@article {pmid40364497,
year = {2025},
author = {Kim, JH and Nam, H and Won, D and Im, CH},
title = {Domain-generalized Deep Learning for Improved Subject-independent Emotion Recognition Based on Electroencephalography.},
journal = {Experimental neurobiology},
volume = {},
number = {},
pages = {},
doi = {10.5607/en25011},
pmid = {40364497},
issn = {1226-2560},
abstract = {Electroencephalography (EEG) provides high temporal resolution and noninvasiveness for a range of practical applications, including emotion recognition. However, inherent variability across subjects poses significant challenges to model generalizability. In this study, we systematically evaluated twelve approaches by combining four domain generalization (DG) techniques, Deep CORAL, GroupDRO, VREx, and DANN, with three representative deep learning architectures (ShallowFBCSPNet, EEGNet, and TSception) to enable improved subject-independent EEG-based emotion recognition. The performances of the DG-integrated deep learning models were quantitatively evaluated using two emotional EEG datasets collected by the authors. Data from each subject were treated as distinct domains in each model. Binary classification tasks were conducted to identify the valence or arousal state of each participant based on a ten-fold cross-validation strategy. The results indicated that the application of DG methods consistently enhanced classification accuracy across datasets. In one dataset, TSception combined with VREx achieved the highest performance for both valence and arousal classifications. In the other dataset, TSception with VREx still yielded the highest valence classification accuracy, while TSception combined with GroupDRO showed the best arousal classification performance among the twelve models, slightly outperforming TSception with VREx. These findings underscore the potential of DG approaches to mitigate distributional shifts caused by intersubject and intersession variabilities to implement robust subject-independent EEG-based emotion recognition systems.},
}

@article {pmid40363359,
year = {2025},
author = {Deng, X and Huo, H and Ai, L and Xu, D and Li, C},
title = {A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {9},
pages = {},
doi = {10.3390/s25092922},
pmid = {40363359},
issn = {1424-8220},
support = {No. XTZW2024-KF02//Chongqing Key Laboratory of Germplasm Innovation 755 and Utilization of Native Plants under Grant/ ; },
mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Signal-To-Noise Ratio ; Movement/physiology ; Deep Learning ; Algorithms ; },
abstract = {Motor imagery (MI) is a crucial research field within the brain-computer interface (BCI) domain. It enables patients with muscle or neural damage to control external devices and achieve movement functions by simply imagining bodily motions. Despite the significant clinical and application value of MI-BCI technology, accurately decoding high-dimensional and low signal-to-noise ratio (SNR) electroencephalography (EEG) signals remains challenging. Moreover, traditional deep learning approaches exhibit limitations in processing EEG signals, particularly in capturing the intrinsic correlations between electrode channels and long-distance temporal dependencies. To address these challenges, this research introduces a novel end-to-end decoding network that integrates convolutional neural networks (CNNs) and a Swin Transformer, aiming at enhancing the classification accuracy of the MI paradigm in EEG signals. This approach transforms EEG signals into a three-dimensional data structure, utilizing one-dimensional convolutions along the temporal dimension and two-dimensional convolutions across the EEG electrode distribution for initial spatio-temporal feature extraction, followed by deep feature exploration using a 3D Swin Transformer module. Experimental results show that on the BCI Competition IV-2a dataset, the proposed method achieves 83.99% classification accuracy, which is significantly better than the existing deep learning methods. This finding underscores the efficacy of combining a CNN and Swin Transformer in a 3D data space for processing high-dimensional, low-SNR EEG signals, offering a new perspective for the future development of MI-BCI. Future research could further explore the applicability of this method across various BCI tasks and its potential clinical implementations.},
}

*Electroencephalography/methods
Humans
Brain-Computer Interfaces
*Neural Networks, Computer
Signal Processing, Computer-Assisted
*Imagination/physiology
Signal-To-Noise Ratio
Movement/physiology
Deep Learning
Algorithms

@article {pmid40363182,
year = {2025},
author = {Hougaard, BI and Knoche, H and Kristensen, MS and Jochumsen, M},
title = {Experience of Virtual Help in a Simulated BCI Stroke Rehabilitation Serious Game and How to Measure It.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {9},
pages = {},
doi = {10.3390/s25092742},
pmid = {40363182},
issn = {1424-8220},
support = {22357//VELUX FONDEN/ ; },
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Video Games ; Aged ; Adult ; *Stroke/physiopathology ; User-Computer Interface ; },
abstract = {Designers of digital rehabilitation experiences can accommodate error-prone input devices like brain-computer interfaces (BCIs) by incorporating virtual help mechanisms to adjust the difficulty, but it is unclear on what grounds users are willing to accept such help. To study users' experience of virtual help mechanisms, we used three help mechanisms in a blink-controlled game simulating a BCI-based stroke rehabilitation exercise. A mixed-method, simulated BCI study was used to evaluate game help by 19 stroke patients who rated their frustration and perceived control when experiencing moderately high input recognition. None of the help mechanisms affected ratings of frustration, which were low throughout the study, but two mechanisms affected patients' perceived control ratings positively and negatively. Patient ratings were best explained by the amount of positive feedback, including game help, which increased perceived control ratings by 8% and decreased frustration ratings by 3%. The qualitative analysis revealed appeal, interference, self-blame, and prominence as deciding experiential factors of help, but it was unclear how they affected frustration and perceived control ratings. Building upon the results, we redesigned and tested self-reported measures of help quantity, help appeal, irritation, and pacing with game-savvy adults in a follow-up study using the same game. Help quantity appeared larger when game help shielded players from negative feedback, but this did not necessarily appeal to them. Future studies should validate or control for the constructs of perceived help quantity and appeal.},
}

Humans
*Stroke Rehabilitation/methods
*Brain-Computer Interfaces
Male
Female
Middle Aged
*Video Games
Aged
Adult
*Stroke/physiopathology
User-Computer Interface

@article {pmid40361409,
year = {2025},
author = {Marín-Liébana, S and Llor, P and Serrano-García, L and Fernández-Murga, ML and Comes-Raga, A and Torregrosa, D and Pérez-García, JM and Cortés, J and Llombart-Cussac, A},
title = {Gene Expression Signatures for Guiding Initial Therapy in ER+/HER2- Early Breast Cancer.},
journal = {Cancers},
volume = {17},
number = {9},
pages = {},
doi = {10.3390/cancers17091482},
pmid = {40361409},
issn = {2072-6694},
abstract = {In triple-negative (TNBC) and human epidermal growth factor receptor 2-positive (HER2+) breast cancer patients, neoadjuvant systemic therapy is the standard recommendation for tumors larger than 2 cm. Monitoring the response to primary systemic therapy allows for the assessment of treatment effects, the need for breast-conserving surgery (BCS), and the achievement of pathological complete responses (pCRs). In estrogen receptor-positive/HER2-negative (ER+/HER2-) breast cancer, the benefit of neoadjuvant strategies is controversial, as they have shown lower tumor downstaging and pCR rates compared to other breast cancers. In recent decades, several gene expression assays have been developed to tailor adjuvant treatments in ER+/HER2- early breast cancer (EBC) to identify the patients that will benefit the most from adjuvant chemotherapy (CT) and those at low risk who could be spared from undergoing CT. It is still a challenge to identify patients who will benefit from neoadjuvant systemic treatment (CT or endocrine therapy (ET)). Here, we review the published data on the most common gene expression signatures (MammaPrint (MP), BluePrint (BP), Oncotype Dx, PAM50, the Breast Cancer Index (BCI), and EndoPredict (EP)) and their ability to predict the response to neoadjuvant treatment, as well as the possibility of using them on core needle biopsies. Additionally, we review the changes in the gene expression signatures after neoadjuvant treatment, and the ongoing clinical trials related to the utility of gene expression signatures in the neoadjuvant setting.},
}

@article {pmid40360495,
year = {2025},
author = {Li, J and Mo, D and Hu, J and Wang, S and Gong, J and Huang, Y and Li, Z and Yuan, Z and Xu, M},
title = {PEDOT:PSS-based bioelectronics for brain monitoring and modulation.},
journal = {Microsystems & nanoengineering},
volume = {11},
number = {1},
pages = {87},
pmid = {40360495},
issn = {2055-7434},
support = {30802-110690303//Guangdong Science and Technology Department (Science and Technology Department, Guangdong Province)/ ; 2021ZD0204300//National Science Foundation of China | Major Research Plan/ ; MYRGGRG2023-00038-FHS//Universidade de Macau (University of Macau)/ ; 28709-312200502501//Beijing Normal University (BNU)/ ; },
abstract = {The growing demand for advanced neural interfaces that enable precise brain monitoring and modulation has catalyzed significant research into flexible, biocompatible, and highly conductive materials. PEDOT:PSS-based bioelectronic materials exhibit high conductivity, mechanical flexibility, and biocompatibility, making them particularly suitable for integration into neural devices for brain science research. These materials facilitate high-resolution neural activity monitoring and provide precise electrical stimulation across diverse modalities. This review comprehensively examines recent advances in the development of PEDOT:PSS-based bioelectrodes for brain monitoring and modulation, with a focus on strategies to enhance their conductivity, biocompatibility, and long-term stability. Furthermore, it highlights the integration of multifunctional neural interfaces that enable synchronous stimulation-recording architectures, hybrid electro-optical stimulation modalities, and multimodal brain activity monitoring. These integrations enable fundamentally advancing the precision and clinical translatability of brain-computer interfaces. By addressing critical challenges related to efficacy, integration, safety, and clinical translation, this review identifies key opportunities for advancing next-generation neural devices. The insights presented are vital for guiding future research directions in the field and fostering the development of cutting-edge bioelectronic technologies for neuroscience and clinical applications.},
}

@article {pmid40360243,
year = {2025},
author = {Schurzig, D and Iseke, R and Maier, H and Prenzler, NK and Lenarz, T and Ghoncheh, M},
title = {Clinical Evidence on the Influence of Implant Position onto Maximum Output with the Bonebridge Bone Conduction Implant.},
journal = {Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {},
number = {},
pages = {},
doi = {10.1097/MAO.0000000000004533},
pmid = {40360243},
issn = {1537-4505},
abstract = {HYPOTHESIS: In bone conduction implantation, the position of the implant influences the audiological benefit of the patient.

BACKGROUND: One way of treating hearing loss is the implantation of bone conduction implants (BCIs), which effectively transmit vibrations through the skull bone to the cochlea given that the implant transducer is securely fixated. Laboratory research on the efficacy of bone conduction sound transmission found that a closer proximity of the transducer to the ipsilateral cochlea yields significantly higher cochlear promontory vibrations and hence, higher stimulation efficacy. Up to now, this finding has not been reproduced using clinical data such as the functional or effective gain.

METHODS: The present, retrospective study was conducted on a cohort of 28 BCI patients to correlate the implantation site of the BC transducer, derived from clinical postoperative imaging and defined in a standardized coordinate system, with maximum output values that are exclusively based on a novel calculation method only employing clinical audiological data.

RESULTS: It could be shown that the efficacy of BCI stimulation is in fact correlated with the transducer distance to the cochlea, and that this correlation is frequency dependent. Furthermore, the longitudinal distance of the transducer and the ipsilateral external auditory canal is negatively correlated with the maximal output while the sagittal distance is not.

CONCLUSION: The present study is hence the first one to clinically demonstrate the significance of BCI placement for maximizing patient benefit, which should be considered during the preoperative planning of bone conduction implantation.},
}

@article {pmid40359554,
year = {2025},
author = {Liu, MY and Fang, MZ and Zhang, BH and Dang, CX and Zhang, YS and Wu, L and Liu, B and Li, Z},
title = {Bibliometric analysis of brain-computer interface research in spinal cord injury: current landscape and future directions.},
journal = {International journal of surgery (London, England)},
volume = {},
number = {},
pages = {},
doi = {10.1097/JS9.0000000000002475},
pmid = {40359554},
issn = {1743-9159},
}

@article {pmid40358727,
year = {2025},
author = {Aamir, A and Siddiqui, M},
title = {Integration of brain-computer interfaces with sacral nerve stimulation: a vision for closed-loop, volitional control of bladder function in neurogenic patients through real-time cortical signal modulation and peripheral neuro-stimulation.},
journal = {World journal of urology},
volume = {43},
number = {1},
pages = {301},
pmid = {40358727},
issn = {1433-8726},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy/methods ; *Urinary Bladder, Neurogenic/therapy/physiopathology ; *Lumbosacral Plexus ; },
abstract = {Sacral nerve stimulation (SNS) and brain-computer interfaces (BCI) are emerging neuromodulation therapies that offer innovative solutions for chronic neurological disorders. SNS, primarily used in the management of conditions such as urinary incontinence and chronic pelvic pain, demonstrates significant therapeutic potential. In contrast, BCIs are rapidly advancing in their ability to restore lost motor functions and improve the quality of life of patients with severe neurological impairments, such as spinal cord injury and stroke. The integration of SNS and BCI technologies presents a promising avenue for enhancing neuromodulation outcomes by leveraging the potential of both systems. This article explores the combined operation of SNS and BCI, addressing current challenges, future directions, and the potential for these combined therapies to revolutionise the field of functional neuromodulation.},
}

Humans
*Brain-Computer Interfaces
*Electric Stimulation Therapy/methods
*Urinary Bladder, Neurogenic/therapy/physiopathology
*Lumbosacral Plexus

@article {pmid40358723,
year = {2025},
author = {Bénard, A and Maliia, DM and Yochum, M and Köksal-Ersöz, E and Houvenaghel, JF and Wendling, F and Sauleau, P and Benquet, P},
title = {Realistic Subject-Specific Simulation of Resting State Scalp EEG Based on Physiological Model.},
journal = {Brain topography},
volume = {38},
number = {4},
pages = {43},
pmid = {40358723},
issn = {1573-6792},
support = {855109//HORIZON EUROPE Reforming and enhancing the European Research and Innovation system/ ; 855109//HORIZON EUROPE Reforming and enhancing the European Research and Innovation system/ ; 855109//HORIZON EUROPE Reforming and enhancing the European Research and Innovation system/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; *Models, Neurological ; Computer Simulation ; *Rest/physiology ; Adult ; Scalp/physiology ; Male ; *Brain Waves/physiology ; Female ; },
abstract = {Electroencephalography (EEG) recordings are widely used in neuroscience to identify healthy individual brain rhythms and to detect alterations associated with various brain diseases. However, understanding the cellular origins of scalp EEG signals and their spatiotemporal changes during the resting state (RS) in humans remains challenging, as cellular-level recordings are typically restricted to animal models. The objective of this study was to simulate individual-specific spatiotemporal features of RS EEG and measure the degree of similarity between real and simulated EEG. Using a physiologically grounded whole-brain computational model (based on known neuronal subtypes and their structural and functional connectivity) that simulates interregional cortical circuitry activity, realistic individual EEG recordings during RS of three healthy subjects were created. The model included interconnected neural mass modules simulating activities of different neuronal subtypes, including pyramidal cells and four types of GABAergic interneurons. High-definition EEG and source localization were used to delineate the cortical extent of alpha and beta-gamma rhythms. To evaluate the realism of the simulated EEG, we developed a similarity index based on cross-correlation analysis in the frequency domain across various bipolar channels respecting standard longitudinal montage. Alpha oscillations were produced by strengthening the somatostatin-pyramidal loop in posterior regions, while beta-gamma oscillations were generated by increasing the excitability of parvalbumin-interneurons on pyramidal neurons in anterior regions. The generation of realistic individual RS EEG rhythms represents a significant advance for research fields requiring data augmentation, including brain-computer interfaces and artificial intelligence training.},
}

Humans
*Electroencephalography/methods
*Brain/physiology
*Models, Neurological
Computer Simulation
*Rest/physiology
Adult
Scalp/physiology
Male
*Brain Waves/physiology
Female

@article {pmid40356632,
year = {2025},
author = {Liao, XY and Jiang, YE and Xu, RJ and Qian, TT and Liu, SL and Che, Y},
title = {A bibliometric analysis of electroencephalogram research in stroke: current trends and future directions.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1539736},
pmid = {40356632},
issn = {1664-2295},
abstract = {BACKGROUND: Electroencephalography (EEG) has become an indispensable tool in stroke research for real-time monitoring of neural activity, prognosis prediction, and rehabilitation support. In recent decades, EEG applications in stroke research have expanded, particularly in areas like brain-computer interfaces (BCI) and neurofeedback for motor recovery. However, a comprehensive analysis of research trends in this domain is currently unavailable.

METHODS: The study collected data from the Web of Science Core Collection database, selecting publications related to stroke and EEG from 2005 to 2024. Visual analysis tools such as VOSviewer and CiteSpace were utilized to build knowledge maps of the research field, analyzing the distribution of publications, authors, institutions, journals, and collaboration networks. Additionally, co-occurrence, clustering, and burst detection of keywords were analyzed in detail.

RESULTS: A total of 2,931 publications were identified, indicating a consistent increase in EEG research in stroke, with significant growth post-2017. The United States, China, and Germany emerged as the leading contributors, with high collaboration networks among Western institutions. Key research areas included signal processing advancements, EEG applications in seizure risk and consciousness disorder assessment, and EEG-driven rehabilitation techniques. Notably, recent studies have focused on integrating EEG with machine learning and multimodal data for more precise functional evaluations.

CONCLUSION: The findings reveal that EEG has evolved from a diagnostic tool to a therapeutic support platform in the context of stroke care. The advent of deep learning and multimodal integration has positioned EEG for expanded applications in personalized rehabilitation. It is recommended that future studies prioritize interdisciplinary collaboration and standardized EEG methodologies in order to facilitate clinical adoption and enhance translational potential in stroke management.},
}

@article {pmid40356046,
year = {2025},
author = {Chen, S and Hu, J and Zhang, D and Li, Z and Zheng, Z and Gui, S and He, N},
title = {Preparation and hemostatic evaluation of carboxymethyl chitosan/gelatin/clinodiside a composite sponges.},
journal = {Journal of biomaterials science. Polymer edition},
volume = {},
number = {},
pages = {1-21},
doi = {10.1080/09205063.2025.2499285},
pmid = {40356046},
issn = {1568-5624},
abstract = {In trauma resuscitation, rapid hemostasis is a top priority for rescuing patients from the risk of hemorrhagic shock and infection. Traditional hemostatic materials are not effective for hemostasis and have some limitations. We added "Duanxue Liu" saponin A (Clinodiside A) to a hemostatic sponge based on carboxymethyl chitosan (CMCS) and gelatin to improve its hemostatic effect. Clinodiside A has hemostatic, anti-inflammatory and antibacterial effects, and its preparation into a sponge can help to improve the coagulation ability, prolong the action time of the drug, increase the bioavailability and improve the stability of the drug. The results showed that the prepared hemostatic sponge had a honeycomb porous structure, strong shape recovery ability, good water absorption and porosity, low hemolysis rate and no obvious cytotoxicity. The results of in vitro coagulation test showed that the coagulation time of GOC, GOC-1, GOC-2 and GOC-3 sponges was shorter than that of the control group, and the BCI index was much lower than that of the commercially available sponges. In the tail-breaking experiment of SD rats, GOC-3 showed the lowest blood loss of 0.2549 g and the hemostasis time of 55 s. In the experiment of rabbit ear artery, GOC-3 showed the lowest blood loss of 98.75 mg and the hemostasis time of 95 s. This indicates that the Clinique A hemostatic sponges have highly efficient hemostatic properties. Therefore, the prepared CMCS/Gel/Clinodiside A sponge has a good application prospect as a hemostatic dressing.},
}

@article {pmid40355527,
year = {2025},
author = {Wu, Y and Liu, Y and Yang, Y and Yao, MS and Yang, W and Shi, X and Yang, L and Li, D and Liu, Y and Yin, S and Lei, C and Zhang, M and Gee, JC and Yang, X and Wei, W and Gu, S},
title = {Author Correction: A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {4381},
doi = {10.1038/s41467-025-59792-1},
pmid = {40355527},
issn = {2041-1723},
}

@article {pmid40355001,
year = {2025},
author = {Ognard, J and El Hajj, G and Verma, O and Ghozy, S and Kadirvel, R and Kallmes, DF and Brinjikji, W},
title = {Advances in endovascular brain computer interface: Systematic review and future implications.},
journal = {Journal of neuroscience methods},
volume = {420},
number = {},
pages = {110471},
doi = {10.1016/j.jneumeth.2025.110471},
pmid = {40355001},
issn = {1872-678X},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) translate neural activity into real-world commands. While traditional invasive BCIs necessitate craniotomy, endovascular BCIs offer a minimally invasive alternative using the venous system for electrode placement.

NEW METHOD: This systematic review evaluates the technical feasibility, safety, and clinical outcomes of endovascular BCIs, discussing their future implications. A systematic review was conducted per PRISMA guidelines. The search spanned PubMed, Web of Science, and Scopus databases using keywords related to neural interfaces and endovascular approaches. Studies were included if they reported on endovascular BCIs in preclinical or clinical settings. Dual independent screening and extraction focused on electrode material, recording capabilities, safety parameters, and clinical efficacy.

