Human Brain Mapping最新文献

{"title":"A Data-Driven Closed-Loop Control Approach to Drive Neural State Transitions for Mechanistic Insight","authors":"Niklas Emonds,&nbsp;Evelyn Herberg,&nbsp;Martin Fungisai Gerchen,&nbsp;Marc Pritsch,&nbsp;Joshua Rocha,&nbsp;Vera Zamoscik,&nbsp;Peter Kirsch,&nbsp;Roland Herzog,&nbsp;Georgia Koppe","doi":"10.1002/hbm.70600","DOIUrl":"10.1002/hbm.70600","url":null,"abstract":"<p>Altered affective state dynamics are a characteristic feature of depression and can persist beyond symptomatic remission. Individuals with remitted major depressive disorder (rMDD) often show heightened reactivity to negative mood states and reduced efficiency in recovering from them, consistent with changes in affective dynamics after remission. These patterns may reflect alterations in the brain's capacity to flexibly shift between neural states that support distinct affective modes. Characterizing the dynamical mechanisms that govern transitions into and out of experimentally induced affective states is therefore essential for understanding vulnerability to recurrence and informing mechanistic interventions. We developed a data-driven framework combining dynamical system reconstruction (DSR) with model-based control to infer optimal control policies for transitions between resting and sad mood brain states using functional magnetic resonance imaging (fMRI) data. Nonlinear DSR models trained on individuals with rMDD and healthy controls (HC) yielded region-specific, state-dependent control strategies. Small regions (e.g., sgACC, NAcc) showed higher controllability, requiring less energy for state transitions. Notably, rMDD participants required less control energy than HC to shift both into and, to a more spatially restricted extent, out of sad mood states. Despite reaching the resting state target with similar proximity, however, they remained closer to the sad mood distribution when returning to rest, reflecting a residual bias toward the sad mood distribution. Elevated coupling in rMDD, especially toward the DLPFC, was linked to lower control energy, suggesting that stronger network coupling facilitates transitions. These findings indicate rMDD dynamics that ease entry into sad mood states but impede full disengagement. More broadly, they demonstrate how closed-loop control applied to data-driven dynamical models can provide mechanistic insight into brain state transitions and inform future hypotheses about cognitive vulnerability or compensatory processes.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 11","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70600","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148446114","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":"BrainEnrich: Revealing Biological Insights for Imaging-Derived Phenotypes Through Transcriptomic Enrichment","authors":"Zhipeng Cao,&nbsp;Dekang Yuan,&nbsp;Jinmei Qin,&nbsp;Yujie Wu,&nbsp;Chenhu Li,&nbsp;Guilai Zhan","doi":"10.1002/hbm.70605","DOIUrl":"10.1002/hbm.70605","url":null,"abstract":"<p>While the field of imaging transcriptomics is evolving rapidly, several methodological challenges persist in functional enrichment analysis. Here, we introduce BrainEnrich, an R package that integrates whole-brain gene expression profiles from the Allen Human Brain Atlas (AHBA) with in vivo imaging-derived phenotypes (IDPs). By offering a suite of flexible association methods, aggregation strategies, comprehensive lists of predefined gene sets, and both competitive and self-contained null models, the package enables researchers to examine the spatial coupling between molecular profiles and IDPs at both group and individual levels. A novel feature of BrainEnrich is its individual-level enrichment analysis, which mapped individual IDPs onto a molecular coordinate framework, capturing the molecular signature of individual IDPs and enabling a deeper exploration of inter-individual variability. Its statistical power was examined through extensive simulation studies based on linear regression with different combinations of test statistics and null models. The results suggest that with appropriate null models, this approach effectively controlled Type 1 error while retaining sensitivity to detect associations between molecular profiles and phenotypic data. Two case studies were performed to demonstrate the utility of the package. In the group-level enrichment analysis, the effect size map of case–control comparison in cortical thickness of major depressive disorder patients was associated with molecular pathways such as synaptic signaling, lipid regulation, and steroid hormone balance, providing candidate molecular annotations for group-level IDPs. A separate case study found that synaptic gene set scores showed nominal associations with multiple cognitive measures, demonstrating its utility for individual-level molecular annotations to explore associations with phenotypic variables. Collectively, BrainEnrich provides a flexible framework for integrating macro-level IDPs with micro-level transcriptomic profiles for molecular contextualization of IDPs.