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Reviving-like prosocial behavior in response to unconscious or dead conspecifics in rodents

Science (New York, N.Y.) 2025年第6736期387卷 eadq2677页

作者： Sun, Wenjian Zhang, Guang-Wei Huang, Junxiang J. Tao, Can Seo, Michelle B. Tao, Huizhong Whit Zhang, Li I. Center for Neural Circuits and Sensory Processing Disorders Zilkha Neurogenetic Institute Keck School of Medicine University of Southern California Los Angeles CA United States Graduate Program in Biomedical and Biological Sciences Keck School of Medicine University of Southern California Los Angeles CA United States Graduate Program in Neuroscience University of Southern California Los Angeles CA United States Department of Physiology and Neuroscience Keck School of Medicine University of Southern California Los Angeles CA United States

Whereas humans exhibit emergency responses to assist unconscious individuals, how nonhuman animals react to unresponsive conspecifics is less well understood. We report that mice exhibit stereotypic behaviors toward unconscious or dead social partners, which escalate from sniffing and grooming to more forceful actions such as mouth or tongue biting and tongue pulling. The latter intense actions, more prominent in familiar pairs, begin after prolonged immobility and unresponsiveness and cease when the partner regains activity. Their consequences, including improved airway opening and clearance and accelerated recovery from unconsciousness, suggest rescue-like efforts. Oxytocin neurons in the hypothalamic paraventricular nucleus respond differentially to the presence of unconscious versus active partners, and their activation, along with oxytocin signaling, is required for the reviving-like actions. This tendency to assist unresponsive members may enhance group cohesion and survival of social species.

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Efficacy of a Personalized Mobile Health Intervention (BedTime) to Increase Sleep Duration Among Short-Sleeping Patients With Type 2 Diabetes: Protocol for a Pilot Randomized Controlled Trial

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JMIR Research Protocols 2025年 14卷 e64023页

作者： Ban, Yuki Waki, Kayo Nakada, Ryohei Isogawa, Akihiro Miyoshi, Kengo Waki, Hironori Kato, Shunsuke Sawaki, Hideaki Murata, Takashi Hirota, Yushi Saito, Shuichiro Nishikage, Seiji Tone, Atsuhito Seno, Mayumi Toyoda, Masao Kajino, Shinichi Yokota, Kazuki Tsurutani, Yuya Yamauchi, Toshimasa Nangaku, Masaomi Ohe, Kazuhiko Professional Degree Program School of Public Health Graduate School of Medicine The University of Tokyo Tokyo Japan Department of Biomedical Informatics Graduate School of Medicine The University of Tokyo Tokyo Japan Department of Diabetes and Metabolic Diseases Graduate School of Medicine The University of Tokyo Tokyo Japan Division of Diabetes Mitsui Memorial Hospital Tokyo Japan Department of Metabolism and Endocrinology Akita University Graduate School of Medicine Akita Japan Center for Medical Education and Training Akita University Hospital Akita Japan Sawaki Internal Medicine and Diabetes Clinic Osaka Japan Division of Diabetes and Endocrinology Department of Internal Medicine Kobe University Graduate School of Medicine Kobe Japan Department of Internal Medicine Diabetes Center Okayama Saiseikai General Hospital Okayama Japan Division of Nephrology Endocrinology and Metabolism Department of Internal Medicine Tokai University School of Medicine Isehara Japan Aikawa Comprehensive Internal Medicine Clinic Nagoya Japan Yokota Medical Clinic Akashi Japan Endocrinology and Diabetes Center Yokohama Rosai Hospital Yokohama Japan Division of Nephrology and Endocrinology Graduate School of Medicine The University of Tokyo Tokyo Japan