RESULTS: From 1385 initial publications, 26 met the inclusion criteria. Seventeen studies investigated the Stentrode device. Among the 24 preclinical studies, 16 used ovine or rodent models, and 9 addressed engineering or simulation aspects. Two clinical studies reported six ALS patients successfully using an endovascular BCI for digital communication. Preclinical data established the endovascular ovine model, demonstrating stable neural recordings and vascular changes with long-term implantation. Key challenges include thrombosis risk, long-term electrode stability, and anatomical variability.

Endovascular BCI reduced invasiveness, improved safety profiles, with comparable neural recording fidelity to invasive methods, and promising preliminary clinical outcomes in severely paralyzed patients.

CONCLUSIONS: Early results are promising, but clinical data remain scarce. Further research is needed to optimize signal processing, enhance electrode biocompatibility, and refine endovascular procedures for broader clinical applications.},
}

@article {pmid40354812,
year = {2025},
author = {Poli, R and Mercimek, ACC and Cinel, C},
title = {Novel Sequential BCI Speller based on ERPs and Event-Related Slow Cortical Potentials.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add772},
pmid = {40354812},
issn = {1741-2552},
abstract = {One of the most effective Brain-Computer Interfaces (BCI) spellers, Donchin and Farwell's matrix speller, uses visual stimulus presentation and the oddball effect, eliciting P300 event-related potentials to rare and randomly presented stimuli of interest. Although proposed almost 4 decades ago, most BCI spellers still rely on this principle and the original matrix speller design although some of the issues that affect oddball spellers have progressively been addressed over the years with significant, but very incremental, performance improvements. Farwell and Donchin seminal paper suggested the future possibility of abandoning the oddball paradigm, for a regular/periodic presentation pattern which they predicted might produce a Contingent Negative Variation (CNV) thus improve speller performance. However, this has never been investigated. Building on our past research on a BCI for cursor control which adopted a periodic stimulation protocol, here we explore whether a periodic presentation pattern could be a viable alternative to the oddball paradigm in a BCI speller. Approach. We tested the periodic presentation principle in a BCI speller where 36 letters are organised around a circle and are highlighted sequentially, and compared it to the original matrix speller at two stimulus presentation rates. Main Results. Our periodic speller produces not only clear P300s, but also equally clear CNVs, as postulated by Farwell and Donchin, as well as other Slow Cortical Potentials (SCPs). At the higher stimulation rate, this leads to significantly higher AUC, classification accuracy, ITR and utility w.r.t. Donchin's speller. Significance. Our findings suggest that periodic stimulation can not only produce clear P300s but also a variety of event-related SCPs, leading to significant performance improvements over Donchin's paradigm. This work opens new avenues for BCI spelling where ERPs are combined with naturally-triggered (rather than trained) SCPs, that will hopefully result in more efficient communication systems for individuals with severe motor impairments.},
}

@article {pmid40354807,
year = {2025},
author = {Ladouce, S and Torre-Tresols, JJ and Le Goff, K and Dehais, F},
title = {EEG-based assessment of long-term vigilance and lapses of attention using a user-centered frequency-tagging approach.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add771},
pmid = {40354807},
issn = {1741-2552},
abstract = {Sustaining vigilance over extended periods is crucial for many critical operations but remains challenging due to the cognitive resources required. Fatigue and other factors contribute to fluctuations in vigilance, causing attentional focus to drift from task-relevant information. Such lapses of attention, common in prolonged tasks, lead to decreased performance and missed critical information, with potentially serious consequences. Identifying physiological markers that predict inattention is key to developing preventive strategies. Approach: Previous research has established electroencephalography (EEG) responses to periodic visual stimuli, known as steady-state visual evoked potentials (SSVEP), as sensitive markers of attention. In this study, we evaluated a minimally intrusive SSVEP-based approach for tracking vigilance in healthy participants (N = 16) during two sessions of a 45-minute sustained visual attention task (Mackworth's clock task). A 14 Hz frequency-tagging flicker was either superimposed on the task or absent. Main results: Results revealed that SSVEP responses were lower prior to lapses of attention, while other spectral EEG markers, such as frontal theta and parietal alpha activity, did not reliably distinguish between detected and missed attention probes. Importantly, the flicker did not affect task performance or participant experience. Significance: This non-intrusive frequency-tagging method provides a continuous measure of vigilance, effectively detecting attention lapses in prolonged tasks. It holds promise for integration into passive brain-computer interfaces, offering a practical solution for real-time vigilance monitoring in high-stakes settings like air traffic control or driving. .},
}

@article {pmid40353311,
year = {2025},
author = {Shah, AM},
title = {Unlocking Naturalistic Speech With Brain-Computer Interface.},
journal = {Artificial organs},
volume = {},
number = {},
pages = {},
doi = {10.1111/aor.15021},
pmid = {40353311},
issn = {1525-1594},
abstract = {Novel speech brain-computer interface poses the ability to decode detected neural signals in nearly real time. This decreases brain-to-voice latency and has the opportunity to restore naturalistic communication. Trial Registration: ClinicalTrials.gov: NCT03698149.},
}

@article {pmid40352906,
year = {2025},
author = {Besharat, A and Samadzadehaghdam, N and Ghadiri, T},
title = {A novel hybrid method based on task-related component and canonical correlation analyses (H-TRCCA) for enhancing SSVEP recognition.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1544452},
doi = {10.3389/fnins.2025.1544452},
pmid = {40352906},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) rely on the brain's response to visual stimuli. However, accurately recognizing target frequencies using training-based methods remains challenging due to the time-consuming calibration sessions required by subject-specific training methods.

METHOD: To address this limitation, this study proposes a novel hybrid method called Hybrid task-related component and canonical correlation analysis (H-TRCCA). In the training phase, four spatial filters are derived using canonical correlation analysis (CCA) to maximize the correlation between the training data and reference signals. Additionally, a spatial filter is also computed using task-related component analysis (TRCA). In the test phase, correlation coefficients obtained from the CCA method are clustered using the k-means++ clustering algorithm. The cluster with the highest average correlation identifies the candidate stimuli. Finally, for each candidate, the correlation values are summed and combined with the TRCA-based correlation coefficients.

RESULTS: The H-TRCCA algorithm was validated using two publicly available benchmark datasets. Experimental results using only two training trials per frequency with 1s data length showed that H-TRCCA achieved average accuracies of 91.44% for Dataset I and 80.46% for Dataset II. Additionally, it achieved maximum average information transfer rates of 188.36 bits/min and 139.96 bits/min for Dataset I and II, respectively.

DISCUSSION: Remarkably H-TRCCA achieves comparable performance to other methods that require five trials, utilizing only two or three training trials. The proposed H-TRCCA method outperforms state-of-the-art techniques, showing superior performance and robustness with limited calibration data.},
}

@article {pmid40352453,
year = {2025},
author = {Li, Y and Mei, Z and Liu, Z and Li, J and Sun, G and Ong, MEH and Chen, J and Fan, H and Cao, C},
title = {Cardiometabolic multimorbidity and the risk of sudden cardiac death among geriatric community dwellers using longitudinal EHR-derived data.},
journal = {Frontiers in endocrinology},
volume = {16},
number = {},
pages = {1515495},
doi = {10.3389/fendo.2025.1515495},
pmid = {40352453},
issn = {1664-2392},
mesh = {Humans ; Aged ; Male ; Female ; *Multimorbidity ; *Electronic Health Records/statistics & numerical data ; Retrospective Studies ; Aged, 80 and over ; China/epidemiology ; *Death, Sudden, Cardiac/epidemiology/etiology ; *Independent Living/statistics & numerical data ; Risk Factors ; Longitudinal Studies ; *Cardiovascular Diseases/epidemiology ; Prevalence ; *Metabolic Diseases/epidemiology ; },
abstract = {BACKGROUND: Cardiometabolic multimorbidity (CMM) has increased globally in recent years, especially among geriatric community dwellers. However, it is currently unclear how SCD risk is impacted by CMM in older adults. This study aimed to examine the associations between CMM and SCD among geriatric community dwellers in a province of China.

METHODS: This study was a retrospective, population-based cohort design based on electronic health records (EHRs) of geriatric community dwellers (≥65 years old) in four towns of Tianjin, China. 55,130 older adults were included in our study. Older adults were categorized into different CMM patterns according to the cardiometabolic disease (CMD) status at baseline. The count of CMDs was also entered as a continuous variable to examine the potential additive effect of CMM on SCD. Cox proportional hazard models were used to evaluate associations between CMM and SCD. The results are expressed as hazard ratios (HRs) and 95% confidence intervals (CIs).

RESULTS: The prevalence of CMM was approximately 25.3% in geriatric community dwellers. Among participants with CMM, hypertension and diabetes was the most prevalent combination (9,379, 17.0%). The highest crude mortality rates for SCD were 7.5 (2.9, 19.1) per 1000 person-years in older adults with hypertension, coronary heart disease, diabetes and stroke (HR, 4.496; 95% CI, 1.696, 11.917), followed by those with hypertension, coronary heart disease, and stroke (HR, 3.290; 95% CI, 1.056, 10.255). The risks of SCD were significantly increased with increasing numbers of CMDs (HR, 1.787; 95% CI, 1.606, 1.987). The demographic, risk factors, serum measures and ECG-adjusted HR for SCD was 1.488 (1.327, 1.668) for geriatric community dwellers with an increasing number of CMDs.

CONCLUSION: The risk of SCD varied by the pattern of CMM, and increased with increasing number of CMM among geriatric community dwellers.},
}

Humans
Aged
Male
Female
*Multimorbidity
*Electronic Health Records/statistics & numerical data
Retrospective Studies
Aged, 80 and over
China/epidemiology
*Death, Sudden, Cardiac/epidemiology/etiology
*Independent Living/statistics & numerical data
Risk Factors
Longitudinal Studies
*Cardiovascular Diseases/epidemiology
Prevalence
*Metabolic Diseases/epidemiology

@article {pmid40351848,
year = {2025},
author = {Jin, X and Yuan, Y and Chang, C and Wu, X and Tan, X and Liu, Z},
title = {Telemedicine in China: Effective indicators of telemedicine platforms for promoting health and well-being among healthcare consumers.},
journal = {Digital health},
volume = {11},
number = {},
pages = {20552076251341163},
pmid = {40351848},
issn = {2055-2076},
abstract = {OBJECTIVE: Telemedicine platforms played a crucial role during the COVID-19 pandemic, alleviating issues related to the shortage and unequal distribution of healthcare resources. The purpose of this study is to identify key factors affecting the service quality of telemedicine platforms in China, with the dual objectives of advancing patient wellbeing and informing evidence-based service innovations for industry stakeholders.

METHODS: To quantitatively assess the impact of these key factors on health and wellbeing from the perspective of healthcare consumers, a total of 25,499 valid online reviews were collected from telemedicine platforms. To establish a service quality evaluation framework, this study proposes a novel approach that combines the Servqual quality assessment model with a CNN-BiLSTM deep learning model enhanced by an attention mechanism.

RESULTS: Analysis of the full sample shows that healthcare consumers are most concerned about the quality of services provided by telemedicine platforms, with the most important being the professional competence of doctors, a critical factor for promoting consumer health and wellbeing. The proposed hybrid deep learning approach demonstrates superior performance in sentiment classification accuracy, outperforming conventional methods by 11.11 percentage points. This methodological innovation enables more precise identification of consumer sentiment patterns across service dimensions.

CONCLUSION: The novel quality assessment framework introduced here provides actionable insights for advancing telemedicine platforms, driving progress toward precision healthcare and consumer-centric wellbeing. Furthermore, it enables healthcare consumers to select telemedicine services aligned with their personalized needs.},
}

@article {pmid40351570,
year = {2025},
author = {Sercek, I and Sampathila, N and Tasci, I and Ekmekyapar, T and Tasci, B and Barua, PD and Baygin, M and Dogan, S and Tuncer, T and Tan, RS and Acharya, UR},
title = {A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {71},
pmid = {40351570},
issn = {1871-4080},
abstract = {Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop a computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: We retrospectively analyzed the EEG records of 134 AD and 113 non-AD patients. To generate multilevel features, a multilevel discrete wavelet transform was used to decompose the input EEG-signals. We devised a novel quantum-inspired EEG-signal feature extraction function based on 7-distinct different subgraphs of the Goldner-Harary pattern (GHPat), and selectively assigned a specific subgraph, using a forward-forward distance-based fitness function, to each input EEG signal block for textural feature extraction. We extracted statistical features using standard statistical moments, which we then merged with the extracted textural features. Other model components were iterative neighborhood component analysis feature selection, standard shallow k-nearest neighbors, as well as iterative majority voting and greedy algorithm to generate additional voted prediction vectors and select the best overall model results. With leave-one-subject-out cross-validation (LOSO CV), our model attained 88.17% accuracy. Accuracy results stratified by channel lead placement and brain regions suggested P4 and the parietal region to be the most impactful. Comparison with existing methods: The proposed model outperforms existing methods by achieving higher accuracy with a computationally efficient quantum-inspired approach, ensuring robustness and generalizability. Cortex maps were generated that allowed visual correlation of channel-wise results with various brain regions, enhancing model explainability.},
}

@article {pmid40350042,
year = {2025},
author = {Xie, C and Wang, L and Yang, J and Guo, J},
title = {A subject transfer neural network fuses Generator and Euclidean Alignment for EEG-based motor imagery classification.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110483},
doi = {10.1016/j.jneumeth.2025.110483},
pmid = {40350042},
issn = {1872-678X},
abstract = {BACKGROUND: Brain-computer interface (BCI) facilitates the connection between human brain and computer, enabling individuals to control external devices indirectly through cognitive processes. Although it has great development prospects, the significant difference in EEG signals among individuals hinders users from further utilizing the BCI system.

NEW METHOD: Addressing this difference and improving BCI classification accuracy remain key challenges. In this paper, we propose a transfer learning model based on deep learning to transfer the data distribution from the source domain to the target domain, named a subject transfer neural network combining the Generator with Euclidean alignment (ST-GENN). It consists of three parts: 1) Align the original EEG signals in the Euclidean space; 2) Send the aligned data to the Generator to obtain the transferred features; 3) Utilize the Convolution-attention-temporal (CAT) classifier to classify the transferred features.

RESULTS: The model is validated on BCI competition IV 2a, BCI competition IV 2b and SHU datasets to evaluate its classification performance, and the results are 82.85%, 86.28% and 67.2% for the three datasets, respectively.

The results have been shown to be robust to subject variability, with the average accuracy of the proposed method outperforming baseline algorithms by ranging from 2.03% to 15.43% on the 2a dataset, from 0.86% to 10.16% on the 2b dataset and from 3.3% to 17.9% on the SHU dataset.

The advantage of our model lies in its ability to effectively transfer the experience and knowledge of the source domain data to the target domain, thus bridging the gap between them. Our method can improve the practicability of MI-BCI systems.},
}

@article {pmid40349743,
year = {2025},
author = {Dong, T and Lee, HH and Zang, H and Lee, H and Tian, Q and Wan, L and Fan, Q and Huang, SY},
title = {In Vivo Cortical Microstructure Mapping Using High-Gradient Diffusion MRI Accounting for Intercompartmental Water Exchange Effects.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121258},
doi = {10.1016/j.neuroimage.2025.121258},
pmid = {40349743},
issn = {1095-9572},
abstract = {In recent years, mapping tissue microstructure in the cortex using high gradient diffusion MRI has received growing attention. The Soma And Neurite Density Imaging (SANDI) explicitly models the soma compartment in the cortex assuming impermeable membranes. As such, it does not account for diffusion time dependence due to water exchange in the estimated microstructural properties, as neurites in gray matter are much less myelinated than in white matter. In this work, we performed a systematic evaluation of an extended SANDI model for in vivo human cortical microstructural mapping that accounts for water exchange effects between the neurite and extracellular compartments using the anisotropic Kärger model. We refer to this model as in vivo SANDIX, adapting the nomenclature from previous publications. As in the original SANDI model, the soma compartment is modeled as an impermeable sphere due to the much smaller surface-to-volume ratio compared to the neurite compartment. A Monte Carlo simulation study was performed to examine the sensitivity of the in vivo SANDIX model to sphere radii, compartment fractions, and water exchange times. The simulation results indicate that the proposed in vivo SANDIX framework can account for the water exchange effect and provide measures of intra-soma and intra-neurite signal fractions without spurious time-dependence in estimated parameters, whereas the measured water exchange times need to be interpreted with caution. The model was then applied to in vivo diffusion MRI data acquired in 13 healthy adults on the 3-Tesla Connectome MRI scanner equipped with 300 mT/m gradients. The in vivo results exhibited patterns that were consistent with corresponding anatomical characteristics in both cortex and white matter. In particular, the estimated water exchange times in gray and white matter were distinct and differentiated between the two tissue types. Our results show the SANDIX approach applied to high-gradient diffusion MRI data achieves cortical microstructure mapping of the in vivo human brain with the evaluation of water exchange effects. This approach potentially provides a more appropriate description of in vivo cortical microstructure for improving data interpretation in future neurobiological studies.},
}

@article {pmid40348851,
year = {2025},
author = {Sun, G and Yu, C and Cai, R and Li, M and Fan, L and Sun, H and Lyu, C and Lin, Y and Gao, L and Wang, KH and Li, X},
title = {Neural representation of self-initiated locomotion in the secondary motor cortex of mice across different environmental contexts.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {725},
pmid = {40348851},
issn = {2399-3642},
support = {32371074//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32170991//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *Motor Cortex/physiology ; *Locomotion/physiology ; Mice ; Male ; *Neurons/physiology ; Mice, Inbred C57BL ; Environment ; },
abstract = {The secondary motor cortex (M2) plays an important role in the adaptive control of locomotor behaviors. However, it is unclear how M2 neurons encode the same type of locomotor control variables in different environmental contexts. Here we image the neuronal activity in M2 with a miniscope while mice are moving freely in each of three environments: a Y-maze, a running-wheel, and an open-field. These animals show distinct locomotor patterns in different environmental contexts. Surprisingly, a large population of M2 neurons are active before starting and after ceasing locomotion, while maintaining decreased neural activity during locomotion. Furthermore, the majority of these neurons are consistently engaged across various contexts, suggesting egocentric voluntary control functions. In contrast, the smaller populations of locomotion-activated M2 neurons are mostly context-specific, suggesting exocentric navigation functions. Thus, our results demonstrate that M2 neurons encode motor control variables for self-initiated locomotor behaviors in both context-dependent and context-independent manners.},
}

Animals
*Motor Cortex/physiology
*Locomotion/physiology
Mice
Male
*Neurons/physiology
Mice, Inbred C57BL
Environment

@article {pmid40347675,
year = {2025},
author = {Chen, L and Liu, Y and Wang, Z and Zhang, L and Cheng, S and Ming, D},
title = {Using non-invasive brain stimulation to modulate performance in visuomotor rotation adaptation: A scoping review.},
journal = {Cortex; a journal devoted to the study of the nervous system and behavior},
volume = {187},
number = {},
pages = {144-158},
doi = {10.1016/j.cortex.2025.04.010},
pmid = {40347675},
issn = {1973-8102},
abstract = {As research on the visuomotor rotation (VMR) adaptation expands its scope from behavioral science to encompass neuropsychological perspectives, an increasing number of studies have employed non-invasive brain stimulation (NIBS) techniques to explore the specific contributions of different neural structures to VMR adaptation. Despite early studies suggesting that cerebellar stimulation influenced the rate of adaptation and that stimulating primary motor cortex led to an enhanced retention of newly learned adaptation, subsequent studies could not always achieve consistent results. To probe this inconsistency, we systematically comb through past studies and extract numerous details, including paradigm designs, context settings, and modulation protocols in this scoping review. In summary, the paradigm design primarily serves two purposes: to dissociate implicit and explicit adaptation and to assess the retention of motor memory, whilst context settings such as apparatus, movement-related parameters and the information provided for subjects may complicate the modulated neuropsychological processes. We also conclude key NIBS parameters such as target regions and timing in stimulation protocols. Furthermore, we recognize the potential of neurophysiological biomarkers to support future VMR studies that incorporate NIBS and advocate for the use of several newly emerging NIBS techniques to enrich the field.},
}

@article {pmid40347660,
year = {2025},
author = {Chen, X and Chen, Y and McNamara, TP},
title = {Processing spatial cue conflict in navigation: Distance estimation.},
journal = {Cognitive psychology},
volume = {158},
number = {},
pages = {101734},
doi = {10.1016/j.cogpsych.2025.101734},
pmid = {40347660},
issn = {1095-5623},
abstract = {Spatial navigation involves the use of various cues. This study examined how cue conflict influences navigation by contrasting landmarks and optic flow. Participants estimated spatial distances under different levels of cue conflict: minimal conflict, large conflict, and large conflict with explicit awareness of landmark instability. Whereas increased cue conflict alone had little behavioral impact, adding explicit awareness reduced reliance on landmarks and impaired the precision of spatial localization based on them. To understand the underlying mechanisms, we tested two cognitive models: a Bayesian causal inference (BCI) model and a non-Bayesian sensory disparity model. The BCI model provided a better fit to the data, revealing two independent mechanisms for reduced landmark reliance: increased sensory noise for unstable landmarks and lower weighting of unstable landmarks when landmarks and optic flow were judged to originate from different causes. Surprisingly, increased cue conflict did not decrease the prior belief in a common cause, even when explicit awareness of landmark instability was imposed. Additionally, cue weighting in the same-cause judgment was determined by bottom-up sensory reliability, while in the different-cause judgment, it correlated with participants' subjective evaluation of cue quality, suggesting a top-down metacognitive influence. The BCI model further identified key factors contributing to suboptimal cue combination in minimal cue conflicts, including the prior belief in a common cause and prior knowledge of the target location. Together, these findings provide critical insights into how navigators resolve conflicting spatial cues and highlight the utility of the BCI model in dissecting cue interaction mechanisms in navigation.},
}

