA and Castellote-Huguet, P and Lopez, B and Santabarbara, JM and Valles-Lluch, A and Maceira, AM and Lloret, A and Moratal, D},
title = {Development of Neuroimaging-based Biomarkers for Early Detection of Alzheimer's Disease.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254349},
pmid = {41336726},
issn = {2694-0604},
mesh = {*Alzheimer Disease/diagnosis ; Humans ; *Biomarkers/metabolism ; *Neuroimaging/methods ; Early Diagnosis ; *Magnetic Resonance Imaging/methods ; Cognitive Dysfunction/diagnosis ; Male ; Female ; Aged ; Machine Learning ; },
abstract = {Alzheimer's Disease (AD), the leading cause of dementia, is characterized by a progressive cognitive decline and structural brain degeneration. This study is aimed to identify MRI-based biomarkers for early diagnosis of AD, by analyzing data from three clinical groups: controls, mild cognitive impairment (MCI) and AD patients. A total of 589 parameters were extracted: 536 from volumetry, 35 from diffusion, and 12 from perfusion measurements. Statistical analysis focused on two classification tasks: a binary classification to differentiate between controls and unhealthy patients, and a multiclass classification to distinguish between controls, MCI and AD patients. Key volumetric parameters included gray and white matter, hippocampus, limbic system, ventricles and the subventricular zone (SVZ). All the regions analyzed for diffusion and perfusion provided at least one significant parameter. Thirty key biomarkers were selected for each classification task and used to train machine learning models. These were KNN, SVM and Ensemble methods, trained with 5, 10, 15, 20, 25 and 30 parameters as well as a PCA 95%. Ensemble models achieved the highest accuracy, with 85% for binary classification and 75% for multiclass classification. These findings demonstrate the promise of MRI biomarkers combined with machine learning techniques for enhancing early diagnosis of AD.Clinical Relevance- This project provides innovative MRI-based biomarkers for Alzheimer's diagnosis. Early identification of neurodegeneration is crucial for improving patient outcomes. These findings could also influence on diagnostic protocols to enable a more accurate differentiation between healthy individuals, those with mild cognitive impairment and Alzheimer's Disease patients.},
}

*Alzheimer Disease/diagnosis
Humans
*Biomarkers/metabolism
*Neuroimaging/methods
Early Diagnosis
*Magnetic Resonance Imaging/methods
Cognitive Dysfunction/diagnosis
Male
Female
Aged
Machine Learning

@article {pmid41336569,
year = {2025},
author = {Gao, Y and Calhoun, VD and Miller, RL},
title = {Generative Forecasting of Brain Activity Enhances Alzheimer's Classification and Interpretation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11253394},
pmid = {41336569},
issn = {2694-0604},
mesh = {*Alzheimer Disease/physiopathology/diagnostic imaging/classification/diagnosis ; Humans ; *Brain/physiopathology/diagnostic imaging ; *Magnetic Resonance Imaging/methods ; Forecasting ; },
abstract = {Understanding the relationship between cognition and intrinsic brain activity through purely data-driven approaches remains a significant challenge in neuroscience. Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive method to monitor regional neural activity, providing a rich and complex spatiotemporal data structure. Deep learning has shown promise in capturing these intricate representations. However, the limited availability of large datasets, particularly for disease-specific groups such as Alzheimer's Disease (AD), constrains the generalizability of deep learning models. In this study, we focus on multivariate time series forecasting of independent component networks derived from rs-fMRI as a form of data augmentation, using both a conventional LSTM-based model and the novel Transformer-based BrainLM model. We assess their utility in AD classification, demonstrating how generative forecasting enhances classification performance. Post-hoc interpretation of BrainLM reveals class-specific brain network sensitivities associated with AD.},
}

*Alzheimer Disease/physiopathology/diagnostic imaging/classification/diagnosis
Humans
*Brain/physiopathology/diagnostic imaging
*Magnetic Resonance Imaging/methods
Forecasting