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 10","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70605","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430337","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":"Group Joint ICA (gjICA): A Method for Multimodal Fusion of Concurrent EEG and fMRI Data","authors":"Souvik Phadikar,&nbsp;Mahshid Fouladivanda,&nbsp;Cyrus Eierud,&nbsp;Armin Iraji,&nbsp;Lei Wu,&nbsp;Martin P. Paulus,&nbsp;Rayus Kuplicki,&nbsp;Masaya Misaki,&nbsp;Vince D. Calhoun","doi":"10.1002/hbm.70599","DOIUrl":"10.1002/hbm.70599","url":null,"abstract":"<p>The integration of EEG and fMRI offers a powerful method for exploring the brain's spatial and temporal dynamics. However, existing approaches typically summarize both EEG and fMRI, often removing temporal information before combining the modalities. Our novel approach brings together group ICA of fMRI, group ICA of EEG, and joint ICA to propose a multimodal data fusion approach, named group joint ICA (gjICA), that links simultaneous EEG-fMRI data from multiple subjects. The proposed framework enables group-level mapping and provides single-subject estimates via back-reconstruction, facilitating a comprehensive picture of brain networks. The gjICA also introduces joint functional network connectivity (jFNC), which provides connectivity between fMRI networks as well as between EEG components, hence linking temporal and spatial information. When applied to concurrent resting EEG-fMRI data from 121 participants, we identified 63 multimodal brain components. The statistical analyses of these components further revealed that depression is characterized by widespread multimodal connectivity, with visual and higher cognition networks acting as hubs in these alterations. Further, joint histogram analysis revealed that depression is broadly associated with reduced EEG component expression with increased fMRI component expression in cerebellar, visual, subcortical, and sensorimotor networks, suggesting altered neurovascular coupling and modality-specific dysfunctions. Overall, the proposed gjICA approach provides a robust framework for fusing EEG and fMRI data while preserving the spatiotemporal information in both modalities, enabling the identification of a more comprehensive picture of multimodal neural relationships, enhancing our understanding of brain dynamics and providing new insights into complex brain disorders such as depression.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 10","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70599","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430346","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":"Unsupervised Clustering Reveals Sociodemographic Determinants of Differential Brain Development During Adolescence","authors":"Jiangyun Hou,&nbsp;Laurens van de Mortel,&nbsp;Arne Popma,&nbsp;Dirk J. A. Smit,&nbsp;Guido van Wingen","doi":"10.1002/hbm.70606","DOIUrl":"10.1002/hbm.70606","url":null,"abstract":"<p>Adolescence is a critical period for brain development, impacting social, cognitive, and emotional functions. While hypothesis-driven studies have linked multiple person characteristics to brain development, the driving force behind differential brain development remains unclear. We applied unsupervised clustering to multimodal neuroimaging data from early adolescents in the Adolescent Brain Cognitive Development (ABCD) study. Clustering analyses were conducted separately for resting-state functional MRI (rs-fMRI), structural MRI (sMRI), and diffusion MRI (dMRI), and replicated in two independent samples of 2666 individuals each. Longitudinal trajectories over a two-year follow-up period were examined. Associations with sociodemographic and family-related factors were assessed. Two clusters were identified in rs-fMRI data across both independent samples. One cluster, comprising approximately 9%–10% of individuals, showed functional brain differences at baseline, along with altered neurodevelopmental trajectories over 2 years. These functional differences were associated with lower socioeconomic status, family instability, and stronger cultural/family values. In contrast, no significant clustering emerged from structural MRI and diffusion MRI data. Our findings suggest that sociodemographic factors are closely associated with early adolescent brain function and development, underscoring the need to consider social environment in neurodevelopmental models and prevention strategies.