@article {pmid40342556,
year = {2025},
author = {Haseeb, M and Nadeem, R and Sultana, N and Naseer, N and Nazeer, H and Dehais, F},
title = {Monitoring pilots' mental workload in real flight conditions using multinomial logistic regression with a ridge estimator.},
journal = {Frontiers in robotics and AI},
volume = {12},
number = {},
pages = {1441801},
doi = {10.3389/frobt.2025.1441801},
pmid = {40342556},
issn = {2296-9144},
abstract = {Piloting an aircraft is a cognitive task that requires continuous verbal, visual, and auditory attentions (e.g., Air Traffic Control Communication). An increase or decrease in mental workload from a specific level can alter auditory and visual attention, resulting in pilot errors. The objective of this research is to monitor pilots' mental workload using advanced machine learning techniques to achieve improved accuracy compared to previous studies. Electroencephalogram (EEG) data were recorded from 22 pilots operating under visual flight rules (VFR) conditions using a six dry-electrode Enobio Neuroelectrics system, and the Riemannian artifact subspace reconstruction (rASR) filter was used for data cleaning. An information gain (IG) attribute evaluator was used to select 25 optimal features out of 72 power spectral and statistical extracted features. In this study, 15 classifiers were used for classification. Multinomial logistic regression with a ridge estimator was selected, achieving a significant mean accuracy of 84.6% on the dataset from 17 subjects. Data were initially collected from 22 subjects, but 5 were excluded due to data synchronization issues. This work has several limitations, such as the author did not counter balance the order of scenario, could not control all the variables such as wind conditions, and workload was not stationary in each leg of the flight pattern. This study demonstrates that multinomial logistic regression with a ridge estimator shows significant classification accuracy (p < 0.05) and effectively detects pilot mental workload in real flight scenarios.},
}

@article {pmid40341884,
year = {2025},
author = {Bertheau, MAK and Boetzel, C and Herrmann, CS},
title = {Event-related potentials reveal incongruent behavior of autonomous vehicles in the moral machine dilemma.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {16048},
pmid = {40341884},
issn = {2045-2322},
mesh = {Humans ; Male ; Female ; *Evoked Potentials/physiology ; *Morals ; Electroencephalography ; Adult ; Young Adult ; *Decision Making/physiology ; *Artificial Intelligence ; },
abstract = {We investigated event-related potentials (ERPs) in the context of autonomous vehicles (AVs)-specifically in ambiguous, morally challenging traffic situations. In our study, participants (n = 34) observed a putative artificial intelligence (AI) making decisions in a dilemma situation involving an AV, expanding on the Moral Machine (MM) experiment. Additionally to the original MM experiment, we incorporated electroencephalography recordings. We were able to replicate most of the behavioral findings of the original MM: In case of an unavoidable traffic accident, participants consistently favored sparing pedestrians over passengers, more characters over fewer characters, and humans over pets. Beyond that, in the ERP we observed an increased P3 (322-422 ms), and late positive potential (LPP) (500-900 MS) amplitude in fronto-central regions when the putative AI's decision on a moral dilemma was incongruent to the participants' decision. As P3, and LPP are associated with the processing of stimulus significance, our findings suggest that these ERP components could potentially be used to identify critical, or unacceptable situations during human-AI interactions involving moral decision-making. This might be useful in brain computer interfaces research when, classifying single-trial ERP components, to dynamically adopt an AV's behavior.},
}

Humans
Male
Female
*Evoked Potentials/physiology
*Morals
Electroencephalography
Adult
Young Adult
*Decision Making/physiology
*Artificial Intelligence

@article {pmid40341243,
year = {2025},
author = {Lore, S and Poganik, JR and Atala, A and Church, G and Gladyshev, VN and Scheibye-Knudsen, M and Verdin, E},
title = {Replacement as an aging intervention.},
journal = {Nature aging},
volume = {},
number = {},
pages = {},
pmid = {40341243},
issn = {2662-8465},
support = {1U01AI180158-01//U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)/ ; 1U01AI180158-01//U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)/ ; },
abstract = {Substantial progress in aging research continues to deepen our understanding of the fundamental mechanisms of aging, yet there is a lack of interventions conclusively shown to attenuate the processes of aging in humans. By contrast, replacement interventions such as joint replacements, pacemaker devices and transplant therapies have a long history of restoring function in injury or disease contexts. Here, we consider biological and synthetic replacement-based strategies as aging interventions. We discuss innovations in tissue engineering, such as the use of scaffolds or bioprinting to generate functional tissues, methods for enhancing donor-recipient compatibility through genetic engineering and recent progress in both cell therapies and xenotransplantation strategies. We explore synthetic approaches including prostheses, external devices and brain-machine interfaces. Additionally, we evaluate the evidence from heterochronic parabiosis experiments in mice and donor-recipient age-mismatched transplants to consider whether systemic benefits could result from personalized replacement approaches. Finally, we outline key challenges and future directions required to advance replacement therapies as viable, scalable and ethical interventions for aging.},
}

@article {pmid40340020,
year = {2025},
author = {Yu, X and Jian, Z and Dang, L and Zhang, X and He, P and Xiong, X and Feng, Y and Rehman, AU},
title = {Chemogenetic modulation in stroke recovery: A promising stroke therapy Approach.},
journal = {Brain stimulation},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.brs.2025.05.003},
pmid = {40340020},
issn = {1876-4754},
abstract = {Stroke remains a leading cause of long-term disability and mortality worldwide, necessitating novel therapeutic strategies to enhance recovery. Traditional rehabilitation approaches, including physical therapy and pharmacological interventions, often provide limited functional improvement. Neuromodulation has emerged as a promising strategy to promote post-stroke recovery by enhancing neuroplasticity and functional reorganization. Among various neuromodulatory techniques, chemogenetics, particularly Designer Receptors Exclusively Activated by Designer Drugs (DREADDs), offers precise, cell-type-specific, and temporally controlled modulation of neuronal and glial activity. This review explores the mechanisms and therapeutic potential of chemogenetic modulation in stroke recovery. Preclinical studies have demonstrated that activation of excitatory DREADDs (hM3Dq) in neurons located within the peri-infarct area or contralateral M1 has been shown to enhance neuroplasticity, facilitate axonal sprouting, and lead to improved behavioral recovery following stroke. Conversely, stimulation of inhibitory DREADDs (hM4Di) suppresses stroke-induced excitotoxicity, mitigates peri-infarct spreading depolarizations (PIDs), and modulates neuroinflammatory responses. By targeting specific neuronal and glial populations, chemogenetics enables phase-specific interventions-early inhibition to minimize damage during the acute phase and late excitation to promote plasticity during the recovery phase. Despite its advantages over traditional neuromodulation techniques, such as optogenetics and deep brain stimulation, several challenges remain before chemogenetics can be translated into clinical applications. These include optimizing viral vector delivery, improving ligand specificity, minimizing off-target effects, and ensuring long-term receptor stability. Furthermore, integrating chemogenetics with existing stroke rehabilitation strategies, including brain-computer interfaces and physical therapy, may enhance functional recovery by facilitating adaptive neuroplasticity. Future research should focus on refining chemogenetic tools to enable clinical application. By offering a highly selective, reversible, and minimally invasive approach, chemogenetics holds great potential for revolutionizing post-stroke therapy and advancing personalized neuromodulation strategies.},
}

@article {pmid40338888,
year = {2025},
author = {Faisal, M and Khosa, I and Waris, A and Gilani, SO and Khan, MJ and Hazzazi, F and Ijaz, MA},
title = {Optimizing the impact of time domain segmentation techniques on upper limb EMG decoding using multimodal features.},
journal = {PloS one},
volume = {20},
number = {5},
pages = {e0322580},
doi = {10.1371/journal.pone.0322580},
pmid = {40338888},
issn = {1932-6203},
mesh = {Humans ; *Electromyography/methods ; *Upper Extremity/physiology ; Male ; Adult ; Female ; Movement/physiology ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Young Adult ; },
abstract = {Neurological disorders, such as stroke, spinal cord injury, and amyotrophic lateral sclerosis, result in significant motor function impairments, affecting millions of individuals worldwide. To address the need for innovative and effective interventions, this study investigates the efficacy of electromyography (EMG) decoding in improving motor function outcomes. While existing literature has extensively explored classifier selection and feature set optimization, the choice of preprocessing technique, particularly time-domain windowing techniques, remains understudied posing a significant knowledge gap. This study presents upper limb movement classification by providing a comprehensive comparison of eight time-domain windowing techniques. For this purpose, the EMG data from volunteers is recorded involving fifteen distinct movements of fingers. The rectangular window technique among others emerged as the most effective, achieving a classification accuracy of 99.98% while employing 40 time-domain features and a L-SVM classifier, among other classifiers. This optimal combination has implications for the development of more accurate and reliable myoelectric control systems. The achieved high classification accuracy demonstrates the feasibility of using surface EMG signals for accurate upper limb movement classification. The study's results have the potential to improve the accuracy and reliability of prosthetic limbs and wearable sensors and inform the development of personalized rehabilitation programs. The findings can contribute to the advancement of human-computer interaction and brain-computer interface technologies.},
}

Humans
*Electromyography/methods
*Upper Extremity/physiology
Male
Adult
Female
Movement/physiology
Signal Processing, Computer-Assisted
Support Vector Machine
Young Adult

@article {pmid40338479,
year = {2025},
author = {Moreno-Alcayde, Y and Traver, VJ and Leiva, LA},
title = {Predicting fixations and gaze location from EEG.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {40338479},
issn = {1741-0444},
support = {CHIST-ERA-20-BCI-001//HORIZON EUROPE Framework Programme/ ; 101071147//HORIZON EUROPE European Innovation Council/ ; PCI2021-122036-2A//Agencia Estatal de Investigación/ ; },
abstract = {Brain signals carry cognitive information that can be relevant in downstream tasks, but what about eye-gaze? Although this can be estimated with eye-trackers, it can be very convenient in practice to do it without extra equipment. We consider the challenging tasks of fixation prediction and gaze estimation from electroencephalography (EEG) using deep learning models. We argue that there are three critical design criteria when designing neural architectures for EEG: (1) the spatial and temporal dimensions of the data, (2) the local vs global nature of the data processing, and (3) the overall structure and order with which the steps (1) and (2) are orchestrated. We propose two model architectures, based on Transformers and LSTMs, with different variants in this large design space, and compare them with recent state-of-the-art (SOTA) approaches under two constraints: reduced EEG signal length and reduced set of EEG channels. Our Transformer-based model outperforms the LSTM-only model, but it turns out to be more sensitive with short signal lengths and with less number of channels. Interestingly, our results are similar or slightly better than SOTA, and the models are trained from scratch (i.e., without pre-training or fine-tuning). Our findings provide useful insights for advancing in eye-from-EEG tasks.},
}

@article {pmid40336015,
year = {2025},
author = {Yang, P and Zhang, X and Song, H and Zhang, X},
title = {An investigation into mental illness and its comorbidities from the perspective of supervenience physicalism.},
journal = {Philosophy, ethics, and humanities in medicine : PEHM},
volume = {20},
number = {1},
pages = {10},
pmid = {40336015},
issn = {1747-5341},
abstract = {The exploration into the origin of human spirituality has always been a hot spot with many unsolved questions in the philosophy of mind, and issues concerning mental illness and its comorbidities are still unclear. In the 1970s, Donald Davidson first proposed anomalous monism with the supervenience concept, a theory that both insists on physicalism and transcends traditional reductionism. This theory provides solid and accessible proof for perceiving the mind-body relationship of spiritual origin in a non-reductionist approach. This paper develops arguments in two aspects. First, three principles of anomalous monism are employed to explore the origin of mental illness. Second, the comorbidity of mental illness is explained with the help of the supervenience theory.},
}

@article {pmid40335704,
year = {2025},
author = {Yang, L and Guo, C and Zheng, Z and Dong, Y and Xie, Q and Lv, Z and Li, M and Lu, Y and Guo, X and Deng, R and Liu, Y and Feng, Y and Mu, R and Zhang, X and Ma, H and Chen, Z and Zhang, Z and Dong, Z and Yang, W and Zhang, X and Cui, Y},
title = {Publisher Correction: Stress dynamically modulates neuronal autophagy to gate depression onset.},
journal = {Nature},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41586-025-09086-9},
pmid = {40335704},
issn = {1476-4687},
}

@article {pmid40334964,
year = {2025},
author = {Dong, L and Qi, Y and Luan, M and Liu, Q and Wang, M and Tian, C and Zheng, Y},
title = {A multi-channel implantable micro-magnetic stimulator for synergistic magnetic neuromodulation.},
journal = {Brain research},
volume = {},
number = {},
pages = {149679},
doi = {10.1016/j.brainres.2025.149679},
pmid = {40334964},
issn = {1872-6240},
abstract = {Micro-magnetic stimulation (μMS) is an emerging technology in magnetic neuromodulation. However, for larger brain structures with complex neural pathways, such as deep brain neural clusters, traditional implantable single-point μMS devices are immobile and incapable of multi-regional magnetic modulation. While multi-channel μMS can effectively address this limitation, its large size, difficulty in implantation, and unclear synergistic modulation patterns restrict its application. To tackle these challenges, this study designs a 4 × 4 array micro-coil structure targeted at the deep hippocampal region of the mouse brain. Numerical simulations were performed to analyze the coupling coefficients among the micro-coils and the distribution of the electromagnetic field in the structure, indicating that, with optimized parameters, the effective magnetic stimulation threshold can be achieved. Based on this, a multi-channel μMS device was fabricated, solving key issues such as waterproofing, biocompatibility, and dual-brain-region implantation of both stimulation and recording electrodes. A multi-point synergistic magnetic stimulation protocol was developed. After determining the synergistic magnetic stimulation parameters and effective target positions through in vitro experiments, real-time monitoring of calcium signal changes in the CA1 region of the hippocampus in mice during synergistic magnetic stimulation was performed. The results demonstrate that synergistic magnetic stimulation significantly enhances synaptic plasticity and calcium signal activity. This validates the feasibility of the implantable multi-channel micro-magnetic stimulator.},
}

@article {pmid40334847,
year = {2025},
author = {Yuan, J and Pan, H and Sun, Y and Wang, Y and Jia, J},
title = {Neural responses to global and local visual information processing provide neural signatures of ADHD symptoms.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {},
number = {},
pages = {112582},
doi = {10.1016/j.ijpsycho.2025.112582},
pmid = {40334847},
issn = {1872-7697},
abstract = {Individuals with ADHD are thought to exhibit a reduced "global bias" in perceptual processing. This bias, found in typically developed individuals, characterizes the tendency to prioritize global over local information processing. However, the relationship between specific ADHD symptoms and global or local processing remains unclear. This study addresses this gap by employing an ensemble perception task with a large sample (N = 465). EEG recordings allowed for the isolation of neural responses to individual and global stimuli using linear regression modeling. The adult ADHD self-report scale was used to assess ADHD symptoms. The results showed a significant association between ensemble perception and early responses to global stimuli. Furthermore, inattention symptoms were associated with early responses to global stimuli, suggesting a reduced global prioritization in individuals with higher inattention scores. Moreover, inattention symptom was associated with later responses to local stimuli, as shown by attenuated neural responses to local stimuli in individuals with more severe symptoms. These findings provide insights that ADHD includes deficits in both global and local processing, challenging earlier theories that focused solely on global processing impairments.},
}

@article {pmid40334819,
year = {2025},
author = {Yu, H and Zhou, X and Ru, Q and Anthony, A and Cameron, M and Liu, Y and Klann, IP and Guo, H and Lin, J and Wang, D and Chang, D},
title = {The modulatory effects of persimmon leaf extract on sleep-related neurotransmitters and its potential hypnotic effects.},
journal = {Fitoterapia},
volume = {},
number = {},
pages = {106576},
doi = {10.1016/j.fitote.2025.106576},
pmid = {40334819},
issn = {1873-6971},
abstract = {PURPOSE: Persimmon leaf is a traditional herbal medicine with diverse therapeutic applications. This study aimed to explore the effect of persimmon leaf extract (PLE) on the modulation of neurotransmitters involved in sleep regulation and its overall impact on sleep latency and duration.

METHODS: The key components of PLE were identified by ultra performance liquid chromatography. The modulatory effects of PLE in sleep and wakefulness-related neurotransmitters were studied in human neuroblastoma SH-SY5Y cells. PLE was also investigated in pentobarbital sodium-induced sleep and para-chlorophenylalanine (PCPA)-induced insomnia models in mice and rats.

RESULTS: PLE induced chloride influx and increased the intracellular production of gamma-aminobutyric acid (GABA), a neurotransmitter crucial for sleep regulation, in SH-SY5Y cells. Furthermore, PLE influenced the cellular expressions of serotonin, dopamine, and adenosine. It increased monoamine oxidase enzyme-A activity and reduced serotonin levels and its metabolites. It induced dopamine biosynthesis and degradation pathways. In the pentobarbital-induced sleep experiment, PLE significantly prolonged total sleep duration and reduced sleep latency in a dose-dependent manner. In the PCPA-induced insomnia model, PLE consistently increased GABA production, and lowered dopamine expression.

CONCLUSION: PLE exhibited modulatory effects on sleep-related neurotransmitters in vitro, which may also contribute to its hypnotic effects by extending the sleep duration and shortening sleeping latency in vivo.},
}

@article {pmid40334321,
year = {2025},
author = {Chen, X and Jia, T and Wu, D},
title = {Data alignment based adversarial defense benchmark for EEG-based BCIs.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {188},
number = {},
pages = {107516},
doi = {10.1016/j.neunet.2025.107516},
pmid = {40334321},
issn = {1879-2782},
abstract = {Machine learning has been extensively applied to signal decoding in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). While most studies have focused on enhancing the accuracy of EEG-based BCIs, more attention should be given to their security. Recent findings reveal that EEG-based BCIs are vulnerable to adversarial attacks. To address this, we present the first adversarial defense benchmark based on data alignment, aiming to enhance both the accuracy and robustness of EEG-based BCIs. This study evaluates nine adversarial defense approaches (including five defense strategies) across five EEG datasets (covering three paradigms), three neural networks, and four experimental scenarios. Our results show for the first time that integrating data augmentation, data alignment, and robust training can further improve both the accuracy and robustness of BCIs compared to using only one or two of them. Furthermore, we provide insights into the characteristics of various adversarial defense approaches based on EEG data alignment, offering valuable guidance for developing more accurate and secure EEG-based BCIs.},
}

@article {pmid40332078,
year = {2025},
author = {Park, BS and Yang, HR and Kang, H and Kim, KK and Kim, YT and Yang, S and Kim, JG},
title = {α2-Adrenergic Receptors in Hypothalamic Dopaminergic Neurons: Impact on Food Intake and Energy Expenditure.},
journal = {International journal of molecular sciences},
volume = {26},
number = {8},
pages = {},
doi = {10.3390/ijms26083590},
pmid = {40332078},
issn = {1422-0067},
support = {NRF-2021R1C1C2005067//National Research Foundation of Korea/ ; Research Grant (2020)//Incheon National University/ ; },
mesh = {Animals ; *Energy Metabolism/drug effects ; *Hypothalamus/metabolism/cytology/drug effects ; *Dopaminergic Neurons/metabolism/drug effects ; *Receptors, Adrenergic, alpha-2/metabolism/genetics ; Mice ; Male ; *Eating/drug effects ; Mice, Inbred C57BL ; },
abstract = {The adrenergic system plays an active role in modulating synaptic transmission in hypothalamic neurocircuitry. While α2-adrenergic receptors are widely distributed in various organs and are involved in various physiological functions, their specific role in the regulation of energy metabolism in the brain remains incompletely understood. Herein, we investigated the functions of α2-adrenergic receptors in the hypothalamus on energy metabolism in mice. Our study confirmed the expression of α2-adrenergic receptors in hypothalamic dopaminergic neurons and assessed metabolic phenotypes, including food intake and energy expenditure, after treatment with guanabenz, an α2-adrenergic receptor agonist. Guanabenz treatment significantly increased food intake (0.25 ± 0.03 g vs. 0.98 ± 0.05 g, p < 0.001) and body weight (-0.1 ± 0.04 g vs. 0.33 ± 0.03 g, p < 0.001) within 6 h post-treatment. Furthermore, guanabenz markedly elevated energy expenditure parameters, including respiratory exchange ratio (RER, 1.017 ± 0.007 vs. 1.113 ± 0.03, p < 0.01) and carbon dioxide production (1.512 ± 0.018 mL/min vs. 1.635 ± 0.036 mL/min, p < 0.05), compared to vehicle-treated controls. Furthermore, using chemogenetic techniques, we demonstrated that the altered metabolic phenotypes induced by guanabenz treatment were effectively reversed by inhibiting the activity of dopaminergic neurons in the hypothalamic arcuate nucleus (ARC) using a chemogenetic technique. Our findings suggest functional connectivity between hypothalamic α2-adrenergic receptor signals and dopaminergic neurons in metabolic controls.},
}

Animals
*Energy Metabolism/drug effects
*Hypothalamus/metabolism/cytology/drug effects
*Dopaminergic Neurons/metabolism/drug effects
*Receptors, Adrenergic, alpha-2/metabolism/genetics
Mice
Male
*Eating/drug effects
Mice, Inbred C57BL

@article {pmid40330423,
year = {2009},
author = {Mollazadeh, M and Aggarwal, V and Thakor, NV and Law, AJ and Davidson, A and Schieber, MH},
title = {Coherency between Spike and LFP Activity in M1 during Hand Movements.},
journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering},
volume = {2009},
number = {},
pages = {506-509},
pmid = {40330423},
issn = {1948-3546},
abstract = {Local field potentials (LFP) represent the dendritic activity of a population of cells near the recording electrode. However, how LFP activity is related to single unit activity, and if it provides any additional information has not been well studied. Previously we have shown that temporal spectral modulation of LFP activity can be used to decode dexterous movements of the hand. Here, we analyze simultaneous spike and LFP recordings from M1 cortex in a rhesus monkey performing fine hand movements. Using multitaper spectral analysis, we found that both LFP and spiking activity show an increase in power in the <12 Hz and 70-200 Hz (high gamma) ranges, but, were significantly coherent only during the pre-movement time at low frequencies (<12 Hz). Furthermore, using either LFP or spiking activity, we were able to decode amongst three different hand grasps with high accuracy (99% using 97 spikes and 70% using 8 LFP channels). However, while spikes were better in decoding movement types, LFPs performed much better (94% success) than spikes (77%) when differentiating between rest and movement. We also found that combining spike and LFP activity can improve decoding performance when fewer spikes are considered, as may be the case when single unit recordings degrade over time (71% using 40 spikes and 76% using 8 LFPs, vs 88% using 40 spikes + 8 LFPs). Thus, the relative stability of LFP activity can help augment single-unit activity for the chronic operation of a multimodal BMI.},
}

@article {pmid40329250,
year = {2025},
author = {Karimi, J and Cherono, A and Alegana, V and Mutua, M and Kiarie, H and Muthee, R and Temmerman, M and Gichangi, P},
title = {Geographic inequalities, and social-demographic determinants of reproductive, maternal and child health at sub-national levels in Kenya.},
journal = {BMC public health},
volume = {25},
number = {1},
pages = {1656},
pmid = {40329250},
issn = {1471-2458},
support = {211208/WT_/Wellcome Trust/United Kingdom ; 211208/WT_/Wellcome Trust/United Kingdom ; 211208/WT_/Wellcome Trust/United Kingdom ; },
abstract = {BACKGROUND: Global initiatives have emphasized tracking indicators to monitor progress, particularly in countries with the highest maternal and child mortality. Routine data can be used to monitor indicators for improved target setting at national and subnational levels. Our objective was to assess the geographic inequalities in estimates of reproductive, maternal and child health indicators from routine data at the subnational level in Kenya.

