Human Brain Mapping最新文献

{"title":"Resting-State Brain Activity Changes and Their Genetic Correlates in Mild Traumatic Brain Injury","authors":"Lu Wang,&nbsp;Yijing Zhang,&nbsp;He Wang,&nbsp;Xinyu Wang,&nbsp;Wei Wang,&nbsp;Jin Qiao,&nbsp;Zhihui Zhang,&nbsp;Minghuan Lei,&nbsp;Wenjie Cai,&nbsp;Qi An,&nbsp;Linlin Song,&nbsp;Feng Liu,&nbsp;Juanwei Ma","doi":"10.1002/hbm.70259","DOIUrl":"https://doi.org/10.1002/hbm.70259","url":null,"abstract":"<p>Mild traumatic brain injury (mTBI) often leads to persistent cognitive and emotional symptoms, but the underlying neurobiological mechanisms remain unclear. Although previous studies have reported alterations in resting-state brain activity in mTBI patients, the findings have been inconsistent, and the genetic basis of these changes has not been fully explored. A coordinate-based voxel-wise meta-analysis was conducted to investigate resting-state brain activity changes in mTBI, using nine datasets from 374 patients and 302 healthy controls (HCs). Transcription-neuroimaging association analyses were performed using gene expression data from the Allen Human Brain Atlas (AHBA) to identify genes associated with brain activity alterations. Enrichment analyses were conducted to explore the biological functions of these genes. Compared to HCs, mTBI patients showed increased resting-state brain activity in the left insula and right fusiform gyrus, and decreased activity in the bilateral middle frontal gyrus. Transcription-neuroimaging association analyses identified 840 genes significantly correlated with these brain activity changes. Enrichment analyses revealed 15 biological processes significantly associated with the identified genes, primarily involving chemical synaptic transmission, multicellular organism development, and cell–cell signaling. These genes were also enriched in Pnoc+, Ntsr+, and Cort+ neurons and were expressed predominantly from the late fetal to early adulthood stages. Our findings suggest that alterations in resting-state brain activity in mTBI are linked to specific gene expression patterns, highlighting potential biological pathways involved in mTBI-related brain changes.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273352","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":"Coupled Aging of Cyto- and Myeloarchitectonic Atlas-Informed Gray and White Matter Structural Properties","authors":"David A. Hoagey,&nbsp;Ekarin E. Pongpipat,&nbsp;Karen M. Rodrigue,&nbsp;Kristen M. Kennedy","doi":"10.1002/hbm.70244","DOIUrl":"https://doi.org/10.1002/hbm.70244","url":null,"abstract":"<p>A key aspect of brain aging that remains poorly understood is its high regional heterogeneity and heterochronicity. A better understanding of how the structural organization of the brain shapes aging trajectories is needed. Neuroimaging tissue “types” are often collected and analyzed as separate acquisitions, an approach that cannot provide a holistic view of age-related change in the related portions of the neurons (cell bodies and axons). Because neuroimaging can only assess indirect features at the gross macrostructural level, incorporating post-mortem histological information may aid in a better understanding of structural aging gradients. Longitudinal design, coupling of gray and white matter (GM and WM) properties, and a biologically informed approach to organizing neural properties are needed. Thus, we tested aging of the regional coupling between GM (cortical thickness, surface area, volume) and WM (fractional anisotropy, mean, axial, and radial diffusivities) structural metrics using linear mixed effects modeling in 102 healthy adults aged 20–94 years old, scanned on two occasions over a four-year period. The association between age-related within-person change in GM morphometry and the diffusion properties of the directly neighboring portion of white matter was assessed, capturing both aspects of neuronal health in one model. Additionally, we parcellated the brain utilizing the histological-staining informed von Economo-Koskinas atlas to consider regional cyto- and myelo-architecture. Results demonstrate several gradients of coupled association in the age-related decline of neighboring white and gray matter. Most notably, gradients of coupling along the heteromodal association to sensory axis were found for several areas (e.g., anterior frontal and lateral temporal cortices, vs. pre- and post-central gyrus, occipital, and limbic areas), in line with heterochronicity and retrogenesis theories of aging. Further effort to bridge across data and measurement scales will enhance understanding of the mechanisms of the aging brain.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70244","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273351","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":"Single-Subject Network Analysis of FDOPA PET in Parkinson's Disease and Psychosis Spectrum","authors":"Mario Severino,&nbsp;Julia J. Schubert,&nbsp;Giovanna Nordio,&nbsp;Alessio Giacomel,&nbsp;Rubaida Easmin,&nbsp;Nick P. Lao-Kaim,&nbsp;Pierluigi Selvaggi,&nbsp;Zhilei Xu,&nbsp;Joana B. Pereira,&nbsp;Sameer Jauhar,&nbsp;Paola Piccini,&nbsp;Oliver Howes,&nbsp;Federico Turkheimer,&nbsp;Mattia Veronese,&nbsp;FDOPA PET Imaging Working Group Consortium","doi":"10.1002/hbm.70253","DOIUrl":"https://doi.org/10.1002/hbm.70253","url":null,"abstract":"<p>Greater understanding of individual biological differences is essential for developing more targeted treatment approaches to complex brain disorders. Traditional analysis methods in molecular