Background: A strong association exists between sleep duration and glycemic control in patients with type 2 diabetes (T2D), yet convincing evidence of a causal link remains lacking. Improving sleep is increasingly emphasized in clinical T2D treatment guidance, highlighting the need for effective, scalable sleep interventions that can affordably serve large populations through mobile health (mHealth). Objective: This study aims to pilot an intervention that extends sleep duration by modifying bedtime behavior, assessing its efficacy among short-sleeping (≤6 hours per night) patients with T2D, and establishing robust evidence that extending sleep improves glycemic control. Methods: This randomized, single-blinded, multicenter study targets 70 patients with T2D from 9 institutions in Japan over a 12-week intervention period. The sleep extension intervention, BedTime, is developed using the Theory of Planned Behavior (TPB) and focuses on TPB’s constructs of perceived and actual behavioral control (ABC). The pilot intervention combines wearable actigraphy devices with SMS text messaging managed by human operators. Both the intervention and control groups will use an actigraphy device to record bedtime, sleep duration, and step count, while time in bed (TIB) will be assessed via sleep diaries. In addition, the intervention group will receive weekly bedtime goals, daily feedback on their bedtime performance relative to those goals, identify personal barriers to an earlier bedtime, and select strategies to overcome these barriers. The 12-week intervention period will be followed by a 12-week observational period to assess the sustainability of the intervention’s effects. The primary outcome is the between-group difference in the change in hemoglobin A_1c (HbA_1c) at 12 weeks. Secondary outcomes include other health measures, sleep metrics (bedtime, TIB, sleep duration, total sleep time, and sleep quality), behavioral changes, and assessments of the inter

关键词： behavior change digital therapeutics randomized controlled trial sleep duration Theory of Planned Behavior type 2 diabetes

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Do Sites Benefit Equally from Distributed Learning in Medical Image Analysis? 2nd

Do Sites Benefit Equally from Distributed Learning in Medi...

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2nd International Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and 3rd International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with the International Conference on Medical Image Computing and Computer Assisted Interventions, MICCAI 2024

作者： Souza, Raissa Stanley, Emma A. M. Camicioli, Richard Monchi, Oury Ismail, Zahinoor Wilms, Matthias Forkert, Nils D. Department of Radiology Cumming School of Medicine University of Calgary Calgary Canada Hotchkiss Brain Institute University of Calgary Calgary Canada Biomedical Engineering Graduate Program University of Calgary Calgary Canada Alberta Children’s Hospital Research Institute University of Calgary Calgary Canada University of Alberta Edmonton Canada Department of Radiology Radio-oncology and Nuclear Medicine Université de Montréal Montréal Canada Centre de Recherche Institut Universitaire de Gériatrie de Montréal Montréal Canada Department of Clinical Neurosciences Cumming School of Medicine University of Calgary Calgary Canada Department of Psychiatry University of Calgary Calgary Canada Clinical and Biomedical Sciences Faculty of Health and Life Sciences University of Exeter Exeter United Kingdom Department of Pediatrics University of Calgary Calgary Canada Department of Community Health Sciences University of Calgary Calgary Canada

ISBN: (纸本)9783031727863

Artificial intelligence (AI) has the potential to make medical image analysis more accessible to healthcare institutions worldwide. However, when trained on multi-site datasets, models may excel with data from certain institutions but struggle with others, even when exposed to their training data. This emphasizes the importance of investigating whether all sites benefit from AI models, especially within distributed learning setups. Distributed learning methods allow access to broader and more diverse datasets from multiple sites during training. In this context, the travelling model (TM) paradigm has demonstrated superior performance in limited data scenarios, making it particularly relevant in low-resource settings. This work investigates whether all sites can benefit from TM development and identifies the factors influencing performance disparities. Specifically, a Parkinson’s disease (PD) database comprising 1,817 neuroimaging datasets from 83 different sites is utilized to investigate how site-specific and participant-specific factors influence the performance of TM in classifying PD. Therefore, we analyze the false positive rate (FPR) and false negative rate (FNR) to identify the characteristics contributing to misdiagnosis. Our findings reveal disparities in benefits across sites, with class imbalance emerging as the major factor influencing FPR and FNR, especially for sites with more PD cases. This research underscores the urgency of a rigorous analysis of a model’s behaviour in distributed setups to detect misdiagnosis risks and encourage developers to evaluate and optimize models beyond overall accuracy. Thus, comprehensive analyses of this type can help pave the way for the development of more equitable AI-based medical imaging models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Survey and Improvement Strategies for Gene Prioritization with Large Language Models