METHODS: Monthly data from 47 counties clustered in 8 regions, from January 2018 to December 2021 were assembled from the District Health Information Software version 2 (DHIS2) in Kenya. This included women of reproductive age receiving family planning commodities, pregnant women completing four antenatal care visits, deliveries conducted by skilled birth attendants, fully immunized children at 1 year and number of maternal deaths at health facilities, from which five indicators were constructed with denominators. A hierarchical Bayesian model was used to generate estimates of the five indicators at the at sub-national levels(counties and sub counties), adjusting for four determinants of health. A reproductive, maternal, and child health (RMCH) index was generated from the 5 indicators to compare overall performance across the continuum of care in reproductive, maternal and child health across the different counties.

RESULTS: The DHIS2 data quality for the selected 5 indicators was acceptable with detection of less than 3% outliers for the Facility Maternal Mortality Ratio (FMMR) and less than 1% for the other indicators. Overall, counties in the north-eastern, eastern and coastal regions had the lowest RMCH index due to low service coverage and high FMMR. Full immunization coverage at 1 year (FIC) had the highest estimate (79.3%, BCI: 77.8-80.5%), while Women of Reproductive age receiving FP commodities had the lowest estimate (38.6%, BCI: 38.2-38.9%). FMMR was estimated at 105.4, (BCI 67.3-177.1)Health facility density was an important determinant in estimating all five indicators. Maternal education was positively correlated with higher FIC coverage, while wealthier sub counties had higher FMMR.

CONCLUSIONS: Tracking of RMCH indicators revealed geographical inequalities at the County and subcounty level, often masked by national-level estimates. These findings underscore the value of routine monitoring indicators as a potential for evidence-based sub-national planning and precision targeting of interventions to marginalized populations.},
}

@article {pmid40327905,
year = {2025},
author = {Sarankumar, R and Ramkumar, M and Karthik, V and Muthuvel, SK},
title = {Integrating attention networks into a hybrid model for HER2 status prediction in breast cancer.},
journal = {Biochemical and biophysical research communications},
volume = {768},
number = {},
pages = {151856},
doi = {10.1016/j.bbrc.2025.151856},
pmid = {40327905},
issn = {1090-2104},
abstract = {Breast cancer is one of the most prevalent cancers amongst women, caused by uncontrolled cell growth in breast tissue. Human Epidermal growth factor Receptor 2 (HER2) proteins play a vital role in regulating normal breast cell development and division, and the status is crucial for determining prognosis and treatment strategies. Despite the availability of various techniques to identify the HER2 gene in tumors, the prediction accuracy of existing methods remains insufficient. This research aims to improve HER2 status prediction accuracy by proposing an Enhanced Hybrid Model with Optimized Attention Network (EHMOA-net) for histopathology image analysis. The methodology involves patch segmentation using an Encoder-Decoder-based hybrid weights alignment with Multi-Dilated U-net (EDMDU) model applied to the TCGA dataset, followed by preprocessing through enhanced Macenko stain normalization for segmented patches and images from the BCI dataset. Improved non-subsampled shearlet transform is utilized for feature extraction, and the Hybrid Enhanced Rough k-means clustering and Fuzzy C-Means (HERFCM) algorithm is employed to cluster neighboring image patches based on similar features. Finally, HER2 prediction is performed using nested graph neural networks integrated with a visual attention network. The proposed method, implemented in Python, achieves an accuracy of 97.85 %, surpassing existing techniques. These findings demonstrate the effectiveness of EHMOA-net in improving HER2 prediction accuracy and its potential utility in clinical applications.},
}

@article {pmid40327498,
year = {2025},
author = {Xu, Y and Wang, X and Li, J and Zhang, X and Li, F and Gao, Q and Fu, C and Leng, Y},
title = {A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3567392},
pmid = {40327498},
issn = {1558-0210},
abstract = {The anthropomorphic grasping capability of prosthetic hands is critical for enhancing user experience and functional efficiency. Existing prosthetic hands relying on brain-computer interfaces (BCI) and electromyography (EMG) face limitations in achieving natural grasping due to insufficient gesture adaptability and intent recognition. While vision systems enhance object perception, they lack dynamic human-like gesture control during grasping. To address these challenges, we propose a vision-powered prosthetic hand system that integrates two innovations. Spatial Geometry-based Gesture Mapping (SG-GM) dynamically models finger joint angles as polynomial functions of hand-object distance, derived from geometric features of human grasping sequences. These functions enable continuous anthropomorphic gesture transitions, mimicking natural hand movements. Motion Trajectory Regressionbased Grasping Intent Estimation (MTR-GIE) predicts user intent in multi-object environments by regressing wrist trajectories and spatially segmenting candidate objects. Experiments with eight daily objects demonstrated high anthropomorphism (similarity coefficient R[2]=0.911, root mean squared error RMSE=2.47°), rapid execution (3.0710.41 s), and robust success rates (95.43% single-object; 88.75% multi-object). The MTR-GIE achieved 94.35% intent estimation accuracy under varying object spacing. This work pioneers vision-driven dynamic gesture synthesis for prosthetics, eliminating dependency on invasive sensors and advancing real-world usability.},
}

@article {pmid40326979,
year = {2026},
author = {Du, X and Wang, Y and Wang, X and Tian, X and Jing, W},
title = {Neural circuit mechanisms of epilepsy: Maintenance of homeostasis at the cellular, synaptic, and neurotransmitter levels.},
journal = {Neural regeneration research},
volume = {21},
number = {2},
pages = {455-465},
doi = {10.4103/NRR.NRR-D-24-00537},
pmid = {40326979},
issn = {1673-5374},
abstract = {Epilepsy, a common neurological disorder, is characterized by recurrent seizures that can lead to cognitive, psychological, and neurobiological consequences. The pathogenesis of epilepsy involves neuronal dysfunction at the molecular, cellular, and neural circuit levels. Abnormal molecular signaling pathways or dysfunction of specific cell types can lead to epilepsy by disrupting the normal functioning of neural circuits. The continuous emergence of new technologies and the rapid advancement of existing ones have facilitated the discovery and comprehensive understanding of the neural circuit mechanisms underlying epilepsy. Therefore, this review aims to investigate the current understanding of the neural circuit mechanisms in epilepsy based on various technologies, including electroencephalography, magnetic resonance imaging, optogenetics, chemogenetics, deep brain stimulation, and brain-computer interfaces. Additionally, this review discusses these mechanisms from three perspectives: structural, synaptic, and transmitter circuits. The findings reveal that the neural circuit mechanisms of epilepsy encompass information transmission among different structures, interactions within the same structure, and the maintenance of homeostasis at the cellular, synaptic, and neurotransmitter levels. These findings offer new insights for investigating the pathophysiological mechanisms of epilepsy and enhancing its clinical diagnosis and treatment.},
}

@article {pmid40323825,
year = {2025},
author = {Kim, H and Chang, WK and Kim, WS and Jang, JH and Lee, YA and Vermehren, M and Peekhaus, N and Colucci, A and Angerhöfer, C and Hömberg, V and Soekadar, SR and Paik, NJ},
title = {Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {218},
pages = {},
doi = {10.3791/67601},
pmid = {40323825},
issn = {1940-087X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Robotics/methods/instrumentation ; *Stroke Rehabilitation/methods/instrumentation ; *Upper Extremity/physiopathology ; Male ; Female ; Middle Aged ; Electroencephalography/methods ; Activities of Daily Living ; Aged ; Adult ; Electrooculography/methods ; },
abstract = {This study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for post-stroke rehabilitation. The system utilizes electroencephalogram (EEG) and electrooculogram (EOG) signals to help users assist upper limb function in everyday tasks while interacting with a robotic hand. We evaluated the effectiveness of this BCI-robot system using the Berlin Bimanual Test for Stroke (BeBiTS), a set of 10 daily living tasks involving both hands. Eight stroke patients participated in this study, but only four participants could adapt to the BCI robot system training and perform the postBeBiTS. Notably, when the preBeBiTS score for each item was four or less, the BCI robot system showed greater assistive effectiveness in the postBeBiTS assessment. Furthermore, our current robotic hand does not assist with arm and wrist functions, limiting its use in tasks requiring complex hand movements. More participants are required to confirm the training effectiveness of the BCI-robot system, and future research should consider using robots that can assist with a broader range of upper limb functions. This study aimed to determine the BCI-robot system's ability to assist patients in performing daily living activities.},
}

Humans
*Brain-Computer Interfaces
*Robotics/methods/instrumentation
*Stroke Rehabilitation/methods/instrumentation
*Upper Extremity/physiopathology
Male
Female
Middle Aged
Electroencephalography/methods
Activities of Daily Living
Aged
Adult
Electrooculography/methods

@article {pmid40321898,
year = {2025},
author = {Zhang, S and Song, Y and Lv, S and Jing, L and Wang, M and Liu, Y and Xu, W and Jiao, P and Zhang, S and Wang, M and Liu, J and Wu, Y and Cai, X},
title = {Electrode Arrays for Detecting and Modulating Deep Brain Neural Information in Primates: A Review.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0249},
pmid = {40321898},
issn = {2692-7632},
abstract = {Primates possess a more developed central nervous system and a higher level of intelligence than rodents. Detecting and modulating deep brain activity in primates enhances our understanding of neural mechanisms, facilitates the study of major brain diseases, enables brain-computer interactions, and supports advancements in artificial intelligence. Traditional imaging methods such as magnetic resonance imaging, positron emission computed tomography, and scalp electroencephalogram are limited in spatial resolution. They cannot accurately capture deep brain signals from individual neurons. With the progress of microelectromechanical systems and other micromachining technologies, single-neuron level detection and stimulation technology in rodents based on microelectrodes has made important progress. However, compared with rodents, human and nonhuman primates have larger brain volume that needs deeper implantation depth, and the test object has higher safety and device preparation requirements. Therefore, high-resolution devices suitable for long-term detection in the brains of primates are urgently needed. This paper reviewed electrode array devices used for electrophysiological and electrochemical detections in primates' deep brains. The research progress of neural recording and stimulation technologies was introduced from the perspective of electrode type and device structures, and their potential value in neuroscience research and clinical disease treatments was discussed. Finally, it is speculated that future electrodes will have a lot of room for development in terms of flexibility, high resolution, deep brain, and high throughput. The improvements in electrode forms and preparation process will expand our understanding of deep brain neural activities, and bring new opportunities and challenges for the further development of neuroscience.},
}

@article {pmid40321282,
year = {2025},
author = {Forenzo, D and Zhang, Y and Wittenberg, GF and He, B},
title = {Continuous Reaching and Grasping with a BCI Controlled Robotic Arm in Healthy and Stroke-Affected Individuals.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.16.25325551},
pmid = {40321282},
abstract = {Recent advancements in signal processing techniques have enabled non-invasive Brain-Computer Interfaces (BCIs) to control assistive devices, like robotic arms, directly with users' EEG signals. However, the applications of these systems are currently limited by the low signal-to-noise ratio and spatial resolution of EEG from which brain intention is decoded. In this study, we propose a motor-imagery (MI) paradigm, inspired by the mechanisms of a computer mouse, that adds an additional "click" signal to an established 2D movement BCI paradigm. The additional output signal increases the degrees of freedom of the BCI system and may enable more complex tasks. We evaluated this paradigm using deep learning (DL) based signal processing on both healthy subjects and stroke-survivors in online BCI tasks derived from two potential applications: clicking on virtual targets and moving physical objects with a robotic arm in a continuous reach-and-grasp task. The results show that subjects were able to control both movement and clicking simultaneously to grab, move, and place up to an average of 7 cups in a 5-minute run using the robotic arm. The proposed paradigm provides an additional degree of freedom to EEG BCIs, and improves upon existing systems by enabling continuous control of reach-and-grasp tasks instead of selecting from a discrete list of predetermined actions. The tasks studied in these experiments show BCIs may be used to control computer cursors or robotic arms for complex real-world or clinical applications in the near future, potentially improving the lives of both healthy individuals and motor-impaired patients.},
}

@article {pmid40318536,
year = {2025},
author = {Tang, E and Li, J and Liu, H and Peng, C and Zhou, D and Hu, S and Chen, H},
title = {Lack of social interaction advantage: A domain-general cognitive alteration in schizophrenia.},
journal = {Journal of psychiatric research},
volume = {186},
number = {},
pages = {434-444},
doi = {10.1016/j.jpsychires.2025.04.030},
pmid = {40318536},
issn = {1879-1379},
abstract = {People with schizophrenia (PSZ) showed preserved ability to unconsciously process simple social information (e.g., face and gaze), but not in higher-order cognition (e.g., memory). It is yet unknown how PSZ process social interactions across different cognitive domains. This study systematically investigated the cognitive characteristics of PSZ during social interaction processing from bottom-up perception to top-down memory, and established correlations with schizophrenic symptoms. In two experiments, social interactions were consistently displayed by face-to-face or back-to-back dyads. Experiment 1 enrolled 30 PSZ and 30 healthy control subjects (HCS) with a breaking continuous flash suppression (b-CFS) paradigm. Experiment 2 recruited 36 PSZ and 36 HCS for two memory tasks, wherein participants restored the between-model distance (working memory task) and recalled the socially bound pairs (long-term memory task). Results indicated that HCS showed advantageous processing of socially interactive stimuli against non-interactive stimuli throughout two experiments, including faster access to visual consciousness, closer spatial distance held in working memory and higher recollection accuracy in long-term memory. However, PSZ did not show any of these advantages, with significant interaction effects for all three tasks (task one: p = .018, ηp[2] = .092; task two: p = .021, ηp[2] = .074; task three: p = .015, ηp[2] = .082). Moreover, correlation analyses indicated that PSZ with more severe negative symptoms (r = -.344, p = .040) or higher medication dosages (r = -.334, p = .046) showed fewer advantages in memorizing socially interactive information. Therefore, social interaction is not prioritized in schizophrenia from bottom-up perception to top-down memory, and the magnitude of such a domain-general cognitive alteration is clinically relevant to symptom severity and medication.},
}

@article {pmid40317785,
year = {2025},
author = {Stieglitz, T and Bersch, I and Mrachacz-Kersting, N and Pasluosta, C},
title = {Differences and Commonalities of Electrical Stimulation Paradigms After Central Paralysis and Amputation.},
journal = {Artificial organs},
volume = {},
number = {},
pages = {},
doi = {10.1111/aor.15017},
pmid = {40317785},
issn = {1525-1594},
abstract = {BACKGROUND: Patients with spinal cord injury (SCI) or with severe brain stroke suffer from life-lasting functional and sensory impairments. Other traumatic injuries such as limb loss after an accident or disease also affect motor function and sensory feedback and impair quality of life in those individuals. Invasive and non-invasive functional electrical stimulation (FES) is a well-established method to partially restore function and sensory feedback of paralyzed and phantom limbs. It is also a supporting technology for the rehabilitation of the neuromuscular system and for complementing assistive devices.

METHODS: This work reviews the current state-of-the-art of FES as a technology for restoring function and supporting rehabilitation therapy and assistive devices.

RESULTS: Electrodes, electrical stimulation, use of brain signals for rehabilitation and control, and sensory feedback are covered as parts of the whole. A perspective is given on how clinical and research protocols developed for patients with SCI and brain injuries can be translated to the treatment of patients with an amputation and vice versa. We further elaborate on how motor learning strategies with quantitative electrophysiological and kinematic measurements may help caregivers in the rehabilitation process. Insights from practitioners (collected during a workshop of the IFESS 2025) have been integrated to summarize common needs, open questions, and challenges.

