Frontiers in Neuroinformatics最新文献

{"title":"ABCDscores: an R package for computing summary scores in the ABCD Study®.","authors":"Le Zhang, Olivier Celhay, Biplabendu Das, Samantha Berman, Laura R Ziemer, Calen J Smith, Shermaine Abad, Anders M Dale, Janosch Linkersdörfer","doi":"10.3389/fninf.2026.1858950","DOIUrl":"10.3389/fninf.2026.1858950","url":null,"abstract":"<p><p>The ABCD Study® is the largest longitudinal research study of brain development and child health across the United States. The study generates a rich multimodal dataset, including file-based raw and derivative data and a curated tabulated dataset comprising data from a broad spectrum of research domains. Historically, summary scores in the tabulated dataset were computed using the electronic data capture system REDCap or external scripting solutions. However, this approach was rigid, unreliable, and lacked transparency around how scores were computed. Here, we introduce ABCDscores, an R software package that is used by the study to compute most non-proprietary summary scores included in the tabulated ABCD data resource and allows for customization to meet specific research needs. ABCDscores provides a flexible, open-source solution that uses R for transparent and reproducible scoring. The package is built to apply standard scoring methods across different types of assessments, ensuring consistent and accurate results even with large datasets. By simplifying complex scoring procedures into a user-friendly process, ABCDscores streamlines research workflows, reduces errors, and saves researchers valuable time. The package aims to improve analyses of data from the ABCD Study® and can serve as a model for the development of similar tools and a standardized framework for other research studies, fostering new discoveries and innovation in the field.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1858950"},"PeriodicalIF":2.9,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13522172/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148850250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG-fNIRS self-supervised emotion recognition method based on dual-stream Siamese network.","authors":"YuGuo Wang, Honglin Wang, Ze Wang, Yan Wu","doi":"10.3389/fninf.2026.1894506","DOIUrl":"10.3389/fninf.2026.1894506","url":null,"abstract":"<p><p>To address the challenges of inconsistent inter-modal contributions, high reliance on labeled data, and limited cross-modal synergy in multimodal emotion recognition, this paper proposes a dual-stream Siamese Self-supervised Emotion Recognition method (DSSER) based on EEG and fNIRS signals. During the pretraining phase, DSSER uses a dual-stream Siamese architecture to extract spatiotemporal features from electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals. It also aligns their representation spaces through a co-training strategy. This approach eliminates the need for negative samples in conventional contrastive learning. In the fine-tuning stage, a Dynamic Gated Cross-Attention Fusion module is introduced. It assigns modality weights and integrates complementary cross-modal features. Experimental results on a ternary emotion dataset involving 16 participants show that DSSER achieves an accuracy of 98.03% under an intra-subject emotion-recognition protocol. This result reflects performance under the adopted intra-subject setting. It should not be interpreted as subject-independent generalization to unseen participants. The results support the effectiveness of DSSER in improving cross-modal interaction.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1894506"},"PeriodicalIF":2.9,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148826529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of inhibition in modeling decision making with spiking neural networks.","authors":"Bartłomiej Król-Józaga, Peter Duggins, Anna Broniec-Wójcik, Szymon Wichary","doi":"10.3389/fninf.2026.1854811","DOIUrl":"10.3389/fninf.2026.1854811","url":null,"abstract":"<p><p>Decision making (DM) requires coordination of elementary information processes subserved by a distributed network of brain areas. Computational models help to understand these processes, but most of the existing models focus on simulating only one of the many parallel operations. An existing spiking neural network (SNN) model attempts to simulate DM holistically, however it does not take advantage of the significant role of inhibition at the neural level as a possible mechanism underlying DM. To address this limitation, we propose to examine the impact of neural inhibition on decision strategy selection in value-based DM using the mentioned model. In this study we outline the methodology and perform successful in-silico validation of the inhibition hypothesis with the SNN model of DM. To perform the simulation, we use a well-studied multi-attribute choice task and we validate simulation results against human behavioral data. The inhibition model achieved approximately 17% lower mean prediction error than the no-inhibition model (0.55 vs. 0.67) when evaluated on held-out, compensatory-condition data not used for fitting (Wilcoxon signed-rank test, <i>p</i> = 0.009, <i>r</i> = 0.55), with no significant difference observed in the condition used for fitting. These findings indicate that the advantage conferred by inhibition is not attributable to model complexity alone, and support neural inhibition as a plausible, biologically grounded mechanism for adaptive, context-sensitive decision strategy selection.