arXiv

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arXiv 2025年

作者： Neeley, Matthew B. Qi, Guantong Wang, Guanchu Tang, Ruixiang Mao, Dongxue Liu, Chaozhong Pasupuleti, Sasidhar Yuan, Bo Xia, Fan Liu, Pengfei Liu, Zhandong Hu, Xia Jan and Dan Duncan Neurological Research Institute Texas Children’s Hospital Houston United States Graduate School of Biomedical Sciences Program in Quantitative and Computational Biosciences Baylor College of Medicine Houston United States Graduate School of Biomedical Sciences Program in Genetics and Genomics Baylor College of Medicine Houston United States Department of Computer Science Rice University Houston United States Department of Computer Science Rutgers University NJ United States Department of Pediatrics Baylor College of Medicine Houston United States Department of Molecular and Human Genetics Baylor College of Medicine Houston United States Human Genome Sequencing Center Baylor College of Medicine Houston United States Baylor Genetics Houston United States

Background Rare diseases can be difficult to diagnose due to limited patient data and broad genetic diversity. Despite the advances in variant prioritization tools, many rare disease cases remain undiagnosed. While large language models (LLMs) have performed well in medical exams, their accuracy in diagnosing rare genetic diseases has not yet been evaluated. Methods To identify causal genes for genetic diseases, we benchmarked various LLMs for gene prioritization. We employed multi-agent and Human Phenotype Ontology (HPO) classification approaches to identify patient case groups based on phenotypes categorizing them into different levels of solvability. To address LLM limitations in ranking large gene sets, we used a divide-and-conquer strategy to break down the ranking task into smaller subsets. Mini-batching inputs and limiting the number of generated tokens improved efficiency. Results In its vanilla form, GPT-4 consistently outperformed the other LLMs with an accuracy around 30%. Multi-agent and HPO classification approaches aided us in distinguishing between confidently-solved and challenging cases. In addition, we observed bias towards well-studied genes and input order sensitivity which are drawbacks of LLMs in disease-causal gene prioritization. Our divide-and-conquer strategy enhanced accuracy by overcoming positional and gene frequency biases in literature. This framework, based on benchmarking insights and novel techniques, significantly optimizes the overall process when identifying disease-causal genes for genetic diseases when compared to our baseline evaluation, and thereby better enables the development of targeted diagnostic and therapeutic interventions. Conclusions Using HPO classification and our novel multi-agent techniques, and our LLM divide-and-conquer strategy (1) highlighted the importance of accounting for differences in patient case solvability and (2) yielded improved performance in identifying causal genes for rare diseases when compare

关键词： Gene Ontology

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Early-Life Risks of Central Precocious Puberty

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Endocrine Practice 2025年

作者： Yin, Wei-Jou Hsu, Jhih-Wei Chen, Chun-Chang Su, Emily Chia-Yu Wang, San-Yuan Chen, Yan-Jen Chen, Yang-Ching College of Medicine Taipei Medical University Taipei Taiwan Department of Family Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Master Program in Clinical Genomics and Proteomics Department of Pharmaceutical Sciences Taipei Medical University Taipei Taiwan Department of Family Medicine Wan Fang Hospital Taipei Medical University Taipei Taiwan Nutrition Research Center Taipei Medical University Hospital Taipei Taiwan

Objectives: To investigate the factors in early life that may contribute to central precocious puberty (CPP). Methods: The study utilized data from the Taiwan Puberty Longitudinal Study, including 2241 children under pubertal assessment and a questionnaire of risk factors. We analyzed associations using the Fitting Generalized Linear Models in R (R Core Team, 2023), with R studio (Posit, 2023) version 4.3.1. Results: Among the 2241 children examined, 745 had CPP. Overall, higher gestational weight gain (GWG) increased the risk of CPP (odds ratio [OR]: 1.03, 95% CI: 1.01-1.05);while higher GWG served as a protective factor in females (OR: 0.97, 95% CI: 0.95-0.99). Maternal gestational diabetes mellitus (GDM) also increased the CPP risk, particularly in males (OR: 2.66, 95% CI: 1.00-7.25). Longer exclusive breastfeeding was linked to lower CPP risk overall (OR: 0.96, 95% CI: 0.93-1.00) but was not significant when analyzed by gender. Conclusions: Higher maternal GWG was associated with an increased risk of CPP overall, while GWG became negatively associated with CPP, only in females. Maternal GDM was linked to a higher risk of CPP, particularly in males. The duration of exclusive breastfeeding was inversely correlated with CPP risk, but this effect was not significant when analyzed by gender. © 2025 AACE