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 10","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70606","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430352","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":"Neuroimaging Correlates of Post-Stroke Pain After Ischemic Stroke: Secondary Analysis of the INSPiRE-TMS Trial","authors":"Alina Stegemann,&nbsp;Ana Sofia Rios,&nbsp;Ahmed Khalil,&nbsp;Ulrike Grittner,&nbsp;Uchralt Temuulen,&nbsp;Ramanan Ganeshan,&nbsp;Tim Bastian Braemswig,&nbsp;Andreas Horn,&nbsp;Thomas Ihl,&nbsp;Heinrich J. Audebert,&nbsp;Anna Kufner,&nbsp;Matthias Endres","doi":"10.1002/hbm.70602","DOIUrl":"10.1002/hbm.70602","url":null,"abstract":"<p>Post-stroke pain (PSP) affects nearly half of stroke survivors, severely compromising quality of life. The causes of PSP remain underexplored, although there is likely a complex interplay of lesion effects, psychological factors, and mobility that play a role in its development. The aim of the study was to investigate clinical characteristics associated with PSP, as well as structural and functional correlates of PSP using lesion symptom (LSM) and network mapping (LNM). We analyzed data from the INSPiRE-TMS cohort, encompassing 1022 minor ischemic stroke patients. Pain severity and psychological factors (EQUATION 5D-3L questionnaire) were assessed annually for up to 3 years. In a sub-group of 391 patients with available imaging data, LSM and LNM analyses were conducted to identify neural correlates of PSP. Overall, 47% of patients reported pain 1-year post-stroke. Multivariable regression analyses identified baseline anxiety as associated with PSP assessed at 1-year post-stroke (OR 2.90, 95% CI 1.17–7.17, <i>p</i> = 0.021). LSM did not identify any voxels associated with new severe pain. LNM identified a network involving the anterior cingulate cortex, thalamus, and insular cortex. Adjusting for anxiety highlighted distinct network contributions, suggesting interactive effects of psychological states on pain perception. Automated comparison to large metanalytical findings using Neurosynth associated the terms ‘pain’ and ‘nociception’ most strongly to the identified network. PSP is closely associated with psychological factors such as anxiety. LNM of PSP revealed disruptions in a pain-related neural network consistent with prior pain research. These results warrant external validation and could guide future network-targeted neuromodulation therapies.</p><p><b>Trial Registration:</b> ClinicalTrials.gov identifier: NCT01586702</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 10","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70602","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148411501","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":"Neural Correlates of Motor Sequence Learning and Enhanced Offline Consolidation in 7–11-Year-Old Children","authors":"Anke Van Roy,&nbsp;Ainsley Temudo,&nbsp;Vincent Koppelmans,&nbsp;Kerstin Hödlmoser,&nbsp;Genevieve Albouy,&nbsp;Bradley R. King","doi":"10.1002/hbm.70579","DOIUrl":"10.1002/hbm.70579","url":null,"abstract":"<p>Many daily activities involve series of interrelated movements and thus the capacity to learn new motor sequences is vital for everyday functioning. Although learning new skills is especially prominent throughout childhood, remarkably few studies have examined the neural underpinnings of motor sequence learning (MSL) and memory consolidation in children. Twenty-two children (7–11 years) and 23 adults (18–30 years) underwent functional magnetic resonance imaging while completing two sessions of a MSL task, separated by a 5-h offline period of wakefulness. Analyses of the behavioral data revealed comparable initial learning in children and adults. However, and consistent with previous research, children exhibited superior motor memory consolidation over the 5-h offline epoch. Neuroimaging analyses revealed that children exhibited smaller modulations in brain activity between task and rest epochs in a widespread network, including the sensorimotor cortex, supplementary motor area, cerebellum, putamen, and regions associated with the default mode network. Similar levels of activity during task and rest epochs in the hippocampus, dorsolateral prefrontal cortex and somatosensory cortex were associated with better motor memory consolidation in children. Results potentially suggest that the continued engagement of the developing brain during interleaved rest contributes to the childhood advantage in motor memory consolidation.