imaging studies have primarily focused on quantifying tracer binding in specific brain regions, often neglecting inter-regional functional relationships. In this study, we propose a statistical framework that combines molecular imaging data with perturbation covariance analysis to construct single-subject networks and investigate individual patterns of molecular alterations. This framework was tested on [18F]-DOPA PET imaging as a marker of the brain dopamine system in patients with Parkinson's Disease (PD) and schizophrenia to evaluate its ability to classify patients and characterize their disease severity. Our results show that single-subject networks effectively capture molecular alterations, differentiate individuals with heterogeneous conditions, and account for within-group variability. Moreover, the approach successfully distinguishes between preclinical and clinical stages of psychosis and identifies the corresponding molecular connectivity changes in response to antipsychotic medications. Mapping molecular imaging networks presents a new and powerful method for characterizing individualized disease trajectories as well as for evaluating treatment effectiveness in future research.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70253","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264380","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":"Misspelled-Word Reading Modulates Late Cortical Dynamics","authors":"Jiaxin You,&nbsp;Aino Saranpää,&nbsp;Tiina Lindh-Knuutila,&nbsp;Marijn van Vliet,&nbsp;Riitta Salmelin","doi":"10.1002/hbm.70247","DOIUrl":"https://doi.org/10.1002/hbm.70247","url":null,"abstract":"<p>Literate humans can effortlessly interpret tens of thousands of words, even when the words are sometimes written incorrectly. This phenomenon suggests a flexible nature of reading that can endure a certain amount of noise. In this study, we investigated where and when brain responses diverged for conditions where misspelled words were resolved as real words or not. We used magnetoencephalography (MEG) to track the cortical activity as the participants read words with different degrees of misspelling that were perceived to range from real words to complete pseudowords, as confirmed by their behavioral responses. In particular, we were interested in how lexical information survives (or not) along the uncertainty spectrum, and how the corresponding brain activation patterns evolve spatiotemporally. We identified three brain regions that were notably modulated by misspellings: left ventral occipitotemporal cortex (vOT), superior temporal cortex (ST), and precentral cortex (pC). This suggests that resolving misspelled words into stored concepts involves an interplay between orthographic, semantic, and phonological processing. Temporally, these regions showed fairly late and sustained responses selectively to misspelled words. Specifically, an increasing level of misspelling increased the response in ST from 300 ms after stimulus onset; a functionally fairly similar but weaker effect was observed in pC. In vOT, misspelled words were sharply distinguished from real words, notably later, after 700 ms. A linear mixed effects (LME) analysis further showed that pronounced and long-lasting misspelling effects appeared first in ST and then in pC, with shorter-lasting activation also observed in vOT. We conclude that reading misspelled words engages brain areas typically associated with language processing, but in a manner that cannot be interpreted merely as a rapid feedforward mechanism. Instead, feedback interactions likely contribute to the late effects observed during misspelled-word reading.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70247","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264672","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":"ComBatLS: A Location- and Scale-Preserving Method for Multi-Site Image Harmonization","authors":"Margaret Gardner,&nbsp;Russell T. Shinohara,&nbsp;Richard A. I. Bethlehem,&nbsp;Rafael Romero-Garcia,&nbsp;Varun Warrier,&nbsp;Lena Dorfschmidt,&nbsp;Lifespan Brain Chart Consortium,&nbsp;Sheila Shanmugan,&nbsp;Paul Thompson,&nbsp;Jakob Seidlitz,&nbsp;Aaron F. Alexander-Bloch,&nbsp;Andrew A. Chen","doi":"10.1002/hbm.70197","DOIUrl":"https://doi.org/10.1002/hbm.70197","url":null,"abstract":"<p>Recent study has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals' morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features' variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features' locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals' normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic “sites.” Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70197","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255907","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":"Microstructural Characterization of Short Association Fibers Related to Long-Range White Matter Tracts in Normative Development","authors":"Chloe Cho,&nbsp;Maxime Chamberland,&nbsp;Francois Rheault,&nbsp;Daniel Moyer,&nbsp;Bennett A. Landman,&nbsp;Kurt G. Schilling","doi":"10.1002/hbm.70255","DOIUrl":"https://doi.org/10.1002/hbm.70255","url":null,"abstract":"<p>Short association fibers (SAFs) in the superficial white matter play a key role in mediating local cortical connections but have not been well-studied as innovations in whole-brain diffusion tractography have only recently been developed to study superficial white matter. Characterizing SAFs and their relationship to long-range white matter