关键词： central precocious puberty early-life risk factors pediatrics

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Leveraging undecided cases in chart-reviewed phenotypes to enhance EHR-based association studies

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Journal of biomedical informatics 2025年 166卷 104839页

作者： Jian, Xinyao Zhang, Dazheng Yu, Zehao Xu, Hua Bian, Jiang Wu, Yonghui Tong, Jiayi Chen, Yong University of Pennsylvania Perelman School of Medicine PhiladelphiaPA United States Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida GainesvilleFL United States Department of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States Department of Biostatistics and Health Data Science School of Medicine Indiana University IndianapolisIN United States Regenstreif Institute Indianapolis IndianaIN United States Cancer Informatics Shared Resource University of Florida Health Cancer Center GainesvilleFL United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States The Graduate Group in Applied Mathematics and Computational Science School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Leonard Davis Institute of Health Economics PhiladelphiaPA United States PhiladelphiaPA United States PhiladelphiaPA United States

Objectives: In electronic health record (EHR)-based association studies, phenotyping algorithms efficiently classify patient clinical outcomes into binary categories but are susceptible to misclassification errors. The gold standard, manual chart review, involves clinicians determining the true disease status based on their assessment of health records. These clinicians-labeled phenotypes are labor-intensive and typically limited to a small subset of patients, potentially introducing a third "undecided" category when phenotypes are indeterminate. We aim to effectively integrate the algorithm-derived and chart-reviewed outcomes when both are available in EHR-based association studies. Material and Methods: We propose an augmented estimation method that combines the binary algorithm-derived phenotypes for the entire cohort with the trinary chart-reviewed phenotypes for a small, selected subset. Additionally, a cost-effective outcome-dependent sampling strategy is used to address the rare disease scenarios. The proposed trinary chart-reviewed phenotype integrated cost-effective augmented estimation (TriCA) was evaluated across a wide range of simulation settings and real-world applications, including using EHR data on Alzheimer's disease and related dementias (ADRD) from the OneFlorida + Clinical Research Network, and using cohort data on second breast cancer events (SBCE) from the Kaiser Permanente Washington. Results: Compared to estimation based on random sampling, our augmented method improved mean square error by up to 28.3% in simulation studies;compared to estimation using only trinary chart-reviewed phenotypes, our method improved efficiency by up to 33.3% in ADRD data and 50.8% in SBCE data. Discussion: Our simulation studies and real-world applications demonstrate that, compared to existing methods, the proposed method provides unbiased estimates with higher statistical efficiency. Conclusion: The proposed method effectively combined binary algorithm-derived

关键词： Clinical research

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Using machine learning to investigate the influence of the prenatal chemical exposome on neurodevelopment of young children

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NeuroToxicology 2025年 108卷 218-230页

作者： England-Mason, Gillian MacEachern, Sarah J. Amador, Kimberly Soomro, Munawar Hussain Reardon, Anthony J.F. MacDonald, Amy M. Kinniburgh, David W. Letourneau, Nicole Giesbrecht, Gerald F. Martin, Jonathan W. Forkert, Nils D. Dewey, Deborah Department of Pediatrics Cumming School of Medicine University of Calgary Calgary AB Canada Alberta Children's Hospital Research Institute University of Calgary Calgary AB Canada Hotchkiss Brain Institute University of Calgary Calgary AB Canada Department of Psychology Faculty of Arts University of Calgary Calgary AB United States Biomedical Engineering Graduate Program University of Calgary Calgary AB Canada Department of Radiology Cumming School of Medicine University of Calgary Calgary AB Canada Department of Laboratory Medicine and Pathology University of Alberta Edmonton AB Canada Alberta Centre for Toxicology University of Calgary Calgary AB Canada Faculty of Nursing University of Calgary Calgary AB Canada Department of Community Health Sciences Cumming School of Medicine University of Calgary Calgary AB Canada Department of Psychiatry Cumming School of Medicine University of Calgary Calgary AB Canada Science for Life Laboratory Department of Environmental Science Stockholm University Stockholm Sweden Department of Clinical Neurosciences University of Calgary Calgary AB Canada

