Human Brain Mapping最新文献

{"title":"Dynamic Fusion of Genomics and Functional Network Connectivity in UK Biobank Reveals Schizophrenia-Related SNP Manifolds.","authors":"Jiayu Chen, Armin Iraji, Zening Fu, Marlena Duda, Pablo Andrés-Camazón, Bishal Thapaliya, Jingyu Liu, Vince D Calhoun","doi":"10.1002/hbm.70530","DOIUrl":"10.1002/hbm.70530","url":null,"abstract":"<p><p>Many mental disorders show strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) has shown high sensitivity to brain changes related to mental disorders. However, previous studies linking dFNC to genetics largely follow a paradigm to identify associations between one set of genetic factors and multiple sets of connectivity features from different dFNC states, ignoring the potential variability in genetic correlates across states. We propose a novel joint ICA (jICA)-based \"dynamic fusion\" framework to identify dynamically tuned genetic manifolds. A sliding window approach was utilized to estimate four dFNC states and compute subject-level state-average dFNC (sa-dFNC) features. The sa-dFNC features of each state were combined with schizophrenia risk single nucleotide polymorphisms (SNPs) within a jICA fusion framework, resulting in four parallel fusions in 32,861 individuals of the UK Biobank cohort. The extracted four sets of joint SNP-dFNC components were further validated for clinical relevance in a combined schizophrenia cohort of 820 individuals (348 patients). The similarity of SNP-dFNC components across four parallel fusions was evaluated as a measure of state variability. We observed a mixture of \"state-invariant\" and \"state-variant\" components for SNP and dFNC modalities. Particularly, the schizophrenia-related state-variant SNP components, or manifolds, complemented each other by capturing different SNPs involved in the same biological functions, revealing a partition of genomic risk particularly elicited by the dynamics of brain function. By augmenting the SNP factors to state-variant manifolds, this dynamic fusion framework promises additional insights into the underlying genetic risk of disease-related alterations in dynamic brain function.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 6","pages":"e70530"},"PeriodicalIF":3.3,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13095861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147728861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI Insights Into EEG Classification and Its Alignment to Neural Correlates.","authors":"Hendrik Eilts, Gabriel Ivucic, Niklas Koenen, Marvin N Wright, Tanja Schultz, Felix Putze","doi":"10.1002/hbm.70528","DOIUrl":"https://doi.org/10.1002/hbm.70528","url":null,"abstract":"<p><p>While deep learning has drastically improved the performance of electroencephalography (EEG) analysis, it remains unclear what these models, such as EEGNet, learn from the data and how their learned features relate to neuroscientific concepts. In this work, we introduce a comprehensive interpretability framework for deep learning models of neural data based on Concept Relevance Propagation (CRP), an extension of layer-wise relevance propagation that enables the analysis of abstract concepts encoded by individual neurons and filters. We apply CRP to individual filters of convolutional neural networks (EEGNet) trained using leave-one-out cross-validation. To identify common classification strategies across models, we guide the selection of representative data for individual filters using relevance maximization, reduce dimensionality via UMAP, and identify clusters of filters encoding similar concepts through density-based clustering. To gain insight into the neural correlates of these tasks, we analyze the learned features across multiple data domains without requiring model retraining. We integrate a virtual inspection layer to project explanations into the frequency domain, enabling the simultaneous analysis of spatial, temporal, and spectral aspects using topographic maps, functional grouping, and independent component analysis (ICA). Using three EEG classification tasks-auditory attention, internal/external attention, and motor imagery-we demonstrate that our approach reveals interpretable, task-relevant neural patterns that generalize across participants. Overall, this framework provides a step toward understanding the models itself and gaining insights into the tasks in terms of neuroscience.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 6","pages":"e70528"},"PeriodicalIF":3.3,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13111923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147769924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET","authors":"Junho Moon,&nbsp;Symac Kim,&nbsp;Haejun Chung,&nbsp;Ikbeom Jang,&nbsp;Alzheimer's Disease Neuroimaging Initiative","doi":"10.1002/hbm.70508","DOIUrl":"10.1002/hbm.70508","url":null,"abstract":"<p>Positron emission tomography (PET) provides an in vivo molecular marker for various diseases, including Alzheimer's disease and related dementias (ADRD). PET has become increasingly integrated into diagnostic decision-making, disease staging, and clinical trial enrichment. However, its widespread use remains constrained by high costs, government regulations, and the invasiveness of radiotracer injection. Modern diagnostic frameworks emphasize the importance of multimodal biomarker assessment, such as the “amyloid/tau/neurodegeneration” (A/T/N) framework for Alzheimer's