tracts is crucial to advance our understanding of neurodevelopment during the period from childhood to young adulthood. This study aims to (1) map SAFs in relation to long-range white matter tracts, (2) characterize typical neurodevelopmental changes across these white matter pathways, and (3) investigate the relationship between microstructural changes in SAFs and long-range white matter tracts. Leveraging a cohort of 616 participants ranging in age from 5.6 to 21.9 years old, we performed quantitative diffusion tractography and advanced diffusion modeling with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Robust linear regression models were applied to analyze microstructural features, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), intracellular volume fraction (ICVF), isotropic volume fraction (ISOVF), and orientation dispersion index (ODI). Our results reveal that both SAFs and long-range tracts exhibit similar overall developmental patterns, characterized by negative associations of MD, AD, and RD with age and positive associations of FA, ICVF, ISOVF, and ODI with age. Notably, FA, AD, and ODI exhibit significant differences between SAFs and long-range tracts, suggesting distinct neurodevelopmental trajectories between superficial and deep white matter. In addition, significant differences were found in MD, RD, and ICVF between males and females, highlighting variations in neurodevelopment. This normative study provides insights into typical microstructural changes of SAFs and long-range white matter tracts during development, laying a foundation for future research to investigate atypical development and dysfunction in disease pathology.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70255","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244639","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":"Neurophysiological Basis of Emotional Face Perception and Working Memory Load in a Dual-Task MEG Study","authors":"Katharina Lingelbach,&nbsp;Jochem W. Rieger","doi":"10.1002/hbm.70242","DOIUrl":"https://doi.org/10.1002/hbm.70242","url":null,"abstract":"<p>Research on the neurophysiological effects of emotional face processing, working memory (WM) load, and their interaction in dual-tasks remains scarce. Therefore, we conducted a combined magnetoencephalography eye-tracking study with 47 participants. The dual-task temporally interleaved a facial emotion discrimination task with a visuo-spatial n-back task. Source-space cluster analyzes of event-related magnetic fields (ERFs) and oscillations revealed significant main effects of emotional expression and WM load. During emotion discrimination, enhanced ERFs for negative facial expressions located across the insula, ACC, and face-specific occipital regions suggest amplified emotion processing but also the recruitment of attentional control mechanisms. During the n-back phase, emotional faces did not affect evoked responses when they were task-irrelevant. Interaction trends in pupil dilation indicated that emotion-specific processing is diminished under high WM load. During the n-back phase, increased WM load reduced alpha and low beta oscillations in temporo- and parieto-occipital areas. In addition, reduced target fixations in the presence of negative facial distractors indicated a tendency toward emotion-specific interference. Furthermore, sustained increased WM load affected perceived valence, pupil size, and reaction time in both subtasks. A convergence of neurophysiological, physiological, and behavioural findings points to specific processing modes with greater resource depletion for negative expressions and high WM load in the dual-task. In conclusion, the study advanced our understanding of (a) circumstances under which emotional faces modulate ERFs in a dual-task, (b) mechanisms underlying emotion discrimination, (c) interaction effects of emotional expression and WM load in gaze behavior, as well as (d) how WM-related oscillatory alpha and beta power is affected by increasing load.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244638","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":"Do Transformers and CNNs Learn Different Concepts of Brain Age?","authors":"Nys Tjade Siegel,&nbsp;Dagmar Kainmueller,&nbsp;Fatma Deniz,&nbsp;Kerstin Ritter,&nbsp;Marc-Andre Schulz","doi":"10.1002/hbm.70243","DOIUrl":"https://doi.org/10.1002/hbm.70243","url":null,"abstract":"<p>“Predicted brain age” refers to a biomarker of structural brain health derived from machine learning analysis of T1-weighted brain magnetic resonance (MR) images. A range of machine learning methods have been used to predict brain age, with convolutional neural networks (CNNs) currently yielding state-of-the-art accuracies. Recent advances in deep learning have introduced transformers, which are conceptually distinct from CNNs, and appear to set new benchmarks in various domains of computer vision. Given that transformers are not yet established in brain age prediction, we present three key contributions to this field: First, we examine whether transformers outperform CNNs in predicting brain age. Second, we identify that different deep learning model architectures potentially capture different (sub-)sets of brain aging effects, reflecting divergent “concepts of brain age”. Third, we analyze whether such differences manifest in practice. To investigate these questions, we adapted a Simple Vision Transformer (sViT) and a shifted window transformer (SwinT) to predict brain age, and compared both models with a ResNet50 on 46,381 T1-weighted structural MR images from the UK Biobank. We found that SwinT and ResNet performed on par, though SwinT is likely to surpass ResNet in prediction accuracy with additional training data. Furthermore, to assess whether sViT, SwinT, and ResNet capture different concepts of brain age, we systematically analyzed variations in their predictions and clinical utility for indicating deviations in neurological and psychiatric disorders. Reassuringly, we observed no substantial differences in the structure of brain age predictions across the model architectures. Our findings suggest that the choice of deep learning model architecture does not appear to have a confounding effect on brain age studies.