@article {pmid41336503,
year = {2025},
author = {Dhinagar, NJ and Thomopoulos, SI and Thompson, PM},
title = {Leveraging a Vision-Language Model with Natural Text Supervision for MRI Retrieval, Captioning, Classification, and Visual Question Answering.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11251809},
pmid = {41336503},
issn = {2694-0604},
mesh = {*Magnetic Resonance Imaging ; Humans ; *Natural Language Processing ; Algorithms ; Brain/diagnostic imaging ; Alzheimer Disease/diagnostic imaging ; Deep Learning ; *Information Storage and Retrieval/methods ; Image Processing, Computer-Assisted/methods ; },
abstract = {Large multimodal models are now extensively used worldwide, with the most powerful ones trained on massive, general-purpose datasets. Despite their rapid deployment, concerns persist regarding the quality and domain relevance of the training data, especially in radiology, medical research, and neuroscience. Additionally, healthcare data privacy is paramount when querying models trained on medical data, as is transparency regarding service hosting and data storage. So far, most deep learning algorithms in radiologic research are designed to perform a specific task (e.g., diagnostic classification) and cannot be prompted to perform multiple tasks using natural language. In this work, we introduce a framework based on vector retrieval and contrastive learning to efficiently learn visual brain MRI concepts via natural language supervision. We show how the method learns to identify factors that affect the brain in Alzheimer's disease (AD) via joint embedding and natural language supervision. First, we pretrain separate text and image encoders using self-supervised learning, and jointly fine-tune these encoders to develop a shared embedding space. We train our model to perform multiple tasks, including MRI retrieval, MRI captioning, and MRI classification. We show its versatility by developing a retrieval and re-ranking mechanism along with a transformer decoder for visual question answering. Clinical Relevance - By learning a cross-modal embedding of radiologic features and text, our approach can learn to perform diagnostic and prognostic assessments in AD research as well as to assist practicing clinicians. Integrating medical imaging with clinical descriptions and text prompts, we aim to provide a general, versatile tool for detecting radiologic features described by text, offering a new approach to radiologic research.},
}

*Magnetic Resonance Imaging
Humans
*Natural Language Processing
Algorithms
Brain/diagnostic imaging
Alzheimer Disease/diagnostic imaging
Deep Learning
*Information Storage and Retrieval/methods
Image Processing, Computer-Assisted/methods

@article {pmid41336445,
year = {2025},
author = {Newman, JL and Collins, J and Phillips, JS},
title = {Discriminating and Measuring Activities of Daily Living.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11251699},
pmid = {41336445},
issn = {2694-0604},
mesh = {*Activities of Daily Living ; Humans ; Wearable Electronic Devices ; Male ; Algorithms ; },
abstract = {Activities of Daily Living (ADLs) are used to assess the ability of individuals to look after themselves in the home, and can be used to aid the diagnosis of degenerative brain diseases, such as Alzheimer's Disease. The current methods for assessing ADLs require the subjective input of a human, and as such may not reflect the objective capabilities of the individual. Therefore, this work explores the automatic and objective quantification of ADLs using data obtained from a wearable medical device which records eye- and head-movements. We focus on detecting four basic activities including eating, brushing teeth, walking and inactivity. Using a subject-independent testing framework, in which the data from each individual being tested does not appear in the training data, our main contribution is to explore a range of contemporary approaches to time series classification and to compare them to our own previous work in this area. Using a MUSE classifier, we obtain a peak mean accuracy of 84.72%. We also present a system for classifying the duration of each activity, which we suggest may be a good parameter for determining how well an activity has been performed. In this task, we achieve perfect discrimination between three durations of 30 s, 60 s and 90 s.Clinical relevanceDetailed measurements of activities of daily living could aid the diagnosis, monitoring and care of individuals with degenerative brain conditions.},
}