Research investigating the prenatal chemical exposome and child neurodevelopment has typically focused on a limited number of chemical exposures and controlled for sociodemographic factors and maternal mental health. Emerging machine learning approaches may facilitate more comprehensive examinations of the contributions of chemical exposures, sociodemographic factors, and maternal mental health to child neurodevelopment. A machine learning pipeline that utilized feature selection and ranking was applied to investigate which common prenatal chemical exposures and sociodemographic factors best predict neurodevelopmental outcomes in young children. Data from 406 maternal-child pairs enrolled in the APrON study were used. Maternal concentrations of 32 environmental chemical exposures (i.e., phthalates, bisphenols, per- and polyfluoroalkyl substances (PFAS), metals, trace elements) measured during pregnancy and 11 sociodemographic factors, as well as measures of maternal mental health and urinary creatinine were entered into the machine learning pipeline. The pipeline, which consisted of a RReliefF variable selection algorithm and support vector machine regression model, was used to identify and rank the best subset of variables predictive of cognitive, language, and motor development outcomes on the Bayley Scales of Infant Development-Third Edition (Bayley-III) at 2 years of age. Bayley-III cognitive scores were best predicted using 29 variables, resulting in a correlation coefficient of r = 0.27 (R²=0.07). For language outcomes, 45 variables led to the best result (r = 0.30;R²=0.09), whereas for motor outcomes 33 variables led to the best result (r = 0.28, R²=0.09). Environmental chemicals, sociodemographic factors, and maternal mental health were found to be highly ranked predictors of cognitive, language, and motor development in young children. Our findings demonstrate the potential of machine learning approaches to identify and dete

关键词： APrON Developmental neurotoxicology Epidemiology Infant and child neurotoxicity studies Neurobehavioural Testing

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The effect of stereochemical constraints on the radius of gyration of folded proteins

arXiv

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arXiv 2025年

作者： Logan, Jack A. Sumner, Jacob Grigas, Alex T. Shattuck, Mark D. O'Hern, Corey S. Department of Mechanical Engineering Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Biomedical Informatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department The City College of New York New YorkNY10031 United States Department of Applied Physics Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States

Proteins are composed of linear chains of amino acids that fold into complex three-dimensional structures. A general feature of folded proteins is that they are compact with a radius of gyration Rg(N) ∼ Nν that obeys power-law scaling with the number of amino acids N in each protein and ν ∼ 1/3. In this study, we investigate the internal scaling of the radius of gyration Rg(n) versus the chemical separation n between amino acids for all subchains of length n. We show that for globular proteins Rg(n) ∼ nν1,2 with a larger exponent ν1 > 1/3 for small n and a smaller exponent ν2 g(N) ∝ Nν. To describe this scaling behavior for Rg(n), we carry out folding simulations for a series of coarse-grained models for proteins beginning with the freely-jointed and freely-rotating chain models composed of spherical monomers and varying degrees of stereochemical constraints. We show that a minimal model, which coarse-grains amino acids into a single spherical backbone atom and one variable-sized spherical side-chain atom, and enforces bend- and dihedral-angle constraints, can recapitulate Rg(n) for x-ray crystal structures of globular proteins. In addition, this model predicts the correct average packing fraction and size of the hydrophobic core, which are two key physical features that can be used to distinguish between computational 'decoys' and correctly folded proteins in protein design applications. © 2025, CC BY.

关键词： Spheres

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Combination of antimicrobial peptide and clinical antibiotic shows enhanced potency toward Acinetobacter baumannii infection

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European Journal of Pharmacology 2025年 999卷

作者： Yeh, Jih-Chao Yang, Chin-Hao Hazam, Prakash Kishore Selvaraj, Sanjay Prasad Lin, You-Ying Hsieh, Chu-Yi Liou, Je-Wen Chen, Jyh-Yih Marine Research Station Institute of Cellular and Organismic Biology Academia Sinica 23-10 Dawen Rd. Jiaushi Ilan 262 Taiwan Department of Biomedical Sciences and Engineering College of Medicine Tzu Chi University Hualien 970 Taiwan Molecular and Biological Agricultural Science Program Taiwan International Graduate Program Academia Sinica Taipei 11529 Taiwan Graduate Institute of Biotechnology National Chung Hsing University Taichung 402 Taiwan The IEGG and Animal Biotechnology Center and the Rong Hsing Research Center for Translational Medicine National Chung Hsing University Taichung 402 Taiwan