CONCLUSIONS: The information from the literature and from practitioners was integrated to propose the next steps towards establishing common guidelines and measures of FES in clinical practice towards evidence-driven treatment and objective assessments.},
}

@article {pmid40317558,
year = {2025},
author = {Du, X and Shen, F and Yu, C and Wang, Y and Yu, J and Yao, L and Liu, N and Zhuang, S},
title = {SMYD3 as an Epigenetic Regulator of Renal Tubular Cell Survival and Regeneration Following Acute Kidney Injury in Mice.},
journal = {FASEB journal : official publication of the Federation of American Societies for Experimental Biology},
volume = {39},
number = {9},
pages = {e70533},
doi = {10.1096/fj.202500089R},
pmid = {40317558},
issn = {1530-6860},
support = {82370698//MOST | National Natural Science Foundation of China (NSFC)/ ; 82070700//MOST | National Natural Science Foundation of China (NSFC)/ ; },
mesh = {Animals ; *Acute Kidney Injury/metabolism/pathology/genetics ; Mice ; *Histone-Lysine N-Methyltransferase/metabolism/genetics/antagonists & inhibitors ; *Epigenesis, Genetic ; *Kidney Tubules/metabolism/pathology ; *Regeneration ; Male ; Cell Survival ; Mice, Inbred C57BL ; Apoptosis ; Cell Proliferation ; Reperfusion Injury/metabolism/pathology ; Histones/metabolism ; },
abstract = {The protein SET and MYND-Domain Containing 3 (SMYD3) is a methyltransferase that modifies various non-histone and histone proteins, linking it to tumorigenesis and cyst formation. However, its role in acute kidney injury (AKI) remains unclear. This study investigates the role and mechanism of AKI using a murine model of ischemia-reperfusion (IR)-induced AKI. After IR injury, SMYD3 and H3K4me3 levels increased in the kidneys, correlating with renal dysfunction, tubular cell injury, and apoptosis. Administration of BCI-121, a selective SMYD3 inhibitor, exacerbated IR-induced tubular cell injury and apoptosis, leading to more severe renal dysfunction and pathological changes. Pharmacological inhibition of SMYD3 also impaired the dedifferentiation and proliferation of renal tubular cells, key regenerative processes in injured kidneys, as evidenced by decreased expression of vimentin, snail, proliferating cell nuclear antigen (PCNA), cyclin D1, and retinoblastoma protein (RB). Additionally, SMYD3 inhibition reduced phosphorylation of the epithelial growth factor receptor (EGFR) and AKT, as well as EGFR expression in damaged kidneys. Finally, both BCI-121 and SMYD3 siRNA reduced EGF-induced expression of vimentin, snail, cyclin D1, PCNA, and EGFR, along with phosphorylation of RB and AKT in cultured renal tubular cells. Chip assay indicated that SMYD3 and H3K4me3 are enriched at the promoter of EGFR and SMYD3 inhibition blocked this response. These data suggest that SMYD3 plays an important role as an epigenetic regulator of renal tubular cell survival and regenerative pathways following kidney injury. Targeting SMYD3 or its epigenetic effects could offer therapeutic potential for enhancing kidney regeneration in AKI and related renal diseases.},
}

Animals
*Acute Kidney Injury/metabolism/pathology/genetics
Mice
*Histone-Lysine N-Methyltransferase/metabolism/genetics/antagonists & inhibitors
*Epigenesis, Genetic
*Kidney Tubules/metabolism/pathology
*Regeneration
Male
Cell Survival
Mice, Inbred C57BL
Apoptosis
Cell Proliferation
Reperfusion Injury/metabolism/pathology
Histones/metabolism

@article {pmid40315903,
year = {2025},
author = {Chueh, SY and Chen, Y and Subramanian, N and Goolsby, B and Navarro, P and Oweiss, K},
title = {Metaplasticity and continual learning: mechanisms subserving brain computer interface proficiency.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add37b},
pmid = {40315903},
issn = {1741-2552},
abstract = {Brain Computer Interfaces (BCIs) require substantial cognitive flexibility to optimize control performance. Remarkably, learning this control is rapid, suggesting it might be mediated by neuroplasticity mechanisms operating on very short time scales. Here, we propose a meta plasticity model of BCI learning and skill consolidation at the single cell and population levels comprised of three elements: a) behavioral time scale synaptic plasticity (BTSP), b) intrinsic plasticity (IP) and c) synaptic scaling (SS) operating at time scales from seconds to minutes to hours and days. Notably, the model is able to explain representational drift - a frequent and widespread phenomenon that adversely affects BCI control and continued use. Approach: We developed an all-optical approach to characterize IP, BTSP and SS with single cell resolution in awake mice using fluorescent two photon (2P) GCaMP7s imaging and optogenetic stimulation of the soma targeted ChRmineKv2.1. We further trained mice on a one-dimensional (1D) BCI control task and systematically characterized within session (seconds to minutes) learning as well as across sessions (days and weeks) with different neural ensembles. Main results: On the time scale of seconds, substantial BTSP could be induced and was followed by significant IP over minutes. Over the time scale of days and weeks, these changes could predict BCI control proficiency, suggesting that BTSP and IP might be complemented by SS to stabilize and consolidate BCI control. Significance: Our results provide early experimental support for a meta plasticity model of continual BCI learning and skill consolidation. The model predictions may be used to design and calibrate neural decoders with complete autonomy while considering the temporal and spatial scales of plasticity mechanisms. With the power of modern-day machine learning (ML) and artificial Intelligence (AI), fully autonomous neural decoding and adaptation in BCIs might be achieved with minimal to no human intervention. .},
}

@article {pmid40315795,
year = {2025},
author = {Chen, TY and Lien, KH and Yeh, KT and Tu, JC and Wai-Yee Ho, V and Chan, KC},
title = {Bonebridge BCI 602 implantation in syndromic and non-syndromic patients with bilateral microtia and aural atresia.},
journal = {International journal of pediatric otorhinolaryngology},
volume = {193},
number = {},
pages = {112370},
doi = {10.1016/j.ijporl.2025.112370},
pmid = {40315795},
issn = {1872-8464},
abstract = {OBJECTIVE: To evaluate the safety and efficacy of Bonebridge bone conduction implant (BCI) 602 implantation in syndromic and non-syndromic patients with bilateral microtia and aural atresia (AA).

METHODS: This retrospective study included 15 patients (3 syndromic, 12 non-syndromic) with bilateral microtia and AA who underwent BCI 602 implantation at a tertiary medical center between January 2022 and June 2024. Intraoperative and postoperative complications were recorded, with a minimum follow-up of six months. Audiological outcomes, including functional hearing gain (FHG), speech reception threshold (SRT), and word recognition score (WRS), were analyzed.

RESULTS: No intraoperative complications occurred in any cases. One minor postoperative complication (6.7 %) was reported in a non-syndromic patient during follow-up. The mean unaided and aided sound field threshold pure tone averages were 60.3 ± 8.7 dB HL and 23.8 ± 3.9 dB HL, respectively, yielding an FHG of 36.6 ± 9.3 dB HL (p < 0.05). SRT improved from 57.0 ± 5.9 dB HL to 27.0 ± 6.5 dB HL in quiet and from 0.3 ± 8.5 dB SNR to -10.7 ± 4.2 dB SNR in noise. WRS increased from 45.1 ± 20.7 % to 89.9 ± 5.6 % in quiet and from 40.9 ± 20.9 % to 80.9 ± 13.8 % in noise (p < 0.05). Improvements in FHG, SRT, and WRS were comparable between syndromic and non-syndromic groups (p > 0.05).

CONCLUSIONS: The Bonebridge BCI 602 is a safe and effective option for hearing restoration in both syndromic and non-syndromic patients with bilateral microtia and AA. Its compact design enhances surgical safety and minimizes risks to critical structures, particularly in syndromic patients with complex temporal bone anatomy.},
}

@article {pmid40315092,
year = {2025},
author = {Zeng, F and Wen, X and Tang, H and Hu, G and Hou, W and Zhang, X},
title = {Age-related Changes in Action Observation EEG Response and its Effect on BCI Performance.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3566371},
pmid = {40315092},
issn = {1558-0210},
abstract = {Action observation-based brain-computer interface (AO-BCI) can simultaneously elicit steady-state motion visual evoked potential in the occipital region and sensorimotor rhythm in the sensorimotor region, demonstrating substantial potential in neuro-rehabilitation applications. While current AO-BCI research primarily focuses on the younger population, this study conducted a comparative investigation of age-related differences in EEG response to the AO-BCI by enrolling 18 older and 18 younger subjects. We employed task discriminant component analysis (TDCA) to decode observed actions and performed comprehensive analyses of prefrontal EEG responses, i.e. approximate entropy (ApEn), sample entropy (SamEn), and rhythm power ratios (RPR), and the whole-brain functional network. Regression analyses were subsequently conducted to analyze the effects on the classification accuracy. Results revealed significantly diminished TDCA accuracy in older subjects (77.01% ± 14.67%) compared to younger subjects (87.22% ± 15.22%). Age-dependent EEG responses emerged across multiple dimensions: 1) Prefrontal ApEn, SamEn, and RPR exhibited distinct aging patterns; 2) Brain network analysis uncovered significant intergroup differences in α and β band connectivity strength; 3) θ band network topology demonstrated reduced prefrontal nodal degree along with enhanced global efficiency in older subjects. Regression analysis identified a robust inverse relationship between the β/θ RPR during stimulation and overall accuracy. And the β/θ RPR and the β band ApEn might be the main factor that causing individual differences in the identification accuracy in older and younger subjects, respectively. This study provides novel insights into age-related neuro-mechanisms in AO-BCI, establishing quantitative relationships between specific EEG features and BCI performance. These findings would offer guidelines for optimizing AO-BCI in rehabilitation.},
}

@article {pmid40313536,
year = {2025},
author = {Xiong, W and Ma, L and Li, H},
title = {Synthesizing intelligible utterances from EEG of imagined speech.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1565848},
pmid = {40313536},
issn = {1662-4548},
abstract = {Decoding natural language directly from neural activity is of great interest to people with limited communication means. Being a non-invasive and convenient approach, the electroencephalogram (EEG) offers better portability and wider application potentiality. We present an EEG-to-speech system (ETS) that synthesizes audible, and highly understandable language by EEG of imagined speech. Our ETS applies a specially designed X-shape deep neural network (DNN) to realize an End-to-End correspondence between imagined speech EEG and the speech. The system innovatively incorporates dynamic time warping into the network's training process, using actual speech EEG data as a bridge to temporally align imagined speech EEG signals with speech signals. The ETS performance was evaluated on 13 participants who pretraining four Chinese disyllabic words. These words cover all four tones and 40% of the phonemes in Chinese. Our synthesized utterances' average accuracy across all subjects was 91.23%, the average MOS value was 3.50, and the best accuracy was 99% with an MOS value of 3.99. Furthermore, a partial feedback mechanism for DNN and spectral subtraction-based speech enhancement are introduced to improve the quality of generated speech. Our findings prove that non-invasive approaches can be a significant step in regaining verbal language ability.},
}

@article {pmid40313458,
year = {2025},
author = {Xu, Y and Yu, B and Chen, X and Peng, A and Tao, Q and He, Y and Wang, Y and Li, XM},
title = {DSCT: a novel deep-learning framework for rapid and accurate spatial transcriptomic cell typing.},
journal = {National science review},
volume = {12},
number = {5},
pages = {nwaf030},
pmid = {40313458},
issn = {2053-714X},
abstract = {Unraveling complex cell-type-composition and gene-expression patterns at the cellular spatial resolution is crucial for understanding intricate cell functions in the brain. In this study, we developed Deep Neural Network-based Spatial Cell Typing (DSCT)-an innovative framework for spatial cell typing within spatial transcriptomic data sets. This approach utilizes a synergistic integration of an enhanced gene-selection strategy and a lightweight deep neural network for data training, offering a more rapid and accurate solution for the analysis of spatial transcriptomic data. Based on comprehensive analysis, DSCT achieved exceptional accuracy in cell-type identification across various brain regions, species and spatial transcriptomic platforms. It also performed well in mapping finer cell types, thereby showcasing its versatility and adaptability across diverse data sets. Strikingly, DSCT exhibited high efficiency and remarkable processing speed, with fewer computational resource demands. As such, this novel approach opens new avenues for exploring the spatial organization of cell types and gene-expression patterns, advancing our understanding of biological functions and pathologies within the nervous system.},
}

@article {pmid40313281,
year = {2025},
author = {Lee, J and Park, H and Spencer, A and Gong, X and DeNardo, M and Vashahi, F and Pollet, F and Norris, S and Hinton, H and El Fakiri, M and Mehrotra, A and Huang, R and Bar, J and Swann, J and Affonseca, D and Armitage, O and Garry, R and Grumbles, E and Murali, A and Tasserie, J and Fragoso, C and Albouy, R and Couturier, CP and Paulk, AC and Coughlin, B and Cash, SS and Costine-Bartell, B and Baskin, B and Stinson, T and Moradi Chameh, H and Movahed, M and Bazrgar, B and Falby, M and Zhang, D and Valiante, TA and Francis, A and Candanedo, C and Bermudez, R and Liu, J and Ye, T and Le Floch, P},
title = {Clinical translation of ultrasoft Fleuron probes for stable, high-density, and bidirectional brain interfaces.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.24.25326126},
pmid = {40313281},
abstract = {Building brain foundation models to capture the underpinning neural dynamics of human behavior requires large functional neural datasets for training, which current implantable Brain-Computer Interfaces (iBCIs) cannot achieve due to the instability of rigid materials in the brain. How can we realize high-density neural recordings with wide brain region access at single-neuron resolution, while maintaining the long-term stability required? In this study, we present a novel approach to overcome these trade-offs, by introducting Fleuron, a family of ultrasoft, ultra-low-k dielectric materials compatible with thin-film scalable microfabrication techniques. We successfully integrate up to 1,024 sites within a single minimally-invasive Fleuron depth electrode. The combination of the novel implant material and geometry enables single-unit level recordings for 18 months in rodent models, and achieves a large number of units detected per electrode across animals. 128-site Fleuron probes, that cover 8x larger tissue volume than state-of-the-art polyimide counterparts, can track over 100 single-units over months. Stability in neural recordings correlates with reduced glial encapsulation compared to polyimide controls up to 9-month post-implantation. Fleuron probes are integrated with a low-power, mixed-signal ASIC to achieve over 1,000 channels electronic interfaces and can be safely implanted in depth using minimally-invasive surgical techniques via a burr hole approach without requiring specialized robotics. Fleuron probes further create a unique contrast in clinical 3T MRI, allowing for post-operative position confirmation. Large-animal and ex vivo human tissue studies confirm safety and functionality in larger brains. Finally, Fleuron probes are used for the first time ever intraoperatively during planned resection surgeries, confirming in-human usability, and demonstrating the potential of the technology for clincical translation in iBCIs.},
}

@article {pmid40313239,
year = {2025},
author = {Sun, S and Li, C and Xie, X and Wan, X and Liu, T and Li, D and Duan, D and Yu, H and Wen, D},
title = {Digital therapeutics for cognitive impairments associated with schizophrenia: our opinion.},
journal = {Frontiers in psychiatry},
volume = {16},
number = {},
pages = {1535309},
pmid = {40313239},
issn = {1664-0640},
}

@article {pmid40311414,
year = {2025},
author = {Pattanayak, S and Dash, P and Satpathi, S and Sahoo, AK and Das, NR and Nayak, B and Sahoo, SK},
title = {Additive manufacturing of 316 L stainless steel orthopedic implant with improved in vitro hemocompatibility and hydrophilicity for osteoinduction in Wistar rat model.},
journal = {Biomaterials advances},
volume = {175},
number = {},
pages = {214322},
doi = {10.1016/j.bioadv.2025.214322},
pmid = {40311414},
issn = {2772-9508},
abstract = {Long-term implantation is still challenging for 316 L stainless steel (SS) due to low hydrophilicity and borderline corrosion, which further advances a coating to induce osteoinduction and prevent metallic ions leaching. Here, arc-based direct energy deposition technology is introduced to fabricate 316 L SS via additive manufacturing (AM). The AM 316 L SS are subjected to metallurgical, mechanical, chemical, in vitro and in vivo analyses for their possible orthopedic applications. Compared to commercially available 316 L SS implant, the AM implants encompass γ-austenite phases with δ-ferrite structures that induce pinning dislocations, improve resistance to crack propagation and enhance mechanical performances. The evolution of δ-ferrite structures with higher inter-layer dwell times promotes Cr and Mo content, improving passive layer thickness and thereby enhancing the corrosion resistance, which prevents the release of toxic ions into the bloodstream and cellular metabolism. Additionally, improved BCI with less adherence and activation of platelets on the AM deposits indicates uninterrupted blood flow along the site of implantation and improved thrombo-resistance. The reduction in contact angle (highly hydrophilic) promotes the adsorption of body fluid and proteinaceous materials that boost the adhesion, proliferation, and cytoplasmic extension of cells (from in vitro), marrow spaces, collagen fibers, and tissue adherences (from in vivo). The AM implants do not show any acute toxicity in blood profiles and vital organs (liver and kidney) after long-term implantation in Wistar rats. These peculiarities highlight the hemocompatibility and osteointegration capabilities of AM implants with a faster bone regeneration rate.},
}

@article {pmid40309860,
year = {2025},
author = {Gong, J and Liu, H and Duan, F and Che, Y and Yan, Z},
title = {Research on Adaptive Discriminating Method of Brain-Computer Interface for Motor Imagination.},
journal = {Brain sciences},
volume = {15},
number = {4},
pages = {},
doi = {10.3390/brainsci15040412},
pmid = {40309860},
issn = {2076-3425},
support = {MKF202203//Open Foundation of Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University/ ; },
abstract = {(1) Background: Brain-computer interface (BCI) technology represents a cutting-edge field that integrates brain intelligence with machine intelligence. Unlike BCIs that rely on external stimuli, motor imagery-based BCIs (MI-BCIs) generate usable brain signals based on an individual's imagination of specific motor actions. Due to the highly individualized nature of these signals, identifying individuals who are better suited for MI-BCI applications and improving its efficiency is critical. (2) Methods: This study collected four motor imagery tasks (left hand, right hand, foot, and tongue) from 50 healthy subjects and evaluated MI-BCI adaptability through classification accuracy. Functional networks were constructed using the weighted phase lag index (WPLI), and relevant graph theory parameters were calculated to explore the relationship between motor imagery adaptability and functional networks. (3) Results: Research has demonstrated a strong correlation between the network characteristics of tongue imagination and MI-BCI adaptability. Specifically, the nodal degree and characteristic path length in the right hemisphere were found to be significantly correlated with classification accuracy (p < 0.05). (4) Conclusions: The findings of this study offer new insights into the functional network mechanisms of motor imagery, suggesting that tongue imagination holds potential as a predictor of MI-BCI adaptability.},
}

@article {pmid40309849,
year = {2025},
author = {Omer, K and Ferracuti, F and Freddi, A and Iarlori, S and Vella, F and Monteriù, A},
title = {Real-Time Mobile Robot Obstacles Detection and Avoidance Through EEG Signals.},
journal = {Brain sciences},
volume = {15},
number = {4},
pages = {},
doi = {10.3390/brainsci15040359},
pmid = {40309849},
issn = {2076-3425},
abstract = {BACKGROUND/OBJECTIVES: The study explores the integration of human feedback into the control loop of mobile robots for real-time obstacle detection and avoidance using EEG brain-computer interface (BCI) methods. The goal is to assess the possible paradigms applicable to the most current navigation system to enhance safety and interaction between humans and robots.

METHODS: The research explores passive and active brain-computer interface (BCI) technologies to enhance a wheelchair-mobile robot's navigation. In the passive approach, error-related potentials (ErrPs), neural signals triggered when users comment or perceive errors, enable automatic correction of the robot navigation mistakes without direct input or command from the user. In contrast, the active approach leverages steady-state visually evoked potentials (SSVEPs), where users focus on flickering stimuli to control the robot's movements directly. This study evaluates both paradigms to determine the most effective method for integrating human feedback into assistive robotic navigation. This study involves experimental setups where participants control a robot through a simulated environment, and their brain signals are recorded and analyzed to measure the system's responsiveness and the user's mental workload.

RESULTS: The results show that a passive BCI requires lower mental effort but suffers from lower engagement, with a classification accuracy of 72.9%, whereas an active BCI demands more cognitive effort but achieves 84.9% accuracy. Despite this, task achievement accuracy is higher in the passive method (e.g., 71% vs. 43% for subject S2) as a single correct ErrP classification enables autonomous obstacle avoidance, whereas SSVEP requires multiple accurate commands.

CONCLUSIONS: This research highlights the trade-offs between accuracy, mental load, and engagement in BCI-based robot control. The findings support the development of more intuitive assistive robotics, particularly for disabled and elderly users.},
}

@article {pmid40309789,
year = {2025},
author = {Khan, S and Kallis, L and Mee, H and El Hadwe, S and Barone, D and Hutchinson, P and Kolias, A},
title = {Invasive Brain-Computer Interface for Communication: A Scoping Review.},
journal = {Brain sciences},
volume = {15},
number = {4},
pages = {},
doi = {10.3390/brainsci15040336},
pmid = {40309789},
issn = {2076-3425},
abstract = {BACKGROUND: The rapid expansion of the brain-computer interface for patients with neurological deficits has garnered significant interest, and for patients, it provides an additional route where conventional rehabilitation has its limits. This has particularly been the case for patients who lose the ability to communicate. Circumventing neural injuries by recording from the intact cortex and subcortex has the potential to allow patients to communicate and restore self-expression. Discoveries over the last 10-15 years have been possible through advancements in technology, neuroscience, and computing. By examining studies involving intracranial brain-computer interfaces that aim to restore communication, we aimed to explore the advances made and explore where the technology is heading.

METHODS: For this scoping review, we systematically searched PubMed and OVID Embase. After processing the articles, the search yielded 41 articles that we included in this review.

RESULTS: The articles predominantly assessed patients who had either suffered from amyotrophic lateral sclerosis, cervical cord injury, or brainstem stroke, resulting in tetraplegia and, in some cases, difficulty speaking. Of the intracranial implants, ten had ALS, six had brainstem stroke, and thirteen had a spinal cord injury. Stereoelectroencephalography was also used, but the results, whilst promising, are still in their infancy. Studies involving patients who were moving cursors on a screen could improve the speed of movement by optimising the interface and utilising better decoding methods. In recent years, intracortical devices have been successfully used for accurate speech-to-text and speech-to-audio decoding in patients who are unable to speak.

CONCLUSIONS: Here, we summarise the progress made by BCIs used for communication. Speech decoding directly from the cortex can provide a novel therapeutic method to restore full, embodied communication to patients suffering from tetraplegia who otherwise cannot communicate.},
}

@article {pmid40307763,
year = {2025},
author = {Zhang, L and Guan, X and Xue, H and Liu, X and Zhang, B and Liu, S and Ming, D},
title = {Sex-specific patterns in social visual attention among individuals with autistic traits.},
journal = {BMC psychiatry},
volume = {25},
number = {1},
pages = {440},
pmid = {40307763},
issn = {1471-244X},
support = {Grant Nos. 23JCZDJC01030//Natural Science Foundation of Tianjin (Key Program)/ ; 2022YGZD02//Tianjin Education Commission Research Program Project/ ; Grant Nos. 81925020//National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Autism is a neurodevelopmental condition more prevalent in males, with sex differences emerging in both prevalence and core symptoms. However, most studies investigating behavioral and cognitive features of autism tend to include more male samples, leading to a male-biased understanding. The sex imbalance limits the specificity of these features, especially in female individuals with autism. Hence, it is necessary to explore sex-related differences in behavioral-cognitive traits linked to autism in the general population.