*Activities of Daily Living
Humans
Wearable Electronic Devices
Male
Algorithms

@article {pmid41336423,
year = {2025},
author = {Ganon, SA and Friedman, A and Zigel, Y},
title = {Electroencephalogram Data-Based Analysis of Paroxysmal Slow Wave Events Patterns in Brain Pathologies.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254025},
pmid = {41336423},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; Machine Learning ; *Epilepsy/physiopathology/diagnosis ; *Brain/physiopathology ; Male ; Female ; Signal Processing, Computer-Assisted ; },
abstract = {Slowing of brain activity observed in electroencephalography (EEG) recordings is normal under resting conditions such as sleep. However, a recent series of studies described a new pattern of cortical slowing in patients with epilepsy and Alzheimer's disease, consisting of transient paroxysmal slowing of the network. These paroxysmal slow wave events (PSWEs) were defined with median power frequency (MPF) less than 6 Hz and duration longer than 5 s. In this research, we are using clinical EEG recordings from the Temple University and Bonn University databases. We aim to: (1) Characterize the temporal and spatial characteristics of PSWEs in patients with epilepsy; (2) Identify PSWEs features that will assist in the diagnosis of epilepsy, specifically drug-resistant epilepsy; (3) Identify the sensitivity and specificity of selected combination of features that will help in differentiating between patients with epilepsy and other brain disorders (e.g. Alzheimer's disease, mood disorders). To this end, we trained machine learning models using the Temple University dataset, achieving a classification accuracy of 78.26% in distinguishing between epilepsy and non-epilepsy patients. Moreover, by training the models on the Bonn University database, we achieved an accuracy of 91.67% in classifying drug-resistant epilepsy versus seizure-free groups.Clinical Relevance- PSWEs serve as a potential biomarker for early epilepsy diagnosis and risk assessment, aiding in distinguishing isolated seizures from chronic epilepsy. Their association with neurodegenerative and cognitive disorders further highlights their clinical significance in neurological disease monitoring.},
}

Humans
*Electroencephalography/methods
Machine Learning
*Epilepsy/physiopathology/diagnosis
*Brain/physiopathology
Male
Female
Signal Processing, Computer-Assisted

@article {pmid41336418,
year = {2025},
author = {Nossum, B and Liverud, AE and Dapsance, F and Annavini, E and Modi, B and Hafting, T and Boccara, C and Escobedo-Cousin, E},
title = {A Lightweight FPGA-Based Platform for Low-Power Neural Recording in Freely Moving Animals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11253988},
pmid = {41336418},
issn = {2694-0604},
mesh = {Animals ; *Signal Processing, Computer-Assisted/instrumentation ; Wireless Technology/instrumentation ; Sleep/physiology ; *Electroencephalography/instrumentation ; Equipment Design ; Brain/physiology ; },
abstract = {Understanding the neural mechanisms of sleep is critical for advancing treatments for neurodegenerative diseases. To investigate these mechanisms, long-term electrophysiological studies are conducted in animal models, where brain activity can be monitored continuously under controlled conditions. However, electrophysiological recordings from freely moving animals are typically performed using a tethered setup, which imposes constraints on natural behaviour. As a first step towards a miniaturised, wireless neural implant, we present a low-power system that enables direct control of the Intan RHD2132 electrophysiology chip using a custom FPGA-based stack. A light-weight yet robust implementation ensures efficient operation while maintaining the necessary flexibility for future integration into a compact, wireless system. The FPGA handles real-time signal processing and data transmission, with support for closed-loop stimulation and onboard data compression. Our system achieves continuous multi-channel recording at 20 kHz, with real-time wireless streaming and long-term data storage. The hardware design prioritises low power consumption, with the aim of enabling extended operation for uninterrupted sleep monitoring over 24 hoursClinical Relevance- This technology enables long-term, high-resolution sleep monitoring, which is essential for understanding the role of sleep in neurodegenerative disorders such as Alzheimer's and Parkinson's. By providing continuous, naturalistic brain recordings, this system may help identify early electrophysiological biomarkers of cognitive decline, with potential translational and therapeutics applications.},
}

Animals
*Signal Processing, Computer-Assisted/instrumentation
Wireless Technology/instrumentation
Sleep/physiology
*Electroencephalography/instrumentation
Equipment Design
Brain/physiology