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1854811"},"PeriodicalIF":2.9,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13506930/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148826469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Reliability and diagnostic performance of an automated MRI-based classifier compared with radiologists in Alzheimer's disease.","authors":"Nurmakhan Zholshybek, Elnora Abdurakhmanova, Almas Bimakhan, Dinara Jumadilova, Zhanibek Baiturlin, Joseph Almazan, Srinivasa Rao Bolla","doi":"10.3389/fninf.2026.1946431","DOIUrl":"https://doi.org/10.3389/fninf.2026.1946431","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fninf.2026.1821249.].</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1946431"},"PeriodicalIF":2.9,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13501169/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic coarse-to-fine AC-PC localization on CT using registration-guided 3D-UNets.","authors":"Sharada Kadaba Sridhar, Alyssa Eastman, Peter Wilson, Shubhendu Mishra, Charles Broadbent, Chip Truwit, Rui Kuang, Uzma Samadani","doi":"10.3389/fninf.2026.1853412","DOIUrl":"10.3389/fninf.2026.1853412","url":null,"abstract":"<p><strong>Introduction: </strong>Automatically localizing the Anterior Commissure (AC) and Posterior Commissure (PC) is foundational for CT-based algorithmic disease screening, yet robust computational methods for this on CT remain lacking. We developed a registration-guided 3D-UNet framework for CT-based AC-PC localization, demonstrating its utility in computing ventriculomegaly features for Normal Pressure Hydrocephalus (NPH) detection.</p><p><strong>Methods: </strong>Framework development and evaluation were on an internal cohort of scans from patients with NPH, Alzheimer's disease, post-traumatic volume loss, and headache (Veterans Affairs [VA]-Cohort, <i>n</i> = 427). External validation was on separate datasets (VA-ExtCohort, University of California, Santa Barbara [UCSB]-ExtCohort). AC-PC reference standard definition, model development, and evaluation were on 1 mm³-resampled scans.</p><p><strong>Results: </strong>On 1-mm³ resampled scans, test-set AC-PC mean radial errors (MREs) were 1.64/1.49 mm on the VA-Cohort, 2.42/1.79 mm on the VA-ExtCohort (<i>n</i> = 40), and 2.31/1.93 mm on the UCSB-ExtCohort (<i>n</i> = 43). Notably, the upper limits of the 95% confidence intervals (CIs) for localization errors across all cohorts were well below 3.2 mm; we empirically determined this to be a clinically relevant threshold beyond which the discriminative power of AC-PC-referenced ventriculomegaly features degrades. Ventriculomegaly features assessed using our framework's predictions successfully distinguished NPH from Alzheimer's disease, post-traumatic volume loss, and headache on a chart-verified VA-Cohort subset (<i>n</i> = 238) with a test-set Area Under the Receiver Operating Characteristic Curve (AUC) of 0.95, closely matching the performance of features assessed using manual AC-PC localization.</p><p><strong>Conclusion: </strong>The proposed registration-guided 3D-UNet framework accurately and automatically localizes the AC-PC on CT despite varied structural degeneration, enabling standardized radiological feature computation. This approach can augment neurodegenerative disease screening on CT, the primary modality for elderly patients evaluated for falls and altered mentation.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1853412"},"PeriodicalIF":2.9,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13445414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148683961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable 3D vision transformer framework for detecting brain anomalies in neuropsychiatric subtypes of Parkinson's disease.","authors":"Carlos Jiménez-Farfán, Alisson Constantine-Macias, Isaac León, Juan Pisco-Jordán, Luis Ramos-Pozo, Enrique Pelaez, Ana Tapia-Rosero, Edwin Valarezo-Añazco, Ignacio Obeso, Ana Briones-Chonillo, Maria José Miranda, Luis Yepez-Guerra, Francis R Loayza","doi":"10.3389/fninf.2026.1810223","DOIUrl":"10.3389/fninf.2026.1810223","url":null,"abstract":"<p><strong>Introduction: </strong>Neuropsychiatric manifestations, such as impulse control disorders (ICD) and apathy, are common in Parkinson's disease (PD) and significantly impact patient quality of life, yet their underlying neuroanatomical substrates remain poorly understood. This study presents an explainable artificial intelligence (XAI) framework to investigate these phenotypes using structural MRI.</p><p><strong>Methods: </strong>We employed a 3D Vision Transformer (MobileViT-3D) trained on T1-weighted images from 364 drug-naive PD patients, stratified into four subgroups: idiopathic PD (PD), PD with apathy (PD + A), PD with ICD (PD + ICD), and PD with both conditions (PD + A + ICD). To interpret the model's decision making, Guided Backpropagation and Integrated Gradients were applied to generate saliency maps, followed by a multi-level statistical analysis. Based on these patterns, volumes of interest (VOI) were defined. Conventional morphometric (e.g., cortical thickness) and radiomic texture features were obtained from each VOI applied to each subject.