METHODS: In this study, we designed a dynamic emotion-discrimination task to investigate sex differences in attention to emotional stimuli among the general population with autistic traits. Behavioral and eye movement data were recorded during the task, and the Autism-Spectrum Quotient (AQ) was used to assess autistic traits. Qualitative and quantitative methods were used to analyze gaze patterns in male and female groups. Additionally, correlation analyses were conducted to examine the relationship between AQ scores and proportion of fixation time in both groups.

RESULTS: Significant sex differences in attention to the eye regions of faces were observed, with females focusing more on the eyes than males. Correlation analyses revealed that, in males, lower eye-looking was associated with higher levels of autistic traits, whereas no such association was found in females.

CONCLUSIONS: Overall, these results reveal that attention patterns to emotional faces differed between females and males, and autistic traits predicted the trend of eye-looking in males. These findings suggest that sex-related stratification in social attention should be considered in clinical contexts.},
}

@article {pmid40307561,
year = {2025},
author = {, and Ferrante, O and Gorska-Klimowska, U and Henin, S and Hirschhorn, R and Khalaf, A and Lepauvre, A and Liu, L and Richter, D and Vidal, Y and Bonacchi, N and Brown, T and Sripad, P and Armendariz, M and Bendtz, K and Ghafari, T and Hetenyi, D and Jeschke, J and Kozma, C and Mazumder, DR and Montenegro, S and Seedat, A and Sharafeldin, A and Yang, S and Baillet, S and Chalmers, DJ and Cichy, RM and Fallon, F and Panagiotaropoulos, TI and Blumenfeld, H and de Lange, FP and Devore, S and Jensen, O and Kreiman, G and Luo, H and Boly, M and Dehaene, S and Koch, C and Tononi, G and Pitts, M and Mudrik, L and Melloni, L},
title = {Adversarial testing of global neuronal workspace and integrated information theories of consciousness.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {40307561},
issn = {1476-4687},
abstract = {Different theories explain how subjective experience arises from brain activity[1,2]. These theories have independently accrued evidence, but have not been directly compared[3]. Here we present an open science adversarial collaboration directly juxtaposing integrated information theory (IIT)[4,5] and global neuronal workspace theory (GNWT)[6-10] via a theory-neutral consortium[11-13]. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes and interpretation thereof[12]. Human participants (n = 256) viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography and intracranial electroencephalography. We found information about conscious content in visual, ventrotemporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results align with some predictions of IIT and GNWT, while substantially challenging key tenets of both theories. For IIT, a lack of sustained synchronization within the posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in the prefrontal cortex. These challenges extend to other theories of consciousness that share some of the predictions tested here[14-17]. Beyond challenging the theories, we present an alternative approach to advance cognitive neuroscience through principled, theory-driven, collaborative research and highlight the need for a quantitative framework for systematic theory testing and building.},
}

@article {pmid40306303,
year = {2025},
author = {Dinh, TH and Singh, AK and Doan, QM and Linh Trung, N and Nguyen, DN and Lin, CT},
title = {An EEG signal smoothing algorithm using upscale and downscale representation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add297},
pmid = {40306303},
issn = {1741-2552},
abstract = {OBJECTIVE: Effective smoothing of electroencephalogram (EEG) signals while maintaining the original signal's features is important in EEG signal analysis and brain-computer interface (BCI). This paper proposes a novel EEG signal-smoothing algorithm and its potential application in cognitive conflict processing.

APPROACH: Instead of being processed in the time domain, the input signal is visualized in increasing line width, the representation frame of which is converted into a binary image. An effective thinning algorithm is employed to obtain a unit-width skeleton as the smoothed signal.

MAIN RESULTS: Experimental results on data fitting have verified the effectiveness of the proposed approach on different levels of signal-to-noise (SNR) ratio, especially on high noise levels (SNR ≤ 5 dB), where our fitting error is only 86.4%-90.4% compared to that of its best counterpart. The potential application of the proposed algorithm in EEG-based cognitive conflict processing is comprehensively evaluated in a classification and a visual inspection task. The employment of the proposed approach in pre-processing the input data has significantly boosted the F1 score of state-of-the-art models by more than 1%. The robustness of our algorithm is also evaluated via a visual inspection task, where specific cognitive conflict peaks, i.e. the prediction error negativity (PEN) and error-related positive potential (Pe), can be easily observed at multiple line-width levels, while the insignificant ones are eliminated.

SIGNIFICANCE: These results demonstrated not only the advance of the proposed approach but also its impact on classification accuracy enhancement.},
}

@article {pmid40305243,
year = {2025},
author = {Yang, X and Li, Y and Zhang, J and Tian, H and Li, S and Pan, G},
title = {EvoMoE: Evolutionary Mixture-of-Experts for SSVEP-EEG classification with User-Independent Training.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3565882},
pmid = {40305243},
issn = {2168-2208},
abstract = {The analysis of EEG data in BCI systems captures unique individual characteristics, presenting diverse patterns that deviate from conventional identical distribution assumptions. Therefore, applying AI models directly to brain data becomes challenging due to the non-identical distribution issue. Meanwhile, as user numbers in BCI systems rise, scalable models are crucial to handle the growing data volume. Moreover, the limited availability of individual data necessitates the use of collective data for training, requiring models with strong generalization capabilities. To address these challenges, we propose Evolutionary Mixture of Experts (EvoMoE), a framework leveraging a set of diverse experts to model data from individuals. Users with similar distributions are grouped together, allowing experts to handle EEG data with different distribution types. The gating network of EvoMoE selects experts that closely match the distribution of the current sample, effectively tackling non-identical distribution issues. When encountering an unrecognized distribution, a new expert is introduced to accommodate the new data pattern, ensuring model adaptability. Evaluations on two 40-category BCI Speller datasets demonstrate significant performance improvements over state-of-the-art methods. On the BETA dataset, our online EvoMoE achieves 13.06% increase in accuracy and a 27.24-point increase in high information transfer rate (ITR) compared to the online UI method. The Bench dataset shows 3.64% increase in accuracy and a 10.42-point increase in ITR. These qualities make it a promising solution for practical BCI implementation, while setting the stage for the development of comprehensive biological big models.},
}

@article {pmid40302943,
year = {2025},
author = {Wu, X and Ye, Y and Sun, M and Mei, Y and Ji, B and Wang, M and Song, E},
title = {Recent Progress of Soft and Bioactive Materials in Flexible Bioelectronics.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0192},
pmid = {40302943},
issn = {2692-7632},
abstract = {Materials that establish functional, stable interfaces to targeted tissues for long-term monitoring/stimulation equipped with diagnostic/therapeutic capabilities represent breakthroughs in biomedical research and clinical medicine. A fundamental challenge is the mechanical and chemical mismatch between tissues and implants that ultimately results in device failure for corrosion by biofluids and associated foreign body response. Of particular interest is in the development of bioactive materials at the level of chemistry and mechanics for high-performance, minimally invasive function, simultaneously with tissue-like compliance and in vivo biocompatibility. This review summarizes the most recent progress for these purposes, with an emphasis on material properties such as foreign body response, on integration schemes with biological tissues, and on their use as bioelectronic platforms. The article begins with an overview of emerging classes of material platforms for bio-integration with proven utility in live animal models, as high performance and stable interfaces with different form factors. Subsequent sections review various classes of flexible, soft tissue-like materials, ranging from self-healing hydrogel/elastomer to bio-adhesive composites and to bioactive materials. Additional discussions highlight examples of active bioelectronic systems that support electrophysiological mapping, stimulation, and drug delivery as treatments of related diseases, at spatiotemporal resolutions that span from the cellular level to organ-scale dimension. Envisioned applications involve advanced implants for brain, cardiac, and other organ systems, with capabilities of bioactive materials that offer stability for human subjects and live animal models. Results will inspire continuing advancements in functions and benign interfaces to biological systems, thus yielding therapy and diagnostics for human healthcare.},
}

@article {pmid40302941,
year = {2025},
author = {Feng, C and Cao, L and Wu, D and Zhang, E and Wang, T and Jiang, X and Chen, J and Wu, H and Lin, S and Hou, Q and Zhu, J and Yang, J and Sawan, M and Zhang, Y},
title = {Acoustic Inspired Brain-to-Sentence Decoder for Logosyllabic Language.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0257},
pmid = {40302941},
issn = {2692-7632},
abstract = {Recent advances in brain-computer interfaces (BCIs) have demonstrated the potential to decode language from brain activity into sound or text, which has predominantly focused on alphabetic languages, such as English. However, logosyllabic languages, such as Mandarin Chinese, present marked challenges for establishing decoders that cover all characters, due to its unique syllable structures, extended character sets (e.g., over 50,000 characters for Mandarin Chinese), and complex mappings between characters and syllables, thus hindering practical applications. Here, we leverage the acoustic features of Mandarin Chinese syllables, constructing prediction models for syllable components (initials, tones, and finals), and decode speech-related stereoelectroencephalography (sEEG) signals into coherent Chinese sentences. The results demonstrate a high sentence-level offline decoding performance with a median character accuracy of 71.00% over the full spectrum of characters in the best participant. We also verified that incorporating acoustic-related features into the design of prediction models substantially enhances the accuracy of initials, tones, and finals. Moreover, our findings revealed that effective speech decoding also involves subcortical structures like the thalamus in addition to traditional language-related brain regions. Overall, we established a brain-to-sentence decoder for logosyllabic languages over full character set with a large intracranial electroencephalography dataset.},
}

@article {pmid40301357,
year = {2025},
author = {Ni, H and Yang, Y and Zhang, F and Sun, Y and Zheng, Y and Zhu, J and Xu, K},
title = {Dataset of long-term multi-site LFP activity with spontaneous chronic seizures in temporal lobe epilepsy rats.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {709},
pmid = {40301357},
issn = {2052-4463},
support = {82272112//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {The characteristics of refractory epilepsy change with disease progression. However, relevant studies are scarce due to the difficulty in obtaining long-term multi-site data from patients with epilepsy. This work aimed to provide a long-term brain electrophysiological dataset of 15 pilocarpine-treated rats with temporal lobe epilepsy (TLE). The dataset was constituted by multi-site local field potential (LFP) signal recorded from 12 sites in the Papez circuit in TLE, including spontaneous seizures and interictal fragments in the chronic period. The LFP data were saved in MATLAB, stored in the Neurodata Without Borders format, and published on the DANDI Archive. We validated the dataset technically through specific signal analysis. In addition, we provided MATLAB codes for basic analyses of this dataset, including power spectral analysis, seizure onset pattern identification, and interictal spike detection. This dataset could reveal how the electrophysiological and epileptic network properties of the brain of rats with chronic TLE changed during epilepsy development, thus help inform the design of adaptive neuromodulation for epilepsy.},
}

@article {pmid40301272,
year = {2025},
author = {Tan, Q and Jia, O and Anderson, BA and Jia, K and Gong, M},
title = {Reward history alters priority map based on spatial relationship, but not absolute location.},
journal = {Psychonomic bulletin & review},
volume = {},
number = {},
pages = {},
pmid = {40301272},
issn = {1531-5320},
abstract = {Attention is rapidly directed to stimuli associated with rewards in past experience, independent of current task goals and physical salience of stimuli. However, despite the robust attentional priority given to reward-associated features, studies often indicate negligible priority toward previously rewarded locations. Here, we propose a relational account of value-driven attention, a mechanism that relies on spatial relationship between items to achieve value-guided selections. In three experiments (N = 124), participants were trained to associate specific locations with rewards (e.g., high-reward: top-left; low-reward: top-right). They then performed an orientation-discrimination task where the target's absolute location (top-left or top-right) or spatial relationship ("left of" or "right of") had previously predicted reward. Performance was superior when the target's spatial relationship matched high-reward than low-reward, irrespective of absolute locations. Conversely, the impact of reward was absent when the target matched the absolute location but not the spatial relationship associated with high reward. Our findings challenge the default assumption of location specificity in value-driven attention, demonstrating a generalizable mechanism that humans adopted to integrate value and spatial information into priority maps for adaptive behavior.},
}

@article {pmid40300857,
year = {2025},
author = {Zhang, W and Pan, X and Wang, L and Li, W and Dai, X and Zheng, M and Guo, H and Chen, X and Xu, Y and Wu, H and He, Q and Yang, B and Ding, L},
title = {Selective BCL-2 inhibitor triggers STING-dependent antitumor immunity via inducing mtDNA release.},
journal = {Journal for immunotherapy of cancer},
volume = {13},
number = {4},
pages = {},
doi = {10.1136/jitc-2024-010889},
pmid = {40300857},
issn = {2051-1426},
abstract = {BACKGROUND: The stimulator of interferon genes (STING) signaling pathway has been demonstrated to propagate the cancer-immunity cycle and remodel the tumor microenvironment and has emerged as an appealing target for cancer immunotherapy. Interest in STING agonist development has increased, and the candidates hold significant promise; however, most are still in the early stages of human clinical trials. We found that ABT-199 activated the STING pathway to enhance the immunotherapeutic effect, and provided a ready-to-use small molecule drug for STING signaling activation.

METHODS: Phosphorylation of STING, TBK1, and IRF3, as well as activation of the interferon-I (IFN-I) signaling pathway, were detected following ABT-199 treatment in various colorectal cancer cells. C57BL/6J and BALB/c mice with subcutaneous tumors were employed to evaluate the in vivo therapeutic effects of the ABT-199 and anti-PD-L1 combination. Flow cytometry and ELISA were employed to analyze the level and activity of tumor-infiltrating T lymphocytes. Immunofluorescence and quantitative real-time PCR were conducted to assess the source and accumulation of double stranded DNA (dsDNA) in the cytoplasm. Chemical cross-linking assay, co-immunoprecipitation, and CRISPR/Cas9-mediated knockout were performed to investigate the molecular mechanism underlying ABT-199-induced voltage-dependent anion channel protein 1 (VDAC1) oligomerization and mitochondrial DNA (mtDNA) release.

RESULTS: ABT-199 significantly activated the STING signaling pathway in various colorectal cancer cells, which was evidenced by increased phosphorylation of TBK1 and IRF3, and upregulation of C-C motif chemokine ligand 5 (CCL5), C-X-C motif chemokine ligand 10 (CXCL10), and interferon beta transcription. By promoting chemokine expression and cytotoxic T-cell infiltration, ABT-199 promoted antitumor immunity and synergized with anti-PD-L1 therapy to improve antitumor efficacy. ABT-199 induced mtDNA accumulation in the cytoplasm and triggered STING signaling via the canonical pathway. cGAS or STING-KO models significantly abolished both STING signaling activation and the antitumor efficacy of ABT-199. Mechanically, ABT-199 promoted VDAC1 oligomerization by disturbing the binding between BCL-2 and VDAC1, thereby facilitating mtDNA release into the cytoplasm. ABT-199-triggered STING signaling was attenuated when VADC1 was knocked out. Consistently, the antitumor effect of ABT-199 in vivo was abolished in the absence of VDAC1.

CONCLUSIONS: Our results identify a ready-to-use small molecule compound for STING activation, reveal the underlying molecular mechanism through which ABT-199 activates the STING signaling pathway, and provide a theoretical basis for the use of ABT-199 in cancer immunotherapy.},
}

@article {pmid40297854,
year = {2025},
author = {Liu, X and Jin, X and Yun, L and Chen, Z},
title = {Prefrontal cortex activity during binocular color fusion and rivalry: an fNIRS study.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1527434},
pmid = {40297854},
issn = {1664-2295},
abstract = {INTRODUCTION: Understanding how the brain processes color information from both the left and right eyes is a significant topic in neuroscience. Binocular color fusion and rivalry, which involve advanced cognitive functions in the prefrontal cortex (PFC), provide a unique perspective for exploring brain activity.

METHODS: This study used functional near-infrared spectroscopy (fNIRS) to examine PFC activity during binocular color fusion and rivalry conditions. The study included two fNIRS experiments: Experiment 1 employed long-duration (90 s) stimulation to assess brain functional connectivity, while Experiment 2 used short-duration (10 s) repeated stimulation (eight trials), analyzed with a generalized linear model to evaluate brain activation levels. Statistical tests were then conducted to compare the differences in brain functional connectivity strength and activation levels.

RESULTS: The results indicated that functional connectivity strength was significantly higher during the color fusion condition than the color rivalry condition, and the color rivalry condition was stronger than the Mid-Gray field condition. Additionally, brain activation levels during binocular color fusion were significantly greater, with significant differences concentrated in channel (CH) 12, CH13, and CH14. CH12 is located in the dorsolateral prefrontal cortex, while CH13 and CH14 are in the frontal eye fields, areas associated with higher cognitive functions and visual attention.

DISCUSSION: These findings suggest that binocular color fusion requires stronger brain integration and higher brain activation levels. Overall, this study demonstrates that color fusion is more cognitively challenging than color rivalry, engaging more attention and executive functions. These results provide theoretical support for the development of color-based brain-computer interfaces and offer new insights into future research on the brain's color-visual information processing mechanisms.},
}

@article {pmid40297442,
year = {2025},
author = {Padmaja, GKR and Bhagat, NA and Balasubramani, PP},
title = {Assessing the utility of Fronto-Parietal and Cingulo-Opercular networks in predicting the trial success of brain-machine interfaces for upper extremity stroke rehabilitation.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.08.25325026},
pmid = {40297442},
abstract = {For stroke participants undergoing motor rehabilitation, brain-machine/computer interfaces (BMI/BCI) can potentially improve the efficacy of robotic or exoskeleton-based therapies by ensuring patient engagement and active participation, through monitoring of motor intent. In such interventions, exploring the network-level understanding of the source space, in terms of various cognitive dimensions such as executive control versus reward processing is fruitful in both improving the existing therapy protocols as well as understanding the subject-level differences. This contrasts to traditional approaches that predominantly investigate rehabilitation from resting state data. Moreover, conventional BMIs used for stroke rehabilitation barely accommodate people suffering from moderate to severe cognitive impairments. In this first-of-the-kind study, we explore the cognitive dimensions of a BMI trial by probing the networks that are core to the BMI performance and propose a network connectivity-based measurement with the potential to characterize the cognitive impairments in patients for closed-loop intervention. Specifically, we tease apart the extent of cognitive evaluation versus executive control aspects of impairments in these patients, by measuring the activation power of a major cognitive evaluation network-the Cingulo-Opercular Network (CON) and a major executive control circuit-the Fronto-Parietal network (FPN), and the connectivity between FPN-CON. We test our hypothesis in a previously collected dataset of electroencephalography (EEG) and structural imaging performed on stroke patients with upper limb impairments, while they underwent an exoskeleton-based BMI intervention for about 12 sessions over 4 weeks. Our logistic regression modeling results suggest that the connectivity between FPN and CON networks and their source powers predict trial failure accurately to about 84.2%. In the future, we aim to integrate these observations into a closed-loop design to adaptively control the cognitive difficulty and passively increase the subject's motivation and attention factor for effective BMI learning.},
}

@article {pmid40296528,
year = {2025},
author = {Yang, C and Wang, H and Wang, K and Cao, Z and Ren, F and Zhou, G and Chen, Y and Sun, B},
title = {Silk Fibroin-Based Biomemristors for Bionic Artificial Intelligence Robot Applications.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.5c02480},
pmid = {40296528},
issn = {1936-086X},
abstract = {In the emerging fields of flexible electronics and bioelectronics, protein-based materials have attracted widespread attention due to their biocompatibility, biodegradability, and processability. Among these materials, silk fibroin (SF), a protein derived from natural silk, has demonstrated significant potential in biomedical applications such as medical sensing and bone tissue engineering, as well as in the development of advanced biosensors. This is primarily due to its highly ordered β-sheet structure, mechanical properties, and processability. Furthermore, SF-based memristors provided a material choice for producing flexible wearable, and even implantable bioelectronic devices, which are expected to advance intelligent health monitoring, electronic skin (e-skin), brain-computer interface (BCI), and other frontier bioelectronic technologies. This review systematically summarizes the latest research progress in SF-based memristors concerning structural design, performance optimization, device integration, and application prospects, particularly highlighting their potential applications in neuromorphic computing and memristive sensors. Concurrently, we objectively analyzed the challenges currently faced by SF-based memristors and prospectively discussed their future development trends. This review provides a theoretical foundation and technological roadmap for biomaterials-based memristor devices, aiming to realize applications in flexible electronics and bioelectronics.},
}

@article {pmid40295498,
year = {2025},
author = {Fang, Z and Sims, CR},
title = {Humans learn generalizable representations through efficient coding.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3989},
pmid = {40295498},
issn = {2041-1723},
support = {2024M761999//China Postdoctoral Science Foundation/ ; },
abstract = {Reinforcement learning theory explains human behavior as driven by the goal of maximizing reward. Conventional approaches, however, offer limited insights into how people generalize from past experiences to new situations. Here, we propose refining the classical reinforcement learning framework by incorporating an efficient coding principle, which emphasizes maximizing reward using the simplest necessary representations. This refined framework predicts that intelligent agents, constrained by simpler representations, will inevitably: 1) distill environmental stimuli into fewer, abstract internal states, and 2) detect and utilize rewarding environmental features. Consequently, complex stimuli are mapped to compact representations, forming the foundation for generalization. We tested this idea in two experiments that examined human generalization. Our findings reveal that while conventional models fall short in generalization, models incorporating efficient coding achieve human-level performance. We argue that the classical RL objective, augmented with efficient coding, represents a more comprehensive computational framework for understanding human behavior in both learning and generalization.},
}