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70243","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244694","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 Patients With Cardiovascular Disease From the UK Biobank","authors":"Elizabeth Mcavoy,&nbsp;Emma A. M. Stanley,&nbsp;Anthony J. Winder,&nbsp;Matthias Wilms,&nbsp;Nils D. Forkert","doi":"10.1002/hbm.70252","DOIUrl":"https://doi.org/10.1002/hbm.70252","url":null,"abstract":"<p>The brain undergoes complex but normal structural changes during the aging process in healthy adults, whereas deviations from the normal aging patterns of the brain can be indicative of various conditions as well as an increased risk for the development of diseases. The brain age gap (BAG), which is defined as the difference between the chronological age and the machine learning-predicted biological age of an individual, is a promising biomarker for determining whether an individual deviates from normal brain aging patterns. While the BAG has shown promise for various neurological diseases and cardiovascular risk factors, its utility to quantify brain changes associated with diagnosed cardiovascular diseases has not been investigated to date, which is the aim of this study. T1-weighted MRI scans from healthy participants in the UK Biobank were used to train a convolutional neural network (CNN) model for biological brain age prediction. The trained model was then used to quantify and compare the BAGs for all participants in the UK Biobank with known cardiovascular diseases, as well as healthy controls and patients with known neurological diseases for benchmark comparisons. Saliency maps were computed for each individual to investigate whether brain regions used for biological brain age prediction by the CNN differ between groups. The analyses revealed significant differences in BAG distributions for 10 of the 42 sex-specific cardiovascular disease groups investigated compared to healthy participants, indicating disease-specific variations in brain aging. However, no significant differences were found regarding the brain regions used for brain age prediction as determined by saliency maps, indicating that the model mostly relied on healthy brain aging patterns, even in the presence of cardiovascular diseases. Overall, the findings of this work demonstrate that the BAG is a sensitive imaging biomarker to detect differences in brain aging associated with specific cardiovascular diseases. This further supports the theory of the heart–brain axis by exemplifying that many cardiovascular diseases are associated with atypical brain aging.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244702","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":"Neuroanatomical Changes in the Stopping Network Across the Adult Lifespan Assessed With Quantitative and Diffusion MRI","authors":"Sarah A. Kemp,&nbsp;Pierre-Louis Bazin,&nbsp;Steven Miletić,&nbsp;Russell J. Boag,&nbsp;Max C. Keuken,&nbsp;Mark R. Hinder,&nbsp;Birte U. Forstmann","doi":"10.1002/hbm.70240","DOIUrl":"https://doi.org/10.1002/hbm.70240","url":null,"abstract":"<p>Response inhibition, the cancellation of planned movement, is essential for everyday motor control. Extensive fMRI and brain stimulation research provides evidence for the crucial role of a number of cortical and subcortical regions in response inhibition, including the subthalamic nucleus (STN), presupplementary motor area (preSMA) and the inferior frontal gyrus (IFG). Current models assume that these regions operate as a network, with action cancellation originating in the cortical areas and then executed rapidly via the subcortex. Response inhibition slows in older age, a change that has been attributed to deterioration or changes in the connectivity and integrity of this network. However, previous research has mainly used whole-brain approaches when investigating changes in structural connectivity across the lifespan or has used simpler measures to investigate structural ageing. Here, we used high-resolution quantitative and diffusion MRI to extensively examine the anatomical changes that occur in this network across the lifespan. We found age-related changes in iron concentration in these tracts, increases in the apparent diffusion coefficient and some evidence for a decrease in myelin content. Conversely, we found very little evidence for age-related anatomical changes in the regions themselves. We propose that some of the functional changes observed in these regions in older adult populations (e.g., increased BOLD recruitment) are a reflection of alterations to the connectivity between the regions rather than localised regional change.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70240","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213978","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}