</p><p><strong>Results: </strong>Voxel-wise analysis of saliency maps suggested group-level differences after correction for multiple comparisons (p ≤ 0.05 FWE) in prefrontal, temporal, and cerebellar regions of the neuropsychiatric conditions compared to PD. While no significant group differences were observed for morphometric features, radiomic texture analysis identified eight features associated with differences between the PD + A + ICD group and idiopathic PD. These features included texture metrics indicative of increased heterogeneity and complexity.</p><p><strong>Discussion: </strong>Overall, these findings provide preliminary evidence that a combined XAI and radiomic approach may help identify subtle, texture-based neuroanatomical signatures associated with the co-occurrence of apathy and ICD in early PD that are not readily captured by conventional univariate analyzes. This framework may support the generation of clinically relevant hypotheses regarding the neural correlates of heterogeneous neuropsychiatric symptoms in PD.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1810223"},"PeriodicalIF":2.9,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13415765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148619095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ASCT: a pipeline for standardized analysis of MEG/EEG directional connectivity. Practical guidelines for applications of source-based Directed Transfer Function.","authors":"Miroslaw Wyczesany, Sara Spadone, Tomasz Górski, Adam Łabaza, Michal Domagala, Maciej Kaminski, Tomasz S Ligeza, Paolo Capotosto, Thomas Kroker, Markus Junghöfer, Stefania Della Penna","doi":"10.3389/fninf.2026.1791461","DOIUrl":"10.3389/fninf.2026.1791461","url":null,"abstract":"<p><p>The article presents the Atlantis Source Connectivity Toolbox (ASCT), a pipeline for the complete analysis of source-based directed (effective) connectivity from MEG/EEG signals. Connectivity estimation is implemented with the Directed Transfer Function, a multivariate autoregressive method grounded in the Granger causality approach. The theoretical concepts and challenges in source reconstruction and causal inferences from electrophysiological and biomagnetic recordings are introduced. Importantly, the guidelines for the custom analyses are also presented together with the toolbox user manual. By providing the complete analytic pipeline with practical recommendations, we intend to support standardization and replicability in connectivity studies. The validation of the method is further demonstrated with simulated MEG data, prepared from real resting data recording, as well as with realistic EEG data from the Eriksen flanker task. Our results highlight the importance of routine use of the leakage correction that prevents the appearance of spurious links. We also discuss potential benefits of source separation prior to their reconstruction for more accurate estimates of connectivity.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1791461"},"PeriodicalIF":2.9,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13337504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148404313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting vasovagal syncope during head-up tilt test: three machine learning approaches.","authors":"Matjaž Klemenc, Daniel Pellarini, Aleš Papič, Pavlin G Poličar, Dejan Štepec, Zoran Bosnić","doi":"10.3389/fninf.2026.1740746","DOIUrl":"10.3389/fninf.2026.1740746","url":null,"abstract":"<p><strong>Introduction: </strong>Syncope prediction during head-up tilt testing (HUTT) remains challenging due to the complex interplay between autonomic and cardiovascular responses. This study investigates three computational approaches to forecast HUTT outcomes using continuous electrocardiogram (ECG) and blood pressure recordings from 105 patients with a history of syncope who underwent HUTT following a modified Italian protocol.</p><p><strong>Methods: </strong>Beat-to-beat heart rate and blood pressure signals were analyzed using: (1) gradient boosting models applied to frequency-domain features of heart rate variability (HRV); (2) an analytical modeling approach employing k-nearest neighbors (kNN) regression on transformed physiological signals; and (3) an incremental neural network model.</p><p><strong>Results and discussion: </strong>Among these, the kNN regression approach provided the most consistent short-term forecasting of syncope probability, maintaining mean absolute errors below 0.13 for predictions up to 300 s before syncope onset. Gradient boosting models achieved promising classification performance with ROC AUC values up to 0.70, while the incremental network yielded moderate results. These findings demonstrate that data-driven analysis of early physiological changes can enable short-term forecasting of vasovagal syncope during HUTT, supporting the development of predictive tools for clinical risk assessment and personalized syncope management.