@article {pmid40294568,
year = {2025},
author = {Pan, L and Wang, K and Huang, Y and Sun, X and Meng, J and Yi, W and Xu, M and Jung, TP and Ming, D},
title = {Enhancing motor imagery EEG classification with a Riemannian geometry-based spatial filtering (RSF) method.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {188},
number = {},
pages = {107511},
doi = {10.1016/j.neunet.2025.107511},
pmid = {40294568},
issn = {1879-2782},
abstract = {Motor imagery (MI) refers to the mental simulation of movements without physical execution, and it can be captured using electroencephalography (EEG). This area has garnered significant research interest due to its substantial potential in brain-computer interface (BCI) applications, especially for individuals with physical disabilities. However, accurate classification of MI EEG signals remains a major challenge due to their non-stationary nature, low signal-to-noise ratio, and sensitivity to both external and physiological noise. Traditional classification methods, such as common spatial pattern (CSP), often assume that the data is stationary and Gaussian, which limits their applicability in real-world scenarios where these assumptions do not hold. These challenges highlight the need for more robust methods to improve classification accuracy in MI-BCI systems. To address these issues, this study introduces a Riemannian geometry-based spatial filtering (RSF) method that projects EEG signals into a lower-dimensional subspace, maximizing the Riemannian distance between covariance matrices from different classes. By leveraging the inherent geometric properties of EEG data, RSF enhances the discriminative power of the features while maintaining robustness against noise. The performance of RSF was evaluated in combination with ten commonly used MI decoding algorithms, including CSP with linear discriminant analysis (CSP-LDA), Filter Bank CSP (FBCSP), Minimum Distance to Riemannian Mean (MDM), Tangent Space Mapping (TSM), EEGNet, ShallowConvNet (sCNN), DeepConvNet (dCNN), FBCNet, Graph-CSPNet, and LMDA-Net, using six publicly available MI-BCI datasets. The results demonstrate that RSF significantly improves classification accuracy and reduces computational time, particularly for deep learning models with high computational complexity. These findings underscore the potential of RSF as an effective spatial filtering approach for MI EEG classification, providing new insights and opportunities for the development of robust MI-BCI systems. The code for this research is available at https://github.com/PLC-TJU/RSF.},
}

@article {pmid40292964,
year = {2025},
author = {Kasawala, E and Mouli, S},
title = {Dual-Mode Visual System for Brain-Computer Interfaces: Integrating SSVEP and P300 Responses.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {6},
pages = {},
doi = {10.3390/s25061802},
pmid = {40292964},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Event-Related Potentials, P300/physiology ; Electroencephalography/methods ; Algorithms ; Photic Stimulation ; Male ; Adult ; Signal Processing, Computer-Assisted ; Female ; Young Adult ; },
abstract = {In brain-computer interface (BCI) systems, steady-state visual-evoked potentials (SSVEP) and P300 responses have achieved widespread implementation owing to their superior information transfer rates (ITR) and minimal training requirements. These neurophysiological signals have exhibited robust efficacy and versatility in external device control, demonstrating enhanced precision and scalability. However, conventional implementations predominantly utilise liquid crystal display (LCD)-based visual stimulation paradigms, which present limitations in practical deployment scenarios. This investigation presents the development and evaluation of a novel light-emitting diode (LED)-based dual stimulation apparatus designed to enhance SSVEP classification accuracy through the integration of both SSVEP and P300 paradigms. The system employs four distinct frequencies-7 Hz, 8 Hz, 9 Hz, and 10 Hz-corresponding to forward, backward, right, and left directional controls, respectively. Oscilloscopic verification confirmed the precision of these stimulation frequencies. Real-time feature extraction was accomplished through the concurrent analysis of maximum Fast Fourier Transform (FFT) amplitude and P300 peak detection to ascertain user intent. Directional control was determined by the frequency exhibiting maximal amplitude characteristics. The visual stimulation hardware demonstrated minimal frequency deviation, with error differentials ranging from 0.15% to 0.20% across all frequencies. The implemented signal processing algorithm successfully discriminated between all four stimulus frequencies whilst correlating them with their respective P300 event markers. Classification accuracy was evaluated based on correct task intention recognition. The proposed hybrid system achieved a mean classification accuracy of 86.25%, coupled with an average ITR of 42.08 bits per minute (bpm). These performance metrics notably exceed the conventional 70% accuracy threshold typically employed in BCI system evaluation protocols.},
}

*Brain-Computer Interfaces
Humans
*Evoked Potentials, Visual/physiology
*Event-Related Potentials, P300/physiology
Electroencephalography/methods
Algorithms
Photic Stimulation
Male
Adult
Signal Processing, Computer-Assisted
Female
Young Adult

@article {pmid40292808,
year = {2025},
author = {Gao, D and Wang, Y and Fu, P and Qiu, J and Li, H},
title = {Modeling and Parameter Analysis of Basic Single Channel Neuron Mass Model for SSVEP.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {6},
pages = {},
doi = {10.3390/s25061706},
pmid = {40292808},
issn = {1424-8220},
mesh = {*Evoked Potentials, Visual/physiology ; Humans ; Brain-Computer Interfaces ; *Neurons/physiology ; *Models, Neurological ; Visual Cortex/physiology ; Electroencephalography ; Photic Stimulation ; },
abstract = {While steady-state visual evoked potentials (SSVEPs) are widely used in brain-computer interfaces (BCIs) due to their robustness to rhythmic visual stimuli, their generation mechanisms remain poorly understood. Challenges such as experimental complexity, inter-subject variability, and limited physiological interpretability hinder the development of efficient BCI systems. This study employed a single-channel neural mass model (NMM) of V1 cortical dynamics to investigate the biophysical underpinnings of SSVEP generation. By systematically varying synaptic gain, time constants, and external input parameters, we simulated δ/α/γ band oscillations and analyzed their generation principles. The model demonstrates that synaptic gain controls oscillation amplitude and harmonic content, and time constants determine signal decay kinetics and frequency precision, while input variance modulates harmonic stability. Our results reveal how V1 circuitry generates frequency-locked SSVEP responses through excitatory-inhibitory interactions and dynamic filtering mechanisms. This computational framework successfully reproduces fundamental SSVEP characteristics without requiring multi-subject experimental data, offering new insights into the physiological basis of SSVEP-based brain-computer interfaces.},
}

*Evoked Potentials, Visual/physiology
Humans
Brain-Computer Interfaces
*Neurons/physiology
*Models, Neurological
Visual Cortex/physiology
Electroencephalography
Photic Stimulation

@article {pmid40292646,
year = {2025},
author = {Jeon, Y and Kim, M and Song, KH},
title = {Development of Hydrogels Fabricated via Stereolithography for Bioengineering Applications.},
journal = {Polymers},
volume = {17},
number = {6},
pages = {},
doi = {10.3390/polym17060765},
pmid = {40292646},
issn = {2073-4360},
support = {Incheon National University (International Cooperative) Research Grant in 2020//Incheon National University/ ; },
abstract = {The architectures of hydrogels fabricated with stereolithography (SLA) 3D printing systems have played various roles in bioengineering applications. Typically, the SLA systems successively illuminated light to a layer of photo-crosslinkable hydrogel precursors for the fabrication of hydrogels. These SLA systems can be classified into point-scanning types and digital micromirror device (DMD) types. The point-scanning types form layers of hydrogels by scanning the precursors with a focused light, while DMD types illuminate 2D light patterns to the precursors to form each hydrogel layer at once. Overall, SLA systems were cost-effective and allowed the fabrication of hydrogels with good shape fidelity and uniform mechanical properties. As a result, hydrogel constructs fabricated with the SLA 3D printing systems were used to regenerate tissues and develop lab-on-a-chip devices and native tissue-like models.},
}

@article {pmid40292025,
year = {2025},
author = {Pawlak, WA and Howard, N},
title = {Neuromorphic algorithms for brain implants: a review.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1570104},
pmid = {40292025},
issn = {1662-4548},
abstract = {Neuromorphic computing technologies are about to change modern computing, yet most work thus far has emphasized hardware development. This review focuses on the latest progress in algorithmic advances specifically for potential use in brain implants. We discuss current algorithms and emerging neurocomputational models that, when implemented on neuromorphic hardware, could match or surpass traditional methods in efficiency. Our aim is to inspire the creation and deployment of models that not only enhance computational performance for implants but also serve broader fields like medical diagnostics and robotics inspiring next generations of neural implants.},
}

@article {pmid40290410,
year = {2025},
author = {Ng, JY},
title = {Exploring the intersection of brain-computer interfaces and traditional, complementary, and integrative medicine.},
journal = {Integrative medicine research},
volume = {14},
number = {2},
pages = {101142},
pmid = {40290410},
issn = {2213-4220},
abstract = {Brain-computer interfaces (BCIs) represent a transformative innovation in healthcare, enabling direct communication between the brain and external devices. This educational article explores the potential intersection of BCIs and traditional, complementary, and integrative medicine (TCIM). BCIs have shown promise in enhancing mind-body practices such as meditation, while their integration with energy-based therapies may offer novel insights and measurable outcomes. Emerging advancements, including artificial intelligence-enhanced BCIs, hold potential for improving personalization and expanding the therapeutic efficacy of TCIM interventions. Despite these opportunities, integrating BCIs with TCIM presents considerable ethical, cultural, and practical challenges. Concerns related to informed consent, cultural sensitivity, data privacy, accessibility, and regulatory frameworks must be addressed to ensure responsible implementation. Interdisciplinary collaboration among relevant stakeholders, including TCIM and conventional practitioners, researchers, and policymakers among other relevant stakeholders is crucial for developing integrative healthcare models that balance innovation with patient safety and respect for diverse healing traditions. Future directions include expanding evidence bases to validate TCIM practices through BCI-enhanced research, fostering equitable access to neurotechnological advancements, and promoting global ethical guidelines to navigate complex sociocultural dynamics. BCIs have the potential to revolutionize TCIM, offering novel solutions for complex health challenges and fostering a more inclusive, integrative approach to healthcare, provided that they are utilized responsibly and ethically.},
}

@article {pmid40289727,
year = {2025},
author = {Lin, X and Zhang, X and Chen, J and Liu, J},
title = {Material Selection and Device Design of Scalable Flexible Brain-Computer Interfaces: A Balance Between Electrical and Mechanical Performance.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e2413938},
doi = {10.1002/adma.202413938},
pmid = {40289727},
issn = {1521-4095},
support = {DMR-2011754//Directorate for Engineering/ ; },
abstract = {Brain-computer interfaces (BCIs) hold the potential to revolutionize brain function restoration, enhance human capability, and advance our understanding of cognitive mechanisms by directly linking neural signals with hardware. However, the mechanical mismatch between conventional rigid BCIs and soft brain tissue limits long-term interface stability. Next-generation BCIs must achieve long-term biocompatibility while maintaining high performance, enabling the integration of millions of sensors within tissue-level flexible and soft, stable neural interfaces. Lithographic fabrication techniques provide scalable thin-film flexible electronics, but traditional electronic materials often fail to meet the unique requirements of BCIs. This review examines the selection of materials and device design for flexible BCIs, starting with an analysis of intrinsic material properties-Young's modulus, electrical conductivity and dielectric constant. It then explores the integration of material selection with electrode design to optimize electrical circuits and assess key mechanical factors. Next, the correlation between electrical and mechanical performance is analyzed to guide material selection and device design. Finally, recent advances in neural probes are reviewed, highlighting improvements in signal quality, recording stability, and scalability. This review focuses on scalable, lithography-based BCIs, aiming to identify optimal materials and designs for long-term, reliable neural recordings.},
}

@article {pmid40289349,
year = {2025},
author = {Pitt, KM and Boster, JB},
title = {Identifying P300 brain-computer interface training strategies for AAC in children: a focus group study.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-10},
doi = {10.1080/07434618.2025.2495912},
pmid = {40289349},
issn = {1477-3848},
abstract = {The integration of Brain-Computer Interface (BCI) technology into Augmentative and Alternative Communication (AAC) systems introduces new complexities in training, particularly for children with diverse cognitive, sensory, motor, and linguistic abilities. Effective AAC training is crucial for enabling individuals to achieve personal goals and enhance social participation. This study aimed to explore potential training strategies for children using P300 based BCI-AAC systems through focus group discussions with experts in AAC and BCI technologies. Participants identified six key themes for effective training: (1) Scaffolding-developing adaptive systems tailored to each child's developmental level, including preteaching, visual display adaptations, and gamification; (2) Verbal Instructions-emphasizing the use of clear, simple language and spoken prompts; (3) Feedback-incorporating immediate feedback and biofeedback methods to reinforce learning; (4) Positioning-ensuring proper trunk stability and addressing electrode placement; (5) Modeling and Physical Supports-using physical cues and demonstrating BCI-AAC use; and (6) Considerations for Visual Impairment-accommodating cortical visual impairment (CVI) with suitable stimuli and environmental adjustments. These insights offer an initial foundation for identifying P300 BCI-AAC training strategies for children. Further systematic research with end users, support networks, and professionals is needed to validate, refine, and expand interventions that support diverse communication needs.},
}

@article {pmid40289107,
year = {2025},
author = {Zhi, Y and Guo, Y and Li, S and He, X and Wei, H and Laster, K and Wu, Q and Zhao, D and Xie, J and Ruan, S and Lemoine, NR and Li, H and Dong, Z and Liu, K},
title = {FBL promotes hepatocellular carcinoma tumorigenesis and progression by recruiting YY1 to enhance CAD gene expression.},
journal = {Cell death & disease},
volume = {16},
number = {1},
pages = {348},
pmid = {40289107},
issn = {2041-4889},
abstract = {Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Accumulating evidence suggests that epigenetic dysregulation contributes to the initiation and progression of HCC. We aimed to investigate key epigenetic regulators that contribute to tumorigenesis and progression, providing a theoretical basis for targeted therapy for HCC. We performed a comprehensive epigenetic analysis of differentially expressed genes in LIHC from the TCGA database. We identified fibrillarin (FBL), an rRNA 2'-O-methyltransferase, as an essential contributor to HCC. A series of in vitro and in vivo biological experiments were performed to investigate the potential mechanisms of FBL. FBL knockdown suppressed the proliferation of HCC cells. In vivo studies using cell-derived xenograft (CDX), patient-derived xenograft (PDX), and diethylnitrosamine (DEN)-induced HCC models in Fbl liver-specific knockout mice demonstrated the critical role of FBL in HCC carcinogenesis and progression. Mechanistically, FBL regulates the expression of CAD in HCC cells by recruiting YY1 to the CAD promoter region. We also revealed that fludarabine phosphate is a novel inhibitor of FBL and can inhibit HCC growth in vitro and in vivo. The antitumor activity of lenvatinib has been shown to be synergistically enhanced by fludarabine phosphate. Our study highlights the cancer-promoting role of the FBL-YY1-CAD axis in HCC and identifies fludarabine phosphate as a novel inhibitor of FBL. A schematic diagram depicting the FBL-YY1-CAD signaling pathway and its regulatory role in HCC progression.},
}

@article {pmid40288968,
year = {2025},
author = {Pan, L and Sun, X and Wang, K and Cao, Y and Xu, M and Ming, D},
title = {[Cross-session motor imagery-electroencephalography decoding with Riemannian spatial filtering and domain adaptation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {2},
pages = {272-279},
doi = {10.7507/1001-5515.202411035},
pmid = {40288968},
issn = {1001-5515},
abstract = {Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% (P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.},
}

@article {pmid40288455,
year = {2025},
author = {Tang, Y and Tang, Z and Zhou, Y and Luo, Y and Wen, X and Yang, Z and Jiang, T and Luo, N},
title = {A systematic review of resting-state functional-MRI studies in the diagnosis, comorbidity and treatment of postpartum depression.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jad.2025.04.142},
pmid = {40288455},
issn = {1573-2517},
abstract = {BACKGROUND: Postpartum depression (PPD) is a common and serious mental health problem that affects many new mothers and their families worldwide. In recent years, there has been an increasing number of studies using magnetic resonance techniques (MRI), particularly functional MRI (fMRI), to explore the neuroimaging biomarker of this disease.

METHODS: PubMed database was used to search for English literature focusing on resting-state fMRI and PPD published up to June 2024.

RESULTS: After screening, 17 studies were finally identified, among which all 17 studies reported abnormal regions or connectivity compared to health controls (HC), 4 studies reported results considering the differences between PPD and PPD with anxiety (PPD-A), and 2 studies reported biomarkers for the treatment of PPD. The existing studies indicate that PPD is characterized by functional impairments in multiple brain regions, especially the medial prefrontal cortex (MPFC), precentral gyrus and cerebellum. Abnormal functional connectivity has been widely reported in the dorsomedial prefrontal cortex (dmPFC), anterior cingulate cortex (ACC) and the orbitofrontal cortex (OFC). However, none of the four comorbidity studies identified overlapping discriminative biomarkers between PPD and PPD-A. Additionally, the two treatment-related studies consistently reported functional improvements in the amygdala after effective treatment.

CONCLUSION: The affected brain regions were highly overlapped with major depressive disorder (MDD), suggesting that PPD may be categorized as a potential subtype of MDD. Considering the negative effects of medication on PPD, future efforts should focus on developing non-pharmacological therapies, such as transcranial magnetic stimulation (TMS) and acupuncture, to support women with PPD in overcoming this unique and important phase.},
}

@article {pmid40288311,
year = {2025},
author = {Li, L and Jiang, C},
title = {Electrodeposited coatings for neural electrodes: A review.},
journal = {Biosensors & bioelectronics},
volume = {282},
number = {},
pages = {117492},
doi = {10.1016/j.bios.2025.117492},
pmid = {40288311},
issn = {1873-4235},
abstract = {Neural electrodes play a pivotal role in ensuring safe stimulation and high-quality recording for various bioelectronics such as neuromodulation devices and brain-computer interfaces. With the miniaturization of electrodes and the increasing demand for multi-functionality, the incorporation of coating materials via electrodeposition to enhance electrodes performance emerges as a highly effective strategy. These coatings not only substantially improve the stimulation and recording performance of electrodes but also introduce additional functionalities. This review began by outlining the application scenarios and critical requirements of neural electrodes. It then delved into the deposition principles and key influencing factors. Furthermore, the advancements in the electrochemical performance and adhesion stability of these coatings were reviewed. Ultimately, the latest innovative works in the electrodeposited coating applications were highlighted, and future perspectives were summarized.},
}

@article {pmid40287824,
year = {2025},
author = {Kumar, R and Waisberg, E and Ong, J and Lee, AG},
title = {The potential power of neuralink - how brain-machine interfaces can revolutionize medicine.},
journal = {Expert review of medical devices},
volume = {},
number = {},
pages = {},
doi = {10.1080/17434440.2025.2498457},
pmid = {40287824},
issn = {1745-2422},
}

@article {pmid40287725,
year = {2025},
author = {Zhang, X and Xie, L and Liu, W and Liang, S and Huang, L and Wang, M and Tian, L and Zhang, L and Liang, Z and Li, H and Huang, G},
title = {Exoskeleton-guided passive movement elicits standardized EEG patterns for generalizable BCIs in stroke rehabilitation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {97},
pmid = {40287725},
issn = {1743-0003},
support = {62201356//National Natural Science Foundation of China/ ; 62276169//National Natural Science Foundation of China/ ; 62271326//National Natural Science Foundation of China/ ; 2023SHIBS0003//Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions/ ; JCYJ20210324134401004//Shenzhen Science and Technology Innovation Program/ ; JCYJ20241202124222027//Shenzhen Science and Technology Innovation Program/ ; C2401028//Shenzhen Medical Research Foundation/ ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) hold significant potential for post-stroke motor recovery, yet active movement-based BCIs face limitations in generalization due to inter-subject variability. This study investigates passive movement-based BCIs, driven by exoskeleton-guided rehabilitation, to address these challenges by evaluating electroencephalogram (EEG) responses and algorithmic generalization in both healthy subjects and stroke patients.

METHODS: EEG signals were recorded from 20 healthy subjects and 10 stroke patients during voluntary and passive hand movements. Time and time-frequency domain analyses were performed to examine the event-related potential (ERP), event-related desynchronization (ERD), and synchronization (ERS) patterns. The performance of two BCI algorithms, Common Spatial Patterns (CSP) and EEGNet, was evaluated in both within-subject and cross-subject decoding tasks.

RESULTS: Time-domain and time-frequency analyses revealed that passive movements elicited stronger, more consistent ERPs in healthy subjects, particularly in bilateral motor cortices (contralateral: - 7.29 ± 4.51  μV; ipsilateral: - 4.33 ± 3.69  μV). Stroke patients exhibited impaired mu/beta ERD/ERS in the affected hemisphere during voluntary movements but demonstrated EEG patterns during passive movements resembling those of healthy subjects. Machine learning evaluation highlighted EEGNet's superior performance, achieving 84.19% accuracy in classifying affected vs. unaffected movements in patients, surpassing healthy subject left-right discrimination (58.38%). Cross-subject decoding further validated passive movement efficacy, with EEGNet attaining 86.00% (healthy) and 72.63% (stroke) accuracy, outperforming traditional CSP methods.