disease; however, they are constrained by these barriers. Medical image synthesis or translation offers a potential solution by enabling the reconstruction of unavailable modalities. The clinical utility of PET depends on accurately capturing regional uptake patterns rather than exact voxel-wise intensities, motivating the use of perceptual loss functions to assess higher-level semantic features in generative models. While 2D, 3D, and 2.5D perceptual losses are utilized in 3D synthesis, each encounters challenges, including limited volumetric context, the scarcity of pretrained 3D models, and difficulty balancing optimization across anatomical planes. In this work, we address cross-modal synthesis of tau PET from structural magnetic resonance imaging (MRI), generating 3D pseudo-[¹⁸F]flortaucipir standardized uptake value ratio (SUVR) maps from 3D T1-weighted MR images. We propose a cyclic 2.5D perceptual loss that cyclically optimizes the axial, coronal, and sagittal planes over training phases, thereby enhancing volumetric consistency. Furthermore, we standardize PET SUVRs by scanner manufacturer, reducing inter-manufacturer variability and better preserving high-uptake regions. We evaluate the proposed approach on cohorts spanning the ADRD spectrum using data from the Alzheimer's Disease Neuroimaging Initiative and the Standardized Centralized Alzheimer's Disease and Related Dementias Neuroimaging cohort. Our approach is broadly applicable across various generative frameworks and achieves high quantitative and qualitative performance on diverse architectures, including U-Net, UNETR, SwinUNETR, CycleGAN, and Pix2Pix. Notably, it achieves better agreement between synthesized SUVRs and measured PET scans in key brain regions relevant to Alzheimer-type tau pathology. The code is publicly available at https://github.com/labhai/Cyclic-2.5D-Perceptual-Loss.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating Emotional Reactivity in Experienced Users of Psychedelics: A Cross-Sectional fMRI Study","authors":"Paweł Orłowski,&nbsp;Aleksandra Domagalik,&nbsp;Michał Bola","doi":"10.1002/hbm.70522","DOIUrl":"10.1002/hbm.70522","url":null,"abstract":"<p>Classic psychedelics profoundly alter emotional states, inducing intense acute experiences lasting hours, followed by subtler, longer-lasting changes in emotional reactivity that can persist for weeks. While experimental and clinical studies document these prolonged effects, the highly context-dependent nature of psychedelic experiences leaves open the question of whether naturalistic, nonclinical use similarly modulates emotional processing. To investigate this, we conducted a preregistered, cross-sectional fMRI study comparing experienced psychedelic users (≥ 10 lifetime uses; <i>N</i> = 33) with closely matched nonusers (<i>N</i> = 34). Participants performed an emotional face recognition task, and we examined behavioral performance and neural responses to angry, happy, and fearful facial expressions. Behavioral results revealed that psychedelic users recognized angry expressions more quickly and accurately, indicating enhanced processing efficiency for threat-related stimuli. Consistent with this, whole-brain fMRI analyses showed reduced activation to anger in key limbic and salience network regions. Psychedelic users also exhibited heightened responses to happy expressions in parietal and sensorimotor cortices—aligning with prior clinical observations—as well as increased precuneus activation to fearful expressions. Region-of-interest analyses further demonstrated reduced differentiation between emotional categories in two default mode network nodes: the frontal medial cortex and parahippocampal gyrus. These findings provide a nuanced characterization of neurofunctional changes in emotional processing linked to repeated naturalistic psychedelic use. By bridging clinical and real-world contexts, this work deepens our understanding of the potential long-term consequences of psychedelics and complements existing evidence from controlled therapeutic settings.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13063118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147638591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Voluntary Attention Selectively Modulates Omission Responses","authors":"Tjerk T. Dercksen,&nbsp;Andreas Widmann,&nbsp;Nicole Wetzel","doi":"10.1002/hbm.70518","DOIUrl":"10.1002/hbm.70518","url":null,"abstract":"<p>Predictive coding conceptualizes attention as a weighting of prediction error signals. However, empirical findings on how attention influences common markers of prediction error have been inconsistent, likely because these markers are typically derived from stimulus-evoked responses. To avoid stimulus-related confounds and isolate effects related purely to prediction, we investigated how attention modulates brain responses to unexpected stimulus omissions. Using visual–auditory couplings where the auditory stimulus was occasionally omitted, we recorded EEG responses that revealed a multistage omission response—from early sensory to later higher-level prediction error activity. Voluntary attention was manipulated along two dimensions: (1) toward the visual or auditory modality, and (2) toward the moment of stimulus presentation or sustained over time. Early sensory prediction error, reflected by the omission N1, was unaffected by any manipulation of attention. In contrast, later high-level prediction error processing, reflected by omission P3 responses, was strongly affected by directing attention: robust responses were elicited when attention was directed to the auditory modality—where the prediction had been violated—but these were markedly reduced or absent when attention was directed to the visual modality. These results suggest an attentional system that does not affect low-level sensory prediction error but is capable of influencing distinct stages in the processing hierarchy in service of task performance. This first investigation of how attention affects different stages of omission activity suggests that voluntary attention may modulate prediction error processing via specific neurotransmitter systems and demonstrates this approach's potential for reliably studying precision-weighting in the brain.