@article {pmid41336397,
year = {2025},
author = {Seo, K and Hwang, J and Kim, K and Argrawal, H and Yun, JH and Yoon Kim, H},
title = {End-to-End Classification of Cognitive Impairment Using Daily-Life Gait Data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254035},
pmid = {41336397},
issn = {2694-0604},
mesh = {Humans ; *Cognitive Dysfunction/diagnosis/physiopathology/classification ; *Gait ; Aged ; Male ; Female ; Middle Aged ; Smartphone ; Accelerometry ; *Activities of Daily Living ; Alzheimer Disease/diagnosis/physiopathology ; Wearable Electronic Devices ; },
abstract = {We investigated the feasibility of using commercial smart devices to prescreen cognitive impairments for timely intervention. A total of 125 individuals aged 50 and older were recruited from a local clinic and categorized into groups based on cognitive diagnosis: Mild Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Subjective Cognitive Decline (SCD).This study focused on distinguishing individuals requiring treatment (AD and MCI) from those without cognitive impairment (SCD) using gait-related data collected from daily life. This classification task was particularly challenging, as the SCD group shares cognitive symptoms with MCI and the data was collected in non-controlled, real-world conditions.Participants used smartphones and smartwatches for one month to collect accelerometer and gyroscope data during daily activities. We preprocessed walking segments and trained a deep learning classifier to differentiate between the two groups. The model achieved an area under the curve (AUC) of 0.70, demonstrating the potential of wearable-based gait analysis for cognitive impairment detection. A sensor ablation study revealed that wrist-worn gyroscope data alone achieved a comparable AUC of 0.70, suggesting a moderate association between arm motion and cognitive impairment. The results further indicated that integrating smartphone accelerometers and smartwatch gyroscopes enhanced classification performance.This study is part of a broader multimodal research initiative that integrates gait analysis with voice recordings, phone usage patterns, and other behavioral data to improve cognitive impairment classification. Future work will explore multi-modal fusion techniques to enhance accuracy and reliability, with the long-term goal of developing accessible, real-world screening tools for early detection.},
}

Humans
*Cognitive Dysfunction/diagnosis/physiopathology/classification
*Gait
Aged
Male
Female
Middle Aged
Smartphone
Accelerometry
*Activities of Daily Living
Alzheimer Disease/diagnosis/physiopathology
Wearable Electronic Devices

@article {pmid41336391,
year = {2025},
author = {Favaro, A and Novotny, K and He, Y and Leng, Y and Das, S and Mekyska, J and Moro-Velazquez, L and Dehak, N},
title = {Toward Inclusive Large-Scale Alzheimer's Disease Detection via Speech and Language Modeling.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254036},
pmid = {41336391},
issn = {2694-0604},
mesh = {Humans ; *Alzheimer Disease/diagnosis/physiopathology ; *Speech ; *Language ; Aged ; Male ; Female ; Cognitive Dysfunction/diagnosis ; },
abstract = {Previous speech-based detection methods for Alzheimer's Disease and Related Dementias (ADRD) have been constrained by small sample sizes, reliance on single corpora, languages, tasks, and recording conditions, limiting their generalizability. Moreover, many studies have simplified cognitive decline progression through binary classification. To address these limitations, we developed a multimodal framework integrating language-agnostic, multilingual, and language-dependent models with demographic data to enhance adaptability across diverse cohorts. We applied this model to a three-class classification problem-cognitively normal controls (CNs), Mild Cognitive Impairment (MCI), and ADRD-using the PREPARE Challenge corpus, which includes 2058 speakers (1140 CNs, 268 MCI, and 650 ADRD). Our best-performing model achieved an F1 score of 0.71 and a log loss of 0.63 on the internal test set, with strong generalization to external test data. Bias mitigation strategies addressed demographic imbalances, including model fusion, data augmentation, and weighted cross-entropy loss. However, challenges remain for underrepresented subgroups. This study highlights the importance of integrating generalizable and language-specific features for scalable, accurate ADRD detection. Future work will expand the dataset to include more languages, improve task diversity, and refine fusion strategies to enhance robustness and scalability in clinical settings.Clinical relevance-The proposed framework provides clinicians with a scalable, inclusive system for early ADRD detection, leveraging multilingual and language-agnostic models to support timely interventions and personalized care, even in resource-constrained or underserved settings.},
}