CONCLUSIONS: These findings underscore that passive movement elicits consistent neural responses, thereby enhancing the generalizability of decoding algorithms for stroke patients. By integrating exoskeleton-evoked proprioceptive feedback, this paradigm reduces inter-subject variability and improves clinical feasibility. Future work should explore the application of exoskeletons in the combination of active and passive movement for stroke rehabilitation.},
}

@article {pmid40281719,
year = {2025},
author = {Wan, X and Liu, Z and Yao, Y and Wan Hasan, WZ and Liu, T and Duan, D and Xie, X and Wen, D},
title = {Data Uncertainty (DU)-Former: An Episodic Memory Electroencephalography Classification Model for Pre- and Post-Training Assessment.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {4},
pages = {},
doi = {10.3390/bioengineering12040359},
pmid = {40281719},
issn = {2306-5354},
support = {62206014//National Natural Science Foundation of China/ ; 62276022//National Natural Science Foundation of China/ ; 2023YFF1203702//National Key Research and Development Program of China/ ; },
abstract = {Episodic memory training plays a crucial role in cognitive enhancement, particularly in addressing age-related memory decline and cognitive disorders. Accurately assessing the effectiveness of such training requires reliable methods to capture changes in memory function. Electroencephalography (EEG) offers an objective way of evaluating neural activity before and after training. However, EEG classification in episodic memory assessment remains challenging due to the variability in brain responses, individual differences, and the complex temporal-spatial dynamics of neural signals. Traditional EEG classification methods, such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), face limitations when applied to episodic memory training assessment, struggling to extract meaningful features and handle the inherent uncertainty in EEG signals. To address these issues, this paper introduces DU-former, which improves feature extraction and enhances the model's robustness against noise. Specifically, data uncertainty (DU) explicitly handles data uncertainty by modeling input features as Gaussian distributions within the reparameterization module. One branch predicts the mean through convolution and normalization, while the other estimates the variance via average pooling and normalization. These values are then used for Gaussian reparameterization, enabling the model to learn more robust feature representations. This approach allows the model to remain stable when dealing with complex or noisy data. To validate the method, an episodic memory training experiment was designed with 17 participants who underwent 28 days of training. Behavioral data showed a significant reduction in task completion time. Object recognition accuracy also improved, as indicated by the higher proportion of correctly identified target items in the episodic memory testing game. Furthermore, EEG data collected before and after the training were used to evaluate the DU-former's performance, demonstrating significant improvements in classification accuracy. This paper contributes by introducing uncertainty learning and proposing a more efficient and robust method for EEG signal classification, demonstrating superior performance in episodic memory assessment.},
}

@article {pmid40281692,
year = {2025},
author = {Acuña Luna, KP and Hernandez-Rios, ER and Valencia, V and Trenado, C and Peñaloza, C},
title = {Deep Learning-Enhanced Motor Training: A Hybrid VR and Exoskeleton System for Cognitive-Motor Rehabilitation.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {4},
pages = {},
doi = {10.3390/bioengineering12040331},
pmid = {40281692},
issn = {2306-5354},
abstract = {This research explored the integration of the real-time machine learning classification of motor imagery data with a brain-machine interface, leveraging prefabricated exoskeletons and an EEG headset integrated with virtual reality (VR). By combining these technologies, the study aimed to develop practical and scalable therapeutic applications for rehabilitation and daily motor training. The project showcased an optimized system designed to assess and train cognitive-motor functions in elderly individuals. Key innovations included a motor imagery EEG acquisition protocol for data classification and a machine learning framework leveraging deep learning with a wavelet packet transform for feature extraction. Comparative analyses were conducted with traditional models such as Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. The performance was further enhanced through a random hyperparameter search, optimizing feature extraction and learning parameters to achieve high classification accuracy (89.23%). A novel VR fishing game was developed to dynamically respond to EEG outputs, enabling the performance of interactive motor imagery tasks in coordination with upper limb exoskeleton arms. While clinical testing is ongoing, the system demonstrates potential for increasing ERD/ERS polarization rates in alpha and beta waves among elderly users after several weeks of training. This integrated approach offers a tangible step forward in creating effective, user-friendly solutions for motor function rehabilitation.},
}

@article {pmid40281628,
year = {2025},
author = {Atkinson, C and Lombardi, L and Lang, M and Keesey, R and Hawthorn, R and Seitz, Z and Leuthardt, EC and Brunner, P and Seáñez, I},
title = {Development and evaluation of a non-invasive brain-spine interface using transcutaneous spinal cord stimulation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {95},
pmid = {40281628},
issn = {1743-0003},
support = {K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; U24-NS109103/NH/NIH HHS/United States ; U24-NS109103/NH/NIH HHS/United States ; P41-EB018783/EB/NIBIB NIH HHS/United States ; P41-EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Humans ; Male ; Female ; Adult ; Electroencephalography ; *Spinal Cord Stimulation/methods ; *Brain-Computer Interfaces ; Middle Aged ; *Spinal Cord Injuries/rehabilitation/physiopathology ; Movement/physiology ; Young Adult ; *Transcutaneous Electric Nerve Stimulation/methods ; Sensorimotor Cortex/physiology ; Discriminant Analysis ; },
abstract = {Motor rehabilitation is a therapeutic process to facilitate functional recovery in people with spinal cord injury (SCI). However, its efficacy is limited to areas with remaining sensorimotor function. Spinal cord stimulation (SCS) creates a temporary prosthetic effect that may allow further rehabilitation-induced recovery in individuals without remaining sensorimotor function, thereby extending the therapeutic reach of motor rehabilitation to individuals with more severe injuries. In this work, we report our first steps in developing a non-invasive brain-spine interface (BSI) based on electroencephalography (EEG) and transcutaneous spinal cord stimulation (tSCS). The objective of this study was to identify EEG-based neural correlates of lower limb movement in the sensorimotor cortex of unimpaired individuals (N = 17) and to quantify the performance of a linear discriminant analysis (LDA) decoder in detecting movement onset from these neural correlates. Our results show that initiation of knee extension was associated with event-related desynchronization in the central-medial cortical regions at frequency bands between 4 and 44 Hz. Our neural decoder using µ (8-12 Hz), low β (16-20 Hz), and high β (24-28 Hz) frequency bands achieved an average area under the curve (AUC) of 0.83 ± 0.06 s.d. (n = 7) during a cued movement task offline. Generalization to imagery and uncued movement tasks served as positive controls to verify robustness against movement artifacts and cue-related confounds, respectively. With the addition of real-time decoder-modulated tSCS, the neural decoder performed with an average AUC of 0.81 ± 0.05 s.d. (n = 9) on cued movement and 0.68 ± 0.12 s.d. (n = 9) on uncued movement. Our results suggest that the decrease in decoder performance in uncued movement may be due to differences in underlying cortical strategies between conditions. Furthermore, we explore alternative applications of the BSI system by testing neural decoders trained on uncued movement and imagery tasks. By developing a non-invasive BSI, tSCS can be timed to be delivered only during voluntary effort, which may have implications for improving rehabilitation.},
}

Humans
Male
Female
Adult
Electroencephalography
*Spinal Cord Stimulation/methods
*Brain-Computer Interfaces
Middle Aged
*Spinal Cord Injuries/rehabilitation/physiopathology
Movement/physiology
Young Adult
*Transcutaneous Electric Nerve Stimulation/methods
Sensorimotor Cortex/physiology
Discriminant Analysis

@article {pmid40280929,
year = {2025},
author = {Bai, Y and Tang, Q and Zhao, R and Liu, H and Zhang, S and Guo, M and Guo, M and Wang, J and Wang, C and Xing, M and Ni, G and Ming, D},
title = {TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {701},
pmid = {40280929},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Reading ; *Semantics ; China ; *Language ; *Natural Language Processing ; East Asian People ; },
abstract = {Semantic understanding is central to advanced cognitive functions, and the mechanisms by which the brain processes language information are still being explored. Existing EEG datasets often lack natural reading data specific to Chinese, limiting research on Chinese semantic decoding and natural language processing. This study aims to construct a Chinese natural reading EEG dataset, TMNRED, for semantic target identification in natural reading environments. TMNRED was collected from 30 participants reading sentences sourced from public internet resources and media reports. Each participant underwent 400-450 trials in a single day, resulting in a dataset with over 10 hours of continuous EEG data and more than 4000 trials. This dataset provides valuable physiological data for studying Chinese semantics and developing more accurate Chinese natural language processing models.},
}

Humans
*Electroencephalography
*Reading
*Semantics
China
*Language
*Natural Language Processing
East Asian People

@article {pmid40280532,
year = {2025},
author = {Gazerani, P},
title = {The neuroplastic brain: current breakthroughs and emerging frontiers.},
journal = {Brain research},
volume = {},
number = {},
pages = {149643},
doi = {10.1016/j.brainres.2025.149643},
pmid = {40280532},
issn = {1872-6240},
abstract = {Neuroplasticity, the brain's capacity to reorganize itself by forming new neural connections, is central to modern neuroscience. Once believed to occur only during early development, research now shows that plasticity continues throughout the lifespan, supporting learning, memory, and recovery from injury or disease. Substantial progress has been made in understanding the mechanisms underlying neuroplasticity and their therapeutic applications. This overview article examines synaptic plasticity, structural remodeling, neurogenesis, and functional reorganization, highlighting both adaptive (beneficial) and maladaptive (harmful) processes across different life stages. Recent strategies to harness neuroplasticity, ranging from pharmacological agents and lifestyle interventions to cutting-edge technologies like brain-computer interfaces (BCIs) and targeted neuromodulation are evaluated in light of current empirical evidence. Contradictory findings in the literature are addressed, and methodological limitations that hamper widespread clinical adoption are discussed. The ethical and societal implications of deploying novel neuroplasticity-based interventions, including issues of equitable access, data privacy, and the blurred line between treatment and enhancement, are then explored in a structured manner. By integrating mechanistic insights, empirical data, and ethical considerations, the aim is to provide a comprehensive and balanced perspective for researchers, clinicians, and policymakers working to optimize brain health across diverse populations.},
}

@article {pmid40280369,
year = {2025},
author = {Zhang, Y and Gao, Y and Zhou, J and Zhang, Z and Feng, M and Liu, Y},
title = {Advances in Brain-Computer Interface Controlled Functional Electrical Stimulation for Upper Limb Recovery After Stroke.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111354},
doi = {10.1016/j.brainresbull.2025.111354},
pmid = {40280369},
issn = {1873-2747},
abstract = {Stroke often results in varying degrees of functional impairment, significantly affecting patients' quality of daily life. In recent years, brain-computer interface-controlled functional electrical stimulation has offered new therapeutic approaches for post-stroke rehabilitation. This paper reviews the application of BCI-FES in the recovery of upper limb function after stroke and explores its underlying mechanisms. By analyzing relevant studies, the aim is to provide a theoretical basis for rehabilitating upper limb function post-stroke, promote BCI-FES, and offer guidance for future clinical practice.},
}

@article {pmid40280291,
year = {2025},
author = {Han, MJ and Oh, Y and Ann, Y and Kang, S and Baeg, E and Hong, SJ and Sohn, H and Kim, SG},
title = {Whole-brain effective connectivity of the sensorimotor system using 7T fMRI with electrical microstimulation in non-human primates.},
journal = {Progress in neurobiology},
volume = {},
number = {},
pages = {102760},
doi = {10.1016/j.pneurobio.2025.102760},
pmid = {40280291},
issn = {1873-5118},
abstract = {The sensorimotor system is a crucial interface between the brain and the environment, and it is endowed with multiple computational mechanisms that enable efficient behaviors. For example, predictive processing via an efference copy of a motor command has been proposed as one of the key computations used to compensate for the sensory consequence of movement. However, the neural pathways underlying this process remain unclear, particularly regarding whether the M1-to-S1 pathway plays a dominant role in predictive processing and how its influence compares to that of other pathways. In this study, we present a causally inferable input-output map of the sensorimotor effective connectivity that we made by combining ultrahigh-field functional MRI, electrical microstimulation of the S1/M1 cortex, and dynamic causal modeling for the whole sensorimotor network in anesthetized primates. We investigated how motor signals from M1 are transmitted to S1 at the circuit level, either via direct cortico-cortical projections or indirectly via subcortical structures such as the thalamus. Across different stimulation conditions, we observed a robust asymmetric connectivity from M1 to S1 that was also the most prominent output from M1. In the thalamus, we identified distinct activations: M1 stimulation showed connections to the anterior part of ventral thalamic nuclei, whereas S1 was linked to the more posterior regions of the ventral thalamic nuclei. These findings suggest that the cortico-cortical projection from M1 to S1, rather than the cortico-thalamic loop, plays a dominant role in transmitting movement-related information. Together, our detailed dissection of the sensorimotor circuitry underscores the importance of M1-to-S1 connectivity in sensorimotor coordination.},
}

@article {pmid40280150,
year = {2025},
author = {Singer-Clark, T and Hou, X and Card, NS and Wairagkar, M and Iacobacci, C and Peracha, H and Hochberg, LR and Stavisky, S and Brandman, D},
title = {Speech motor cortex enables BCI cursor control and click.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add0e5},
pmid = {40280150},
issn = {1741-2552},
abstract = {Decoding neural activity from ventral (speech) motor cortex is known to enable high-performance speech brain-computer interface (BCI) control. It was previously unknown whether this brain area could also enable computer control via neural cursor and click, as is typically associated with dorsal (arm and hand) motor cortex. Approach. We recruited a clinical trial participant with ALS and implanted intracortical microelectrode arrays in ventral precentral gyrus (vPCG), which the participant used to operate a speech BCI in a prior study. We developed a cursor BCI driven by the participant's vPCG neural activity, and evaluated performance on a series of target selection tasks. Main results. The reported vPCG cursor BCI enabled rapidly-calibrating (40 seconds), accurate (2.90 bits per second) cursor control and click. The participant also used the BCI to control his own personal computer independently. Significance. These results suggest that placing electrodes in vPCG to optimize for speech decoding may also be a viable strategy for building a multi-modal BCI which enables both speech-based communication and computer control via cursor and click. (BrainGate2 ClinicalTrials.gov ID NCT00912041).},
}

@article {pmid40280132,
year = {2025},
author = {Zhou, N and Chen, J and Hu, M and Wen, N and Cai, W and Li, P and Zhao, L and Meng, Y and Zhao, D and Yang, X and Liu, S and Huang, F and Zhao, C and Feng, X and Jiang, Z and Xie, E and Pan, H and Cen, Z and Chen, X and Luo, W and Tang, B and Min, J and Wang, F and Yang, J and Xu, H},
title = {SLC7A11 is an unconventional H[+] transporter in lysosomes.},
journal = {Cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cell.2025.04.004},
pmid = {40280132},
issn = {1097-4172},
abstract = {Lysosomes maintain an acidic pH of 4.5-5.0, optimal for macromolecular degradation. Whereas proton influx is produced by a V-type H[+] ATPase, proton efflux is mediated by a fast H[+] leak through TMEM175 channels, as well as an unidentified slow pathway. A candidate screen on an orphan lysosome membrane protein (OLMP) library enabled us to discover that SLC7A11, the protein target of the ferroptosis-inducing compound erastin, mediates a slow lysosomal H[+] leak through downward flux of cystine and glutamate, two H[+] equivalents with uniquely large but opposite concentration gradients across lysosomal membranes. SLC7A11 deficiency or inhibition caused lysosomal over-acidification, reduced degradation, accumulation of storage materials, and ferroptosis, as well as facilitated α-synuclein aggregation in neurons. Correction of abnormal lysosomal acidity restored lysosome homeostasis and prevented ferroptosis. These studies have revealed an unconventional H[+] transport conduit that is integral to lysosomal flux of protonatable metabolites to regulate lysosome function, ferroptosis, and Parkinson's disease (PD) pathology.},
}

@article {pmid40280131,
year = {2025},
author = {Xin, Q and Wang, J and Zheng, J and Tan, Y and Jia, X and Ni, Z and Xu, Z and Feng, J and Wu, Z and Li, Y and Li, XM and Ma, H and Hu, H},
title = {Neuron-astrocyte coupling in lateral habenula mediates depressive-like behaviors.},
journal = {Cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cell.2025.04.010},
pmid = {40280131},
issn = {1097-4172},
abstract = {The lateral habenula (LHb) neurons and astrocytes have been strongly implicated in depression etiology, but it was not clear how the two dynamically interact during depression onset. Here, using multi-brain-region calcium photometry recording in freely moving mice, we discover that stress induces a most rapid astrocytic calcium rise and a bimodal neuronal response in the LHb. LHb astrocytic calcium requires the α1A-adrenergic receptor and depends on a recurrent neural network between the LHb and locus coeruleus (LC). Through the gliotransmitter glutamate and ATP/adenosine, LHb astrocytes mediate the second-wave LHb neuronal activation and norepinephrine (NE) release. Activation or inhibition of LHb astrocytic calcium signaling facilitates or prevents stress-induced depressive-like behaviors, respectively. These results identify a stress-induced positive feedback loop in the LHb-LC axis, with astrocytes being a critical signaling relay. The identification of this prominent neuron-glia interaction may shed light on stress management and depression prevention.},
}

@article {pmid40280000,
year = {2025},
author = {Jiang, Y and Zhou, C and Zhao, J and Ren, X and Wang, Q and Ni, P and Li, T},
title = {Generation and Characterization of a Human-Derived iPSC line from a female child with First-Episode of sporadic schizophrenia.},
journal = {Stem cell research},
volume = {86},
number = {},
pages = {103713},
doi = {10.1016/j.scr.2025.103713},
pmid = {40280000},
issn = {1876-7753},
abstract = {Schizophrenia is a highly heritable neurodevelopmental disorder. In this study, peripheral blood mononuclear cells (PBMCs) were obtained from a female child diagnosed with first-episode of sporadic schizophrenia. Induced pluripotent stem cells (iPSCs) were generated by introducing the reprogramming factors OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT. The iPSC line was confirmed through karyotyping and the expression of key pluripotency markers. These cells demonstrated the ability to differentiate into all three germ layers in vivo.},
}

@article {pmid40279233,
year = {2025},
author = {Aung, HW and Jiao Li, J and An, Y and Su, SW},
title = {A Real-Time Framework for EEG Signal Decoding With Graph Neural Networks and Reinforcement Learning.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3558171},
pmid = {40279233},
issn = {2162-2388},
abstract = {Brain-computer interfaces (BCIs) rely on accurately decoding electroencephalography (EEG) motor imagery (MI) signals for effective device control. Graph neural networks (GNNs) outperform convolutional neural networks (CNNs) in this regard, by leveraging the spatial relationships between EEG electrodes through adjacency matrices. The EEG graph lottery ticket framework, EEG_GLT-Net, featuring the state-of-the-art (SOTA) EEG_GLT adjacency matrix method, has notably enhanced EEG MI signal classification, evidenced by an average accuracy of 83.95% across 20 subjects on the PhysioNet dataset. This significantly exceeds the 76.10% accuracy rate achieved using the Pearson correlation coefficient (PCC) method in the same framework. In this research, we advance the field by applying a reinforcement learning (RL) approach to the classification of EEG MI signals. Our innovative method empowers the RL agent, enabling not only the classification of EEG MI data points with higher accuracy but effective identification of EEG MI data points that are less distinct. We present the EEG_RL-Net, an enhancement of the EEG_GLT-Net framework, which incorporates the trained EEG_GCN Block from EEG_GLT-Net at an adjacency matrix density of 13.39% alongside the RL-centric dueling deep Q network (Dueling DQN) block. The EEG_RL-Net model showcases exceptional classification performance, achieving an unprecedented average accuracy of 96.40% across 20 subjects within 25 ms. This model illustrates the transformative effect of the RL in EEG MI time point classification.},
}

@article {pmid40277624,
year = {2025},
author = {Zhang, W and Tang, X and Dang, X and Wang, M},
title = {A Capsule Decision Neural Network Based on Transfer Learning for EEG Signal Classification.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {4},
pages = {},
doi = {10.3390/biomimetics10040225},
pmid = {40277624},
issn = {2313-7673},
abstract = {Transfer learning is the act of using the data or knowledge in a problem to help solve different but related problems. In a brain computer interface (BCI), it is important to deal with individual differences between topics and/or tasks. A kind of capsule decision neural network (CDNN) based on transfer learning is proposed. In order to solve the problem of feature distortion caused by EEG feature extraction algorithm, a deep capsule decision network was constructed. The architecture includes multiple primary capsules to form a hidden layer, and the connection between the advanced capsule and the primary capsule is determined by the neural decision routing algorithm. Unlike the dynamic routing algorithm that iteratively calculates the similarity between primary capsules and advanced capsules, the neural decision network computes the relationship between each capsule in the deep and shallow hidden layers in a probabilistic manner. At the same time, the distribution of the EEG covariance matrix is aligned in Riemann space, and the regional adaptive method is further introduced to improve the independent decoding ability of the capsule decision neural network for the subject's EEG signals. Experiments on two motor imagery EEG datasets show that CDNN outperforms several of the most advanced transfer learning methods.},
}