Human Brain Mapping最新文献

{"title":"Interhemispheric Metabolic Effects of Transcranial Infrared Laser Stimulation of the Prefrontal Cortex","authors":"Patrick O'Connor,&nbsp;Turner Lime,&nbsp;Douglas W. Barrett,&nbsp;Li-da Huang,&nbsp;F. Gonzalez-Lima","doi":"10.1002/hbm.70578","DOIUrl":"https://doi.org/10.1002/hbm.70578","url":null,"abstract":"<p>Photobiomodulation is the use of low-power, non-ionizing light to stimulate cellular processes. Transcranial infrared laser stimulation (TILS) is a method for prefrontal photobiomodulation that produces dose-dependent metabolic effects in vivo. However, interhemispheric differences in metabolic responses to unilateral right versus left TILS are unknown. Investigating interhemispheric effects of TILS is important because right–left differences are observed with other forms of transcranial stimulation, including magnetic and electrical stimulation. TILS penetration was first characterized using Monte Carlo simulations of photon propagation in the Colin27 MRI-based brain model. Simulations confirmed that 1%–5% photon penetration was confined unilaterally to the stimulated side of the prefrontal cortex. Eighty-one healthy adults were then randomized to receive unilateral right TILS, unilateral left TILS, or sham stimulation (laser off). Active TILS parameters were selected based on prior studies demonstrating metabolic efficacy (1064 nm continuous wave, 250 mW/cm² irradiance, 440 s exposure, 110 J/cm² fluence, and 1496 J total energy). Bilateral changes in cytochrome-c-oxidase (CCO), oxygenated hemoglobin (HbO), and deoxygenated hemoglobin (HHb) were measured with in vivo spectroscopy before, during, and after unilateral TILS or sham. No adverse effects were reported, and no significant metabolic or hemodynamic changes were observed in the sham condition. Unilateral TILS produced ipsilateral (TILS side) increases in CCO and HbO that were similar regardless of whether the right or left prefrontal cortex was stimulated. In contrast, contralateral (opposite side) metabolic responses differed by stimulation side. Contralateral CCO effects emerged later than ipsilateral effects and were greater in the right hemisphere following TILS to the left. This study is the first to demonstrate an interhemispheric difference in the human brain response to photobiomodulation, specifically contralateral CCO effects. Although ipsilateral metabolic effects were similar for right and left prefrontal stimulation, contralateral responses exhibited hemispheric asymmetry. It is possible that these interhemispheric differences may contribute to the different cognitive and emotional effects of right versus left transcranial photobiomodulation.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 9","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70578","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148274879","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":"Efficient Deep Learning Models for Predicting Individualized Task Activation From Resting-State Functional Connectivity","authors":"Soren J. Madsen,&nbsp;Young-Eun Lee,&nbsp;Shaun K. L. Quah,&nbsp;Lucina Q. Uddin,&nbsp;Jeanette A. Mumford,&nbsp;Deanna M. Barch,&nbsp;Damien A. Fair,&nbsp;Ian H. Gotlib,&nbsp;Russell A. Poldrack,&nbsp;Amy Kuceyeski,&nbsp;Manish Saggar","doi":"10.1002/hbm.70557","DOIUrl":"https://doi.org/10.1002/hbm.70557","url":null,"abstract":"<p>Deep learning models have demonstrated the potential to predict task-evoked brain activation from resting-state functional magnetic resonance imaging, offering a pathway toward individualized brain mapping without requiring task-based data. In this study, we systematically evaluate architectural strategies for improving the efficiency and scalability of such models. Using data from the Human Connectome Project, we replicate the BrainSurfCNN framework and introduce two extensions: BrainSERF, which incorporates channel-wise attention through squeeze-and-excitation modules, and BrainSurfGCN, a graph-based model that leverages cortical mesh topology for efficient message passing. Across multiple evaluation metrics, including spatial correlation, Dice score, Dice AUC, and subject identification accuracy, all models achieve comparable predictive performance. Despite similar accuracy, the proposed models offer distinct advantages. BrainSERF provides modest improvements in capturing individual-specific features, while BrainSurfGCN achieves substantial reductions in model size and training time, highlighting a favorable trade-off between performance and computational efficiency. Beyond architectural comparisons, we investigate factors driving variability in prediction accuracy. We find that behavioral task performance, resting-state data quality, and inter-subject variability in task activation jointly constrain prediction fidelity. In particular, contrasts with lower signal reliability and higher variability exhibit reduced predictability across all models. Together, these findings demonstrate that incorporating topological and functional structural priors can improve the efficiency of deep learning models without sacrificing accuracy, while also emphasizing that prediction performance is fundamentally limited by the reliability of the underlying neural signals.