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13065894/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147645127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial Entropy of Brain Network Landscapes: A Novel Method to Assess Spatial Disorder in Brain Networks","authors":"Clayton C. McIntyre,&nbsp;Shannon M. O’Donnell,&nbsp;Mohammadreza Khodaei,&nbsp;Robert G. Lyday,&nbsp;Jonathan H. Burdette,&nbsp;Paul J. Laurienti","doi":"10.1002/hbm.70525","DOIUrl":"10.1002/hbm.70525","url":null,"abstract":"<p>In this work, we introduce a method for mapping the spatial entropy of functional brain network community structure images in brain space. Entropy maps indicate the extent to which the network communities present in a local area are ordered or disordered. We demonstrate how spatial entropy can be quantified for each voxel in the brain according to the network community affiliations of surrounding voxels. This process results in interpretable maps of brain network entropy. We show that local entropy decreases in predictable brain regions during working memory and music-listening tasks. We suggest that these regional entropy reductions reflect self-organization of neural processes in support of functionally localized cognitive tasks. In summary, we propose a method that allows group-level comparison of the brain network community structure identified in individuals. Analyses in this work provide a framework for future analyses of spatial entropy in complex networks that can be mapped to Euclidean space—both within the brain and in other contexts.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13058231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147633119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonlinear Asymmetric Blood Oxygenation Level Dependent Responses in Somatosensory Cortex","authors":"Feng Wang,&nbsp;Pai-Feng Yang,&nbsp;Arabinda Mishra,&nbsp;Li Min Chen,&nbsp;John C. Gore","doi":"10.1002/hbm.70523","DOIUrl":"10.1002/hbm.70523","url":null,"abstract":"<p>Blood oxygenation level dependent (BOLD) responses in fMRI have previously been shown to be nonlinear with regard to changes in stimulus parameters, and as a result they may be asymmetric when comparing stimulus increases with decreases from an initial condition. We measured BOLD responses to varying vibrotactile stimuli of the hand digits in a monkey, including both increases and decreases in intensity and duration, relative to different levels of initial activation. Across variations in stimulus duration and intensity, positive and negative BOLD responses were asymmetric and nonlinear. Moreover, the asymmetry between positive and negative responses was manifest at different levels of baseline activation. The results confirm that the use of a common hemodynamic response function for increases and decreases in activity may underestimate the magnitude of decreases in activation. Electrophysiological recordings from multi-electrode arrays also revealed nonlinear and asymmetric features in multi-unit activities, linking neural firing properties to the nonlinear BOLD profiles.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13057424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147633130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging Histology and Tractography: First In Vivo Visualization of Short-Range Prefrontal Connections Informed by Primate Tract-Tracing","authors":"Matthew Amandola,&nbsp;Michael E. Kim,&nbsp;François Rheault,&nbsp;Bennett Landman,&nbsp;Kurt Schilling","doi":"10.1002/hbm.70520","DOIUrl":"10.1002/hbm.70520","url":null,"abstract":"<p>Decades of histological research in non-human primates have revealed a dense web of short-range connections underpinning prefrontal cortex (PFC) function. However, translating this anatomical ground-truth to the living human brain has been a major challenge, leaving our understanding of the PFC's intrinsic wiring incomplete. These short-range fibers are difficult to resolve with non-invasive methods like diffusion tractography, which are often hampered by false positives. Here, we provide the first systematic in vivo visualization of these pathways in the human brain. By informing high-resolution probabilistic tractography with established tract-tracing findings, we mapped 91 histologically-defined short-range connections within and between five major PFC subdivisions in 1003 individuals (547 F, 456 M). Our anatomically-informed approach successfully reconstructed these intricate connections with high precision (&gt; 80%) and accuracy (&gt; 70%) relative to histological findings. The resulting tracts not only captured broad organizational principles but also replicated fine-grained patterns previously only seen in invasive studies. Furthermore, these connections showed high test–retest reliability within individuals alongside significant variability between them, highlighting a stable yet unique anatomical fingerprint. Ultimately, this study shows how linking histology to tractography provides a powerful framework to advance our understanding of the human connectome and opens avenues to investigate local circuitry that underpins cognition and disease.