The continued development of novel antimicrobial treatment strategies is crucial for maintaining an effective therapeutic arsenal, and antimicrobial peptides (AMPs) exhibit promising activities against a wide range of pathogens. In this study, we tested the combined effects of an AMP, TP4-3, and meropenem on Acinetobacter baumannii, which is responsible for many severe infections and is associated with high rates of overall mortality and morbidity. This study aimed to develop an effective combination therapy for microbial infections. TP4-3 and meropenem were found to act synergistically toward Acinetobacter baumannii and exhibit an activity profile better than those of the individual compounds. TP4-3 is an antimicrobial peptide with proven activity, low toxicity and extended stability. This AMP was combined with meropenem and tested for efficacy against A. baumannii using a panel of in vitro and in vivo tests. The combination of TP4-3 and meropenem exhibited robust activity against Acinetobacter baumannii pathogens. In particular, the combined treatment demonstrated significant antibiofilm properties and a lower degree of induced resistance than meropenem alone. Additionally, the combination showed an excellent activity profile in in vivo studies. Thus, the combination of TP4-3 and meropenem appears to be an effective strategy to mitigate the detrimental consequences of infections caused by this clinically relevant pathogen. Since the combination of TP4-3 and meropenem displayed better activity than the individual compounds, this strategy of combining AMPs with clinical antibiotics may be suitable for development of clinical treatments targeting various microbial infections. © 2025 Elsevier B.V.

关键词： Acinetobacter baumannii Antimicrobial peptide Gram-negative bacteria In vivo antimicrobial activity Stapled peptide Synergism

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Asymmetric Modulation of Brain Connectivity by Anodal Transcranial Direct Current Stimulation in Healthy Individuals: A Single-Blind, Randomized Sham-Controlled Trial

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Human Brain Mapping 2025年第7期46卷 e70218页

作者： Carther-Krone, Tiffany McAllister, Zachary A. Choi, Eun Hyung Ryner, Lawrence Ko, Ji Hyun Department of Human Anatomy and Cell Science Max Rady College of Medicine University of Manitoba Winnipeg Canada PrairieNeuro Research Centre Kleysen Institute for Advanced Medicine Winnipeg Health Science Centre Winnipeg Canada Department of Radiology Max Rady College of Medicine University of Manitoba Winnipeg Canada Graduate Program in Biomedical Engineering Price Faculty of Engineering University of Manitoba Winnipeg Canada

Transcranial direct current stimulation (tDCS) applied to the dorsolateral prefrontal cortex (DLPFC) has shown asymmetric behavioral effects, though the underlying neurophysiological mechanisms remain unclear. In this preliminary study with 34 healthy individuals, tDCS was applied to either the left or right DLPFC or a sham group. Behavioral and neurophysiological changes were examined by the Stroop test and resting-state fMRI, respectively, which were measured before and after a 15-min tDCS session. Seed-to-voxel connectivity analysis with seeds placed under the tDCS target regions (F3 and F4) showed no significant changes, but voxel-to-voxel whole-brain intrinsic connectivity (IC) analysis revealed significant 3 × 2 interaction effects (stimulation site × time) in the right DLPFC (18 mm off from the F4). Post hoc analysis showed that only the right DLPFC stimulation led to an increase in IC from pre- to post-stimulation. Consistent with this finding, right DLPFC stimulation improved Stroop task performance measured by increased interference score, which represents better inhibition of irrelevant information. These findings provide further insights into the hemispheric difference of tDCS effects and its underlying neurophysiological mechanisms. However, the small sample size limits the generalizability of the results and necessitates further research with a larger cohort for confirmation. © 2025 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.

关键词： dorsolateral prefrontal cortex fMRI interference score intrinsic connectivity Stroop task transcranial direct current stimulation

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