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 9","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70557","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148274835","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":"Network-Level Convergence of Adiposity-Related Gray Matter Alterations: A Coordinate-Based Connectome Mapping Meta-Analysis With Imaging Transcriptomics","authors":"Yingxian Li,&nbsp;Mingnan Lin,&nbsp;Gengbiao Zhang,&nbsp;Hongyi Zheng,&nbsp;Wenbin Zheng","doi":"10.1002/hbm.70563","DOIUrl":"10.1002/hbm.70563","url":null,"abstract":"<p>Obesity is commonly associated with atrophy-like gray matter changes, such as lower gray matter volume or density, yet the affected regions reported vary widely across studies. It remains unclear whether these heterogeneous loci converge on a reproducible large-scale network, and whether weight-loss–related increases in gray matter map onto the same network or a distinct set of functional systems. We identified loci of adiposity-related gray matter reduction and post-weight-loss increases from 38 published whole-brain voxel-based morphometry studies, including case–control, dimensional adiposity, and longitudinal weight-loss contrasts. Using coordinate-based connectome mapping applied to a large normative resting-state functional connectome, we reconstructed networks associated with adiposity-related structural alterations. We further performed imaging transcriptomic and gene set enrichment analyses to characterize the molecular programs associated with the adiposity-related network. Gray matter reductions associated with adiposity converged on a reproducible, distributed large-scale network primarily involving frontoparietal control, cingulo-opercular, default mode, and Visual1 systems. In contrast, GMV/GMD increases following weight loss showed a distinct system-level distribution, with the largest contributions in cingulo-opercular, somatomotor, and dorsal attention systems, and comparatively limited representation in default mode and frontoparietal control networks. Imaging transcriptomic analyses further linked the adiposity-related network to coordinated gene expression patterns, and gene set enrichment analyses highlighted pathways related to mitochondrial energy metabolism, synaptic signaling, ion transport, and cellular protein homeostasis. Adiposity-related gray matter alterations converge on a reproducible large-scale network, but the system-level pattern differs across categorical obesity, dimensional adiposity, and GMV/GMD increases following weight loss. Weight-loss–related gray matter increases are not a simple mirror reversal of the cross-sectional network, and imaging transcriptomic analyses implicate coordinated molecular programs related to mitochondrial energetics, synaptic signaling, ion transport, and protein homeostasis.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 8","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70563","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148211212","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":"Replicability of Functional Brain Networks: A Study Through the Lens of Seven Resting-State Networks","authors":"Kaitlyn R. Fales,&nbsp;Xurui Zhi,&nbsp;Hyebin Song,&nbsp;Nicole A. Lazar","doi":"10.1002/hbm.70559","DOIUrl":"10.1002/hbm.70559","url":null,"abstract":"<p>The study of brain networks is essential for improving our understanding of how the human brain functions. Functional connectivity (FC) analysis is a widely used approach for studying co-activating patterns among brain regions by estimating their temporal dependencies and constructing an undirected network. Data processing is critical before estimating a subject's functional network, but the absence of a standardized procedure serves as a source of heterogeneity in results, especially in multi-site studies. Commonly studied functional networks include the default mode, sensorimotor, visual, salience, dorsal attention, frontoparietal, and language networks. These networks are stable and still exhibit intrinsic activation when an individual is at rest, making them ideal networks to focus on for studying how processing choices affect the replicability of functional connectivity networks. We use the aforementioned seven networks to assess the impact of various processing choices, including preprocessing pipeline, band-pass filtering, and brain parcellation, on the replicability of functional connectivity estimates for multi-site resting-state fMRI (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE). Finally, we provide some practical recommendations for how researchers should proceed with processing choices in the face of these effects.