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13058440/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147633176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resting-State and Task Functional Magnetic Resonance Imaging Network Topology Metrics With no Threshold Selection to Predict Cognition.","authors":"Charly Hugo Alexandre Billaud, Junhong Yu","doi":"10.1002/hbm.70526","DOIUrl":"10.1002/hbm.70526","url":null,"abstract":"<p><p>Network topology measures characterise brain networks' organisation. Graph theoretical approaches have shown fMRI topology metrics' association with cognitive performance. Because arbitrary connectivity threshold selection biases such metrics, alternatives including the minimum spanning tree (MST) and novel measures following principles of persistent homology were proposed. The present study compared alternative and graph theoretical metrics in association with cognition for resting-state and task-fMRI. Functional connectivity matrices were computed from Human Connectome Project (Young Adult) fMRI scans during resting-state, working memory (WM), gambling, language, motor, relational processing, social cognition, and movie-watching conditions. Global efficiency, clustering coefficient (at three thresholds), diameter, leaf fraction (LF), backbone strength (BS), and cycle strength were measured. Each was tested in association with cognitive test scores. ResultsBS significantly predicted general cognitive performance, specifically progressive matrices score, composite fluid and crystallised cognition, vocabulary, spatial orientation, and WM. Diameter significantly predicted WM. WM task BS outperformed the predictive performance of graph theory measures, but not at rest, where MST LF outperformed other measures. Stronger associations were observed between cognitive test scores and topology measures derived from task-based fMRI, especially the N-Back task, as opposed to resting-state fMRI. Among task-based topology measures, BS was the most strongly related to cognition.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":"e70526"},"PeriodicalIF":3.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13057421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147633121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Traces of Parenthood but Not Pregnancy Loss in UK Biobank Structural Brain MRI Data.","authors":"Jonas L Fischer, Alkistis Skalkidou, Birgit Derntl, Tobias Kaufmann","doi":"10.1002/hbm.70527","DOIUrl":"10.1002/hbm.70527","url":null,"abstract":"<p><p>Pregnancy induces neuroanatomical changes in the human brain. Previous studies detected both traces of motherhood decades after childbirth and adaptations in fathers. It is unclear which effects can be attributed to persisting traces of pregnancy and which are effects of parenthood. We investigated effects of past birth and of pregnancy loss in women, and effects of fatherhood in men, using univariate and machine learning analyses on 205 regional brain volumes. A group of mothers and an age-matched sample of nulliparous women (N = 4357 per group, mean age 63 years) from the UK Biobank, with no past pregnancy losses, showed significant volumetric group differences in 14 regions at Bonferroni-adjusted α = 0.05. Likewise, we identified 18 significant group differences between age-matched samples of fathers and non-fathers of the same size (mean age 63.4), with 9 regions overlapping between sexes. Brain-wide association statistics for past live birth in mothers and those for fatherhood correlated (r = 0.55). XGBoost machine learning models trained to classify parenthood status separately in both datasets showed performance that was low, but significantly above chance (10-fold cross validation: AUC = 0.56, p < 1e-5 Motherhood classifier, AUC = 0.54, p < 1e-5, Fatherhood classifier, 10 k permutations). We tested the motherhood classification model on an independent test sample comprising four age-matched groups: 1. women who have never been pregnant, 2. women with past pregnancy loss but no live births, 3. women with live births but no pregnancy loss, and 4. women who experienced both. Class probability was significantly associated with live births, but not past loss. These findings may suggest that neuroanatomical patterns of past childbirth partly also reflect traces of parenthood and not solely persisting traces of past pregnancy, although a more detailed characterization of pregnancy loss data would be needed for full confirmation of this interpretation. Therefore, further research is needed to quantify the extent and understand the nature of these changes, particularly considering the known vulnerability for mental disorders associated with reproductive events.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":"e70527"},"PeriodicalIF":3.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13070878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147672800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}