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 10","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70579","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148411532","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":"Diurnal Variations and Test–Retest Reliability of Resting-State Functional MRI Metrics","authors":"Bowen Guo,&nbsp;Kaikai Yan,&nbsp;Zhihui Liu,&nbsp;Yao Deng,&nbsp;Shanna Fu,&nbsp;Weiwei Zhao,&nbsp;Jing Xu,&nbsp;Caihong Jiang,&nbsp;Ruiwen Tao,&nbsp;Xing Chen,&nbsp;Mengmeng Wang,&nbsp;Tianxin Mao,&nbsp;Hengyi Rao","doi":"10.1002/hbm.70590","DOIUrl":"10.1002/hbm.70590","url":null,"abstract":"<p>Resting-state fMRI (rs-fMRI) is widely used to assess intrinsic brain activity, yet concerns about its test–retest reliability and reproducibility persist. Circadian rhythms strongly influence brain physiology, but their impact on rs-fMRI reliability remains poorly understood. In this study, we scanned 39 healthy young adults six times within a single day (08:00–20:00) under standardized conditions. For each session, we computed four common rs-fMRI metrics, including amplitude of low-frequency fluctuations (ALFF), wavelet-transformed ALFF (wALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo), and assessed reliability using intraclass correlation coefficients (ICCs). ReHo showed relatively higher and more stable reliability across sessions, whereas amplitude-based metrics, particularly fALFF, exhibited greater diurnal variation. Both network-level and region-specific analyses revealed low reliability in the limbic and subcortical structures, with a mid-morning dip at 10:00. Moreover, ICCs for ALFF, wALFF, and fALFF declined with increasing inter-scan intervals, whereas ReHo remained robust. These findings demonstrate diurnal fluctuations in rs-fMRI reliability, with different metrics exhibiting distinct temporal stability profiles. We recommend that scan timing and circadian influences should be explicitly considered in the design, analysis, and interpretation of future rs-fMRI studies.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 10","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70590","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148404512","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":"Talking to the Brain: Using Large Language Models as Proxies to Model Brain Semantic Features","authors":"Xin Liu,&nbsp;Ziyue Zhang,&nbsp;Jingxin Nie","doi":"10.1002/hbm.70588","DOIUrl":"10.1002/hbm.70588","url":null,"abstract":"<p>Utilizing naturalistic stimuli while maintaining experimental rigor presents a significant challenge in neuroimaging research, primarily due to the complexities of annotation and high-dimensional analysis. To address this, we introduce a novel paradigm that leverages multimodal large language models (LLMs) as high-throughput, automated annotation tools to extract human-understandable semantic features. This “talking to the brain” approach employs a Visual Question Answering (VQA) strategy to transform complex visual scenes into structured semantic feature vectors, which are then used to predict voxel-wise brain activity. As a critical methodological validation, our model successfully replicated established neural correlates for categories such as face and building. Expanding beyond these benchmarks, we mapped the cortical activation patterns of 80 diverse semantic labels and constructed a data-driven brain semantic similarity space. This space revealed robust clusters reflecting both functional categories and real-world contextual associations. This innovative methodology offers a scalable solution for investigating brain semantic organization, overcoming the limitations of manual annotation and paving the way for more ecologically valid explorations of human cognition.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 10","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148390496","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":"Brain Aging in Specific Phobia: An ENIGMA-Anxiety Mega-Analysis","authors":"Kimberly V. Blake,&nbsp;Kevin Hilbert,&nbsp;Jonathan C. Ipser,&nbsp;Laura K. M. Han,&nbsp;Janna Marie Bas-Hoogendam,&nbsp;Fredrik Åhs,&nbsp;Jochen Bauer,&nbsp;Katja Beesdo-Baum,&nbsp;Johannes Björkstrand,&nbsp;Laura Blanco-Hinojo,&nbsp;Joscha Böhnlein,&nbsp;Robin Bülow,&nbsp;Marta Cano,&nbsp;Narcis Cardoner,&nbsp;Xavier Caseras,&nbsp;Udo Dannlowski,&nbsp;Mats Fredrikson,&nbsp;Liesbet Goossens,&nbsp;Hans J. Grabe,&nbsp;Dominik Grotegerd,&nbsp;Tim Hahn,&nbsp;Alfons Hamm,&nbsp;Ingmar Heinig,&nbsp;Martin J. Herrmann,&nbsp;David Hofmann,&nbsp;Hamidreza Jamalabadi,&nbsp;Andreas Jansen,&nbsp;Merel Kindt,&nbsp;Tilo Kircher,&nbsp;Anna L. Klahn,&nbsp;Katja Koelkebeck,&nbsp;Axel Krug,&nbsp;Elisabeth J. Leehr,&nbsp;Martin Lotze,&nbsp;Juergen Margraf,&nbsp;Markus Muehlhan,&nbsp;Igor Nenadić,&nbsp;Wenceslao Peñate,&nbsp;Andre Pittig,&nbsp;Jens Plag,&nbsp;Jesús Pujol,&nbsp;Jan Richter,&nbsp;Isabelle C. Ridderbusch,&nbsp;Francisco Rivero,&nbsp;Axel Schäfer,&nbsp;Judith Schäfer,&nbsp;Anne Schienle,&nbsp;Elisabeth Schrammen,&nbsp;Koen Schruers,&nbsp;Esther Seidl,&nbsp;Rudolf M. Stark,&nbsp;Benjamin Straube,&nbsp;Thomas Straube,&nbsp;Andreas Ströhle,&nbsp;Boris Suchan,&nbsp;Lea Teutenberg,&nbsp;Sophia I. Thomopoulos,&nbsp;Carlos Ventura-Bort,&nbsp;Renee M. Visser,&nbsp;Henry Völzke,&nbsp;Albert Wabnegger,&nbsp;André Wannemüller,&nbsp;Julia Wendt,&nbsp;Hans-Ulrich Wittchen,&nbsp;Katharina Wittfeld,&nbsp;Yunbo Yang,&nbsp;Anna Zilverstand,&nbsp;Peter Zwanzger,&nbsp;Lianne Schmaal,&nbsp;Moji Aghajani,&nbsp;Daniel S. Pine,&nbsp;Paul M. Thompson,&nbsp;Nic J. A. van der Wee,&nbsp;Dan J. Stein,&nbsp;Ulrike Lueken,&nbsp;Nynke A. Groenewold","doi":"10.1002/hbm.70595","DOIUrl":"10.1002/hbm.70595","url":null,"abstract":"<p>Specific phobia (SPH) is a prevalent anxiety disorder and may involve advanced biological aging. However, limited brain age research has been conducted in anxiety disorders. This mega-analysis investigated brain aging in SPH participants within the ENIGMA-Anxiety Working Group. 