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 8","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70559","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148204259","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":"Mitigating the Impact of MR Sequence Parameters: Increasing the Robustness of DL-Based Cortical Thickness Estimates","authors":"Timo Blattner,&nbsp;David Romascano,&nbsp;Richard McKinley,&nbsp;Michael Rebsamen,&nbsp;Anke Salmen,&nbsp;Maximilian Pistor,&nbsp;Robert Hoepner,&nbsp;Roland Wiest,&nbsp;Piotr Radojewski,&nbsp;Christian Rummel,&nbsp;Milena Capiglioni","doi":"10.1002/hbm.70560","DOIUrl":"10.1002/hbm.70560","url":null,"abstract":"<p>Cortical thickness measurements from MRI are increasingly used as biomarkers for neurodegenerative disease progression. However, variations in MRI acquisition parameters, such as inversion time (TI) and repetition time (TR), which are common in clinical settings, can compromise the reliability and sensitivity of these measurements. We fine-tuned a deep-learning-based segmentation tool (DL+DiReCT) to reduce its dependence to image contrast variations by training it on simulated MPRAGE images derived from quantitative relaxation maps. Fine-tuning markedly reduced contrast sensitivity, with the Pearson correlation coefficient decreasing from <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>0.644</mn>\u0000 </mrow>\u0000 <annotation>$$ -0.644 $$</annotation>\u0000 </semantics></math> to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>0.094</mn>\u0000 </mrow>\u0000 <annotation>$$ 0.094 $$</annotation>\u0000 </semantics></math>. Evaluation on a synthetic atrophy dataset demonstrated that our model accurately replicated atrophy trends with minimal underestimation, outperforming FreeSurfer and SynthSeg. When applied to a dataset of relapsing–remitting multiple sclerosis (RRMS) patients, the fine-tuned model showed a substantial reduction in contrast sensitivity and maintained stable performance after controlling for covariates such as age, sex, field strength, and Expanded Disability Status Scale (EDSS) score. Overall, the proposed approach achieves robust contrast invariance without sacrificing sensitivity to cortical atrophy, offering a practical improvement for longitudinal and multi-center clinical studies.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 8","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148198878","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":"Lifespan Trajectories of Asymmetry in White Matter Tracts","authors":"Sam Bogdanov,&nbsp;Praitayini Kanakaraj,&nbsp;Michael E. Kim,&nbsp;Jessica Samir,&nbsp;Chenyu Gao,&nbsp;Karthik Ramadass,&nbsp;Gaurav Rudravaram,&nbsp;Nancy R. Newlin,&nbsp;Derek Archer,&nbsp;Timothy J. Hohman,&nbsp;Angela L. Jefferson,&nbsp;Victoria L. Morgan,&nbsp;Alexandra Roche,&nbsp;Dario J. Englot,&nbsp;Susan M. Resnick,&nbsp;Lori L. Beason Held,&nbsp;Laurie E. Cutting,&nbsp;Laura A. Barquero,&nbsp;Micah A. D'Archangel,&nbsp;Tin Q. Nguyen,&nbsp;Kathryn L. Humphreys,&nbsp;Yanbin Niu,&nbsp;Sophia Vinci-Booher,&nbsp;Carissa J. Cascio,&nbsp;The HABS-HD Study Team,&nbsp;Alzheimer's Disease Neuroimaging Initiative,&nbsp;The BIOCARD Study Team,&nbsp;Zhiyuan Li,&nbsp;Simon N. Vandekar,&nbsp;Panpan Zhang,&nbsp;John C. Gore,&nbsp;Stephanie J. Forkel,&nbsp;Bennett A. Landman,&nbsp;Kurt G. Schilling","doi":"10.1002/hbm.70519","DOIUrl":"https://doi.org/10.1002/hbm.70519","url":null,"abstract":"<p>Asymmetry in white matter is believed to give rise to the brain's capacity for specialized processing and is involved in the lateralization of various cognitive processes, such as language and visuo-spatial reasoning. Although studies of white matter asymmetry have been previously documented, they have often been constrained by limited age ranges, sample sizes, or the scope of the tracts and structural features examined. While normative lifespan charts for brain structures are emerging, comprehensive charts detailing white matter asymmetries across numerous pathways and diverse structural measures have been notably absent. This study addresses this gap by leveraging a large-scale dataset of 35,120 typically developing and aging individuals, ranging from 0 to 100 years of age, from 50 primary neuroimaging studies. We generated comprehensive lifespan trajectories for 30 lateralized association and projection white matter tracts, examining six distinct microstructural and macrostructural features of these pathways. Our findings reveal that: (1) asymmetries are widespread across the brain's white matter and are present in all 30 pathways; (2) for a given pathway, the degree and direction of asymmetry differ between features of tissue microstructure and pathway macrostructure; (3) asymmetries vary across and within pathway types (association and projection tracts); and (4) these asymmetries are not static, following unique trajectories across the lifespan, with distinct changes during development, and a general trend of becoming more asymmetric with increasing age (particularly in later adulthood) across pathways. This study represents the most extensive characterization of white matter asymmetry across the lifespan to date, charting how lateralization patterns emerge, mature, and change throughout life. It provides a foundational resource for understanding the principles of white matter organization from early to late life, its relation to functional specialization and inter-individual variability, and offers a key reference for interpreting deviations during healthy development and aging as well as those associated with clinical populations.