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1740746"},"PeriodicalIF":2.9,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13283978/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148307688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding basal ganglia motor circuit dysfunction from handwriting: a physics-informed neural signal interpretation framework for Parkinson's disease screening.","authors":"Krishnan Batri, S Lakshmi, Salabat Khan, Zeshan Iqbal, Mohammad Alhefdi, Ghada Atteia, Sivaram Murugan","doi":"10.3389/fninf.2026.1814920","DOIUrl":"10.3389/fninf.2026.1814920","url":null,"abstract":"<p><p>The decoding of latent neural states from observable signals is a key focus of modern brain-AI research. Although most neural decoding models are based on electrophysiological recordings, peripheral motor outputs also convey information about circuit-level dynamics. Handwriting is a channeled behavioral signal that indexes the health of the cortico-basal ganglia-thalamo-cortical loop. In Parkinson's disease (PD), dopaminergic loss disrupts this circuit, leading to tremor oscillations, micrographia, and movement irregularities. The challenge of decoding behavior-encoded neural signals from handwriting images constitutes a principled neural signal interpretation problem. We propose a physics-informed and interpretable AI framework for decoding basal ganglia motor dysfunction through harmonic oscillator perturbation analysis. Six energy-inspired measures are extracted to quantify different aspects of motor system dynamics: intensity variation, spatial gradients, multi-scale stability, deviation variability, directional anisotropy, and cross-scale interactions. The transparent mapping between computational models and neurophysiological processes is enabled by these steps, which have their basis in mechanistic theories of amplitude modulation and oscillatory instability. High levels of discrimination were achieved when 594 handwriting trials (279 with PD and 315 controls) were assessed using repeated 10-fold cross-validation for spiral, circle, and meander tasks. Support Vector Machines achieved 84.06% accuracy and 93.56% sensitivity, with highly significant group differences across all features (<i>p</i> < 10^-33; Cohen's |<i>d</i>| = 0.87-1.51). Through the integration of physics-based modeling and interpretable machine learning, the proposed framework extends neural signal interpretation beyond direct neural recordings, establishing handwriting as a low-cost, behaviorally encoded biomarker of circuit state and advancing AI-driven decoding of brain dysfunction.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1814920"},"PeriodicalIF":2.9,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13269255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148270935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.","authors":"Abid Iqbal, Saad Arif, Ghassan Husnain, Sarra Ayouni","doi":"10.3389/fninf.2026.1799307","DOIUrl":"10.3389/fninf.2026.1799307","url":null,"abstract":"<p><strong>Introduction: </strong>Alzheimer's disease (AD) is difficult to treat because of its multifactorial causes and heterogeneous progression across individuals. This study introduces FUSION-AD, a user-friendly and interpretable artificial intelligence framework for AD risk assessment and subgroup discovery.</p><p><strong>Methods: </strong>FUSION-AD integrates tree-based models, transformer-based neural networks, rule mining, and subgroup discovery to provide accurate and interpretable predictions. The framework was developed using the synthetic El Kharoua Alzheimer's Disease Dataset, which contains 2,149 structured clinical records from patients aged 60-90 years, with a mean age of 74.6 years and an average mini-mental state examination (MMSE) score of 21.7. The pipeline included data preprocessing, model benchmarking, feature-importance analysis, SHAP-based explanation, transformer attention analysis, association rule mining, and subgroup discovery.</p><p><strong>Results: </strong>Within the evaluated dataset, TabNet achieved the strongest point-estimate performance among the standalone benchmark models on the primary evaluation split, with an area under the receiver operating characteristic curve (AUROC) of 0.95, followed by XGBoost at 0.93, Random Forest at 0.92, and Logistic Regression at 0.89. Feature importance, SHAP values, and transformer attention consistently identified MMSE, Functional Assessment, and Memory Complaints as the most influential predictors. Association rule mining further highlighted diabetes and high body mass index as important risk factors. Subgroup discovery identified four clinical clusters, with prevalence ranging from 21.3 to 28.4%. Cluster 0 showed notable declines in daily functioning, with Functional Assessment decreasing by 2.1 and activities of daily living decreasing by 1.5, whereas Cluster 1 maintained daily functioning but showed increased behavioral symptoms.</p><p><strong>Discussion: </strong>FUSION-AD demonstrates that AD can be modeled in a way that balances predictive performance with interpretability within the studied dataset. The identified subgroup patterns suggest that lifestyle-driven profiles may benefit from preventive strategies, while cognitively impaired groups may require closer monitoring. These findings provide a foundation for future clinically oriented decision-support systems and require further validation using real-world clinical datasets.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1799307"},"PeriodicalIF":2.9,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13253770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148249929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}