3D brain structural MRI scans from 17 international samples (600 SPH individuals, of whom 504 formally diagnosed and 96 questionnaire-based cases; 1134 controls; age range: 22–75 years) were processed with FreeSurfer. Brain age was estimated from 77 subcortical and cortical regions with a publicly available ENIGMA brain age model. The brain-predicted age difference (brain-PAD) was calculated as brain age minus chronological age. Linear mixed-effects models examined group differences in brain-PAD and moderation by age. No significant group difference in brain-PAD manifested (<i>β</i>_diagnosis [SE] = 0.37 years [0.43], <i>p</i> = 0.39). A negative diagnosis-by-age interaction was identified, which was most pronounced in formally diagnosed SPH (<i>β</i>_{diagnosis-by-age} = −0.08 [0.03], pFDR = 0.02). This interaction remained significant when excluding participants with anxiety comorbidities, depressive comorbidities, and medication use. Post hoc analyses revealed a group difference for formal SPH diagnosis in younger participants (22–35 years; <i>β</i>_diagnosis = 1.20 [0.60], <i>p</i> &lt; 0.05, mixed-effects <i>d</i> [95% confidence interval] = 0.14 [0.00–0.28]), but not older participants (36–75 years; <i>β</i>_diagnosis = 0.07 [0.65], <i>p</i> = 0.91). Brain aging did not relate to SPH in the full sample. However, a diagnosis-by-age interaction was observed across analyses, and was strongest in formally diagnosed SPH. Post hoc analyses showed subtle advanced brain aging in young adults with formally diagnosed SPH. Taken together, these findings indicate the importance of clinical severity, impairment, and persistence, and may suggest a slightly earlier end to maturational processes or subtle decline of brain structure in SPH.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 10","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70595","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148390436","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":"Emotional Context Modulates the Response to Somatosensory Stimuli Within 20 milliseconds","authors":"Luis Carretié,&nbsp;Vanessa E. DaSilva-Lárez,&nbsp;Victoria Branchadell,&nbsp;Uxía Fernández-Folgueiras","doi":"10.1002/hbm.70601","DOIUrl":"10.1002/hbm.70601","url":null,"abstract":"<p>The processing of discrete stimuli is modulated by the emotional context in which they appear. According to the limited data available, this modulation could occur from the initial levels of processing. Our aim was to find the first trace of emotional context modulation in the neural response to discrete stimuli presented within that context. We focused on the somatosensory domain, characterized by faster and better localized responses within the ascending pathway than the more widely studied visual modality. Early components of somatosensory evoked potentials (SEPs), particularly those reflecting the thalamic and primary somatosensory cortex activity occurring within the first 20 milliseconds from stimulus onset, were analyzed to test whether they are already affected by the emotional context. Participants (<i>n</i> = 37) received somatosensory stimulation via electrical pulses over the median nerve of the right arm while viewing 12 stereoscopic (3D) images of animals categorized as emotionally positive, negative, or neutral to generate different affective contexts. SEPs elicited by somatosensory stimuli—which were identical in all contexts- were recorded through a bipolar CP3-CP4 channel, revealing a P17 (originating in the thalamus) and an N20 (primary somatosensory cortex or S1) component. The negative contexts evoked significantly smaller P17-N20 peak-to-peak amplitudes than positive and neutral contexts, an attenuation of somatosensory perception probably reflecting a rapid defensive mechanism. These results reveal the earliest emotion-modulated brain response described to date in humans (17–20 milliseconds and thalamic/S1 origin) and point to the somatosensory ascending pathway as an active emotional processor.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 10","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70601","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148390472","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}