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 8","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174342","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":"Longitudinal Study of Adolescent Brain Connectivity Development Using Sign-Aware Graph Theory Metrics","authors":"Subhasri Viswanathan,&nbsp;Sean Spinney,&nbsp;Jeremy J. Watts,&nbsp;Patricia Conrod","doi":"10.1002/hbm.70549","DOIUrl":"https://doi.org/10.1002/hbm.70549","url":null,"abstract":"<p>Adolescence is marked by significant changes in brain network organization that underlie cognitive and behavioral development. The sensorimotor-association (SA) axis has been proposed as a hierarchical framework for understanding functional connectivity development, but most studies rely on cross-sectional data and treat positive and negative connections equivalently. We analyzed longitudinal resting-state fMRI data from 125 adolescents who passed quality control of a total of 151 (ages 12–18, 364 total scan sessions across three time points) using both functional connectivity strength and graph-theoretical metrics, comparing results from absolute-value networks (collapsing connection signs) versus sign-aware approaches. Functional connectivity strength showed age-related changes following the SA axis selectively for positive connections (<i>r</i> = −0.614, <i>p</i> &lt; 0.001), with stronger effects in sensorimotor regions, while negative connections showed no SA alignment (<i>r</i> = 0.031, <i>p</i> = 0.803). Critically, graph-theoretical measures revealed opposing developmental gradients depending on network construction: clustering coefficient and local efficiency showed association-dominant patterns in absolute-value networks (<i>r</i> = 0.317, <i>p</i> &lt; 0.001; <i>r</i> = 0.427, <i>p</i> = 0.001) but sensorimotor-dominant patterns in positive-only networks (<i>r</i> = −0.225, <i>p</i> &lt; 0.001; <i>r</i> = −0.277, <i>p</i> &lt; 0.001). Participation coefficient, an integration-based measure, showed no significant SA association in either construction. These findings demonstrate that developmental inferences critically depend on how negative connections and network topology are treated, challenging the notion of a single organizational gradient and highlighting the necessity of sign-aware graph-theoretical approaches for understanding adolescent brain maturation.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 8","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70549","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174459","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":"Energy-Based Phase-Locking State Analysis in Brain State Identification","authors":"Chenfei Ye,&nbsp;Ziyan Deng,&nbsp;Shiqing Cong,&nbsp;Chen Ran,&nbsp;Tao Gong,&nbsp;Shoulin Huang,&nbsp;Ting Ma","doi":"10.1002/hbm.70558","DOIUrl":"10.1002/hbm.70558","url":null,"abstract":"<p>The human brain exhibits inherent multistability, with Energy Landscape Analysis (ELA) providing effective frameworks for investigating this property through BOLD signals. However, traditional amplitude-based approaches fundamentally neglect critical phase synchronization dynamics that mediate large-scale neural coordination, while existing phase-based methods like Leading Eigenvector Dynamic Analysis (LEiDA) lack thermodynamic formalism for state stability quantification. Here, we introduce Energy-based Phase-Locking State Analysis (EPLSA), a transformative computational framework that synergistically integrates instantaneous phase-coupling dynamics with rigorous energy landscape principles, addressing fundamental limitations of conventional methodologies. Comprehensive validation across two independent neuroimaging datasets (HCP and Natural Sleep) demonstrated EPLSA's marked superiority over LEiDA and conventional ELA in terms of test–retest reliability, task-specific brain state differentiation, and individual-level classification performance. To demonstrate the physiological and clinical utility of the proposed method, sleep–wake analysis was performed to reveal EPLSA's enhanced sensitivity to consciousness state transitions, identifying decreased primary state occupancy and increased minor state prevalence during sleep, with significantly reduced direct transition probabilities. Furthermore, application to patients with Alzheimer's disease using the OASIS-3 dataset identified shortened dwell time and occurrence frequency for the frontoparietal control network-default mode network (FPCN-DMN) co-activation state, and prolonged dwell time and occurrence frequency for the visual network-limbic network (VIS-LMN) co-activation state, with these metrics significantly correlating with cognitive impairment. By unifying phase-coupling and thermodynamic principles, EPLSA provides novel insights into neurodynamic mechanisms across cognitive tasks, consciousness states, and neurodegenerative conditions, offering a transformative analytical tool for investigating brain function in health and disease with particular promise for early detection and monitoring of neurological disorders.