Humans
*Alzheimer Disease/diagnosis/physiopathology
*Speech
*Language
Aged
Male
Female
Cognitive Dysfunction/diagnosis

@article {pmid41336382,
year = {2025},
author = {Roy, O and Moshfeghi, Y and Smith, J and Ibanez, A and Parra, MA and Smith, KM},
title = {A Hodge-FAST Framework for High-Resolution Dynamic Functional Connectivity Analysis of Higher Order Interactions in EEG Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-6},
doi = {10.1109/EMBC58623.2025.11253015},
pmid = {41336382},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; Algorithms ; Alzheimer Disease/physiopathology ; *Brain/physiopathology ; Cognitive Dysfunction/physiopathology ; },
abstract = {We introduce a novel framework that integrates Hodge decomposition with Filtered Average Short-Term (FAST) functional connectivity to analyze dynamic functional connectivity (DFC) in EEG signals. This method leverages graph-based topology and simplicial analysis to explore transient connectivity patterns at multiple scales, addressing noise, sparsity, and computational efficiency. The temporal EEG data are first sparsified by keeping only the most globally important connections, instantaneous connectivity at these connections is then filtered by global long-term stable correlations. This tensor is then decomposed into three orthogonal components to study signal flows over higher-order structures such as triangle and loop structures. Our analysis of Alzheimer-related MCI patients show significant temporal differences related to higher-order interactions that a pairwise analysis on its own does not implicate. This allows us to capture higher-dimensional interactions at high temporal resolution in noisy EEG signal recordings.},
}

*Electroencephalography/methods
Humans
*Signal Processing, Computer-Assisted
Algorithms
Alzheimer Disease/physiopathology
*Brain/physiopathology
Cognitive Dysfunction/physiopathology

@article {pmid41336322,
year = {2025},
author = {Lin, MC and Kuo, PC and Li, YT and Chen, CY},
title = {Interpretable Multi-Attention Fusion Mechanisms for Early Detection of Transitional Phases in Alzheimer's Disease.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-4},
doi = {10.1109/EMBC58623.2025.11254165},
pmid = {41336322},
issn = {2694-0604},
mesh = {*Alzheimer Disease/diagnosis/diagnostic imaging ; Humans ; Magnetic Resonance Imaging/methods ; Early Diagnosis ; Machine Learning ; Electronic Health Records ; *Attention ; Algorithms ; },
abstract = {Alzheimer's disease (AD), a chronic neurodegenerative disorder, is characterized by progressive cognitive and memory decline, ultimately leading to physical deterioration and death. Early detection is essential for slowing the progression of symptoms and improving patient outcomes. Recent advancements in technology have led to the increased application of machine learning for AD detection. In this study, we aim to identify individuals at risk for future AD onset. To this end, we propose a Multi-Attention Gated Multimodal Unit model that integrates Electronic Health Records (EHRs) and Magnetic Resonance Imaging (MRI) data. We also present an evaluation framework designed to measure the model's effectiveness in early-stage AD detection. Experimental results demonstrate that our approach outperforms baseline models in recognizing early signs of AD. Furthermore, to enhance interpretability, we employ Gradient-Weighted Class Activation Mapping (Grad-Cam) heatmaps and attention maps, offering insights into the model's decision-making process and its ability to detect crucial AD-related features at an early stage.},
}

*Alzheimer Disease/diagnosis/diagnostic imaging
Humans
Magnetic Resonance Imaging/methods
Early Diagnosis
Machine Learning
Electronic Health Records
*Attention
Algorithms