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 8","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70558","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148163000","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":"Disgust Propensity, Not Disgust Sensitivity, Shapes the Reactivity of a Subjective Disgust Circuit in Humans","authors":"Xianyang Gan,&nbsp;Zihao Zheng,&nbsp;Ran Zhang,&nbsp;Feng Zhou,&nbsp;Ting Xu,&nbsp;Nan Qiu,&nbsp;Junjie Wang,&nbsp;Heng Jiang,&nbsp;Shan Gao,&nbsp;Yu Wu,&nbsp;Benjamin Klugah-Brown,&nbsp;Dezhong Yao,&nbsp;Benjamin Becker","doi":"10.1002/hbm.70577","DOIUrl":"10.1002/hbm.70577","url":null,"abstract":"<p>Disgust constitutes an evolutionary adaptive defensive-avoidance response, yet humans vary markedly in their dispositional tendency to experience disgust (disgust propensity) and in their negative appraisal of such experience (disgust sensitivity). Conceptual frameworks and neuroimaging studies suggest that these traits may differentially modulate neural responses to disgust-eliciting stimuli; however, methodological constraints have left their precise roles unresolved. Our comparably large fMRI study (<i>n</i> = 142) therefore aimed to systematically determine how trait disgust modulates neural responses to carefully selected and validated disgust-specific visual stimuli across varying levels of subjective disgust experience. The whole-brain voxel-wise regression analyses revealed a differential pattern of neural associations between the two disgust traits, with disgust propensity, but not disgust sensitivity, modulating disgust-related neural activity in the anterior, middle, and posterior insula, as well as the caudate, putamen, thalamus, hippocampus, and parahippocampal gyrus. Mediation and network-level analyses further supported this partly dissociable pattern by showing that disgust propensity shapes disgust experience via insula-striatal-hippocampal pathways. Together, these findings provide evidence for a differential association between disgust propensity and disgust sensitivity with disgust-related neural responses and elucidate how trait disgust shapes subjective experiences. They further suggest that disgust propensity and the identified systems may represent promising targets for the regulation of disgust-related pathology.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 8","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148156333","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":"Distinct Physiological Mechanisms Drive Grey Matter Plasticity in Complex Versus Simple Sequence Learning","authors":"Jhelum Paul,&nbsp;A. T. P. Jäger,&nbsp;J. Huck,&nbsp;C. L. Tardif,&nbsp;A. Villringer,&nbsp;C. J. Gauthier,&nbsp;P. L. Bazin,&nbsp;Christopher J. Steele","doi":"10.1002/hbm.70562","DOIUrl":"10.1002/hbm.70562","url":null,"abstract":"<p>Ultra-high field magnetic resonance imaging at sub-millimeter resolution opens the possibility to assess subtle longitudinal changes in brain structure and function. Identifying the regions and putative mechanisms involved in learning specific motor sequences is a key step towards understanding neuroplasticity. To disambiguate sequence-specific learning from simple motor execution, the present study trained an experimental group over five consecutive days on a complex motor sequence and contrasted them with a control group who performed a simple sequence. Both groups were scanned at 4 time points with magnetic resonance imaging at 7 Tesla with MP2RAGE. Training-related grey matter (GM) structural plasticity was assessed with voxel-based morphometry (VBM: time by group interaction) on T1w uniform intensity images and followed up with a focused investigation of quantitative T1 values (qT1) to probe the physiological changes driving GM plasticity. Our interaction analyses identified significant differences in the precuneus, superior parietal cortex (SPC), and angular gyrus (AG) where GM increases were greater in the experimental group. Of these regions, only the left SPC (during slow learning) exhibited sequence-specific plasticity—where the magnitude of change was greater in the experimental vs. the control group. Interestingly, while the experimental group showed increased GM volume, the control group was characterized by decreases. Post hoc comparisons revealed that the control group exhibited increasing T1 across days 1, 2 and 5, while the experimental group initially increased (day 1 to 2) and then decreased (day 2 to 5), suggesting that the two groups underwent differential physiological change as a function of training. These findings align with invasive animal studies showing that learning simple tasks is characterized by angiogenesis while learning complex sequences initially leads to synaptogenesis and is then followed by intracortical myelination as the sequence is well learned. Our findings provide initial evidence in humans that learning a complex sequence induces GM increases that are likely driven by synaptogenesis for initial encoding and myelogenesis for later learning and consolidation, while habituating to a simple repeated task leads to GM decreases due to attenuated blood flow.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 8","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70562","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148156311","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}