@article {pmid41336319,
year = {2025},
author = {Vlontzou, ME and Athanasiou, M and Davatzikos, C and Nikita, KS},
title = {Comparative assessment of fairness definitions and bias mitigation strategies in machine learning-based diagnosis of Alzheimer's disease from MR images.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-7},
doi = {10.1109/EMBC58623.2025.11254128},
pmid = {41336319},
issn = {2694-0604},
mesh = {*Alzheimer Disease/diagnostic imaging/diagnosis ; Humans ; *Machine Learning ; *Magnetic Resonance Imaging/methods ; Male ; Female ; Aged ; Cognitive Dysfunction/diagnostic imaging ; Bias ; Aged, 80 and over ; },
abstract = {The present study performs a comprehensive fairness analysis of machine learning (ML) models for the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) from MRI-derived neuroimaging features. Biases associated with age, race, and gender in a multi-cohort dataset, as well as the influence of proxy features encoding these sensitive attributes, are investigated. The reliability of various fairness definitions and metrics in the identification of such biases is also assessed. Based on the most appropriate fairness measures, a comparative analysis of widely used pre-processing, in-processing, and post-processing bias mitigation strategies is performed. Moreover, a novel composite measure is introduced to quantify the trade-off between fairness and performance by considering the F1-score and the equalized odds ratio, making it appropriate for medical diagnostic applications. The obtained results reveal the existence of biases related to age and race, while no significant gender bias is observed. The deployed mitigation strategies yield varying improvements in terms of fairness across the different sensitive attributes and studied subproblems. For race and gender, Reject Option Classification improves equalized odds by 46% and 57%, respectively, and achieves harmonic mean scores of 0.75 and 0.80 in the MCI versus AD subproblem, whereas for age, in the same subproblem, adversarial debiasing yields the highest equalized odds improvement of 40% with a harmonic mean score of 0.69. Insights are provided into how variations in AD neuropathology and risk factors, associated with demographic characteristics, influence model fairness.},
}

*Alzheimer Disease/diagnostic imaging/diagnosis
Humans
*Machine Learning
*Magnetic Resonance Imaging/methods
Male
Female
Aged
Cognitive Dysfunction/diagnostic imaging
Bias
Aged, 80 and over

@article {pmid41336252,
year = {2025},
author = {Uudeberg, T and Paeske, L and Bachmann, M},
title = {Longitudinal Stability of Detrended Fluctuation Analysis in Monthly Recorded EEG Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11253319},
pmid = {41336252},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; Male ; *Signal Processing, Computer-Assisted ; Adult ; Longitudinal Studies ; Reproducibility of Results ; },
abstract = {Electroencephalography (EEG) is a cost-effective, noninvasive method with high temporal resolution that enables detailed assessment of neuronal activity. Detrended fluctuation analysis (DFA), a nonlinear technique for quantifying long-range temporal correlations in time-series data, has found application in EEG research across various contexts. This study investigates the temporal stability of DFA over the course of one year, during which 12 monthly EEG recordings were collected from each of nine healthy male participants. Our findings demonstrate excellent within-subject reliability, with intraclass correlation coefficients (ICCs) ranging from 0.985 to 0.997 across 30 EEG channels. These high ICCs indicate that interindividual variability exceeds intraindividual variability, supporting DFA's reliability for long-term neural monitoring. Despite considerable differences between individuals, DFA remained consistent within subjects, highlighting its potential as a subject-specific biomarker for neurological disorders such as epilepsy, depression, and Alzheimer's disease. These findings underscore the importance of accounting for individual variability in EEG measures when developing tools for early diagnosis and clinical monitoring.Clinical Relevance- Reliable biomarkers for neuronal activity must demonstrate consistent temporal stability. This study shows that DFA offers excellent stability within individuals, supported by ICC analyses, suggesting its potential as a subject-specific biomarker for early detection of disorders characterized by altered EEG dynamics.},
}

Humans
*Electroencephalography/methods
Male
*Signal Processing, Computer-Assisted
Adult
Longitudinal Studies
Reproducibility of Results