Human Brain Mapping最新文献

{"title":"The Impact of Early Life Exposure to Air Pollution on the Brain: A Diffusion MRI Study in 10–13-Year-Old Children With and Without ADHD Diagnosis","authors":"Paulina Lewandowska,&nbsp;Claude J. Bajada,&nbsp;Yarema Mysak,&nbsp;Aleksandra Domagalik,&nbsp;Bartosz Kossowski,&nbsp;Clemens Baumbach,&nbsp;Katarzyna Kaczmarek-Majer,&nbsp;Anna Degórska,&nbsp;Krzysztof Skotak,&nbsp;Katarzyna Sitnik-Warchulska,&nbsp;Małgorzata Lipowska,&nbsp;Bernadetta Izydorczyk,&nbsp;James Grellier,&nbsp;Iana Markevych,&nbsp;Marcin Szwed","doi":"10.1002/hbm.70306","DOIUrl":"https://doi.org/10.1002/hbm.70306","url":null,"abstract":"<p>Recent studies indicate that air pollutants not only increase the risk of cardiovascular and respiratory diseases but also have a negative impact on the developing brain. Exposure to airborne particulate matter (PM) and nitrogen dioxide (NO₂) may lead to disruption of neural development by interfering with critical maturation processes. In this study, we assessed the impact of prenatal and early life PM₁₀ and NO₂ exposure on diffusion Magnetic Resonance Imaging (dMRI) structural measures: fractional anisotropy (FA), mean diffusivity (MD), and fixel-based analysis (FBA) on a population of 425 10- to 13-year-old children with attention deficit hyperactivity disorder (ADHD, <i>n</i> = 116), a sensitive, at-risk population, and typically developing children (TD, <i>n</i> = 309) from the NeuroSmog study. Unlike traditional voxel-based methods, FBA allows identification of distinct fiber bundles within voxels. We show that early life exposure to NO₂ was associated with lower global FA and higher MD measures. However, despite having a large sample size and using state-of-the-art techniques, we found no significant fixel-level associations. Notably, we found no evidence that individuals with ADHD are more susceptible to the effects of air pollution. Combined with other studies, our results suggest that dMRI measures are the brain outcomes most consistently affected by air pollution.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 14","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70306","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111190","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":"Disrupted Structure–Function Integration in Systemic Lupus Erythematosus and Its Impact on Cognitive Flexibility","authors":"Xing Qian,&nbsp;Dani S. Bassett,&nbsp;Kwun Kei Ng,&nbsp;Beatrice R. Y. Loo,&nbsp;Roger Chun-man Ho,&nbsp;Anselm Mak,&nbsp;Juan Helen Zhou","doi":"10.1002/hbm.70299","DOIUrl":"10.1002/hbm.70299","url":null,"abstract":"<p>Systemic lupus erythematosus (SLE) is a chronic autoimmune disease, with cognitive dysfunction being one of its most common neuropsychiatric manifestations. Cognitive flexibility relies on the integration of brain structure and function, with white matter networks providing anatomical constraints for functional dynamics. Reduced cognitive flexibility is frequently observed in SLE, but the underlying structure–function integration changes remain poorly understood. This study investigated whether brain structure–function integration is altered in SLE and how it links to cognitive flexibility. We examined 22 SLE patients without clinically overt neuropsychiatric manifestation (age: 34.99 ± 10.67; 18 females) and 60 healthy controls (HCs) (age: 28.43 ± 8.56; 29 females). Using diffusion MRI and task-based fMRI acquired during the Montreal Card Sorting Test (MCST), a cognitive flexibility task, we derived brain structural–functional alignment and liberality, which quantify the extent to which brain functional signals are either coupled with or deviate from the underlying anatomical network. We found SLE patients exhibited globally higher liberality and lower alignment compared to HCs, and this was driven by the disrupted structure–function integration in the executive control network (ECN). The ECN comprises three subnetworks: ECN-A and ECN-B comprise key lateral fronto-parietal executive control areas, while ECN-C is anatomically closer to the default mode network. Further analyses revealed that SLE had higher liberality in ECN-A and ECN-B regions, alongside lower alignment in ECN-A, while ECN-C did not show these alterations. Importantly, increased liberality and decreased alignment in the ECN regions were associated with poorer cognitive flexibility (MCST performance) in SLE participants. This association was also observed across all participants. In SLE individuals specifically, liberality and alignment in the fronto-parietal ECN were further linked to clinical variables, including serum albumin and corticosteroid dosage. Additionally, the liberality and alignment in the ECN and its subnetworks were associated with cognitive performance outside the scanner (measured by Automated Neuropsychological Assessment Metrics) across all participants. Our findings suggest that aberrant structure–function integration, particularly within the fronto-parietal ECN, impacts cognitive flexibility and may contribute to the development of cognitive impairment in SLE.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 14","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12454912/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145124601","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":"Individual Differences in Developmental Trajectories of Global and Subcortical Brain Volumes Between Late Childhood and Late Adolescence: Findings From a 12-Wave Neuroimaging Study","authors":"Chloe Carrick,&nbsp;William Frans Christiaan Baaré,&nbsp;Silia Vitoratou,&nbsp;Kathrine Skak Madsen,&nbsp;Delia Fuhrmann","doi":"10.1002/hbm.70348","DOIUrl":"https://doi.org/10.1002/hbm.70348","url":null,"abstract":"<p>Adolescence is characterised by protracted structural brain development, with different brain regions showing distinct developmental trajectories. While studies have identified average developmental trajectories, few have formally quantified individual differences in the developmental trajectories of global brain structures and subcortical regions across adolescence. Utilising the unique 12 waves of high temporal resolution MRI data from the Danish HUBU cohort (<i>N</i> = 90; ages seven to 21; 745 scans; on average 8.30 scans per participant) and nonlinear mixed modelling techniques, we examined both group and individual-level patterns of volumetric change in global brain measures and subcortical regions. At the group level, cortical grey matter, total brain, caudate, putamen, accumbens, and thalamus volume decreased, while white matter, amygdala, hippocampus, and pallidum volume increased. We observed substantial interindividual variability in the rate of volumetric change in the caudate, as well as in the age at which cortical grey matter, white matter, and pallidum volumes changed most rapidly. For instance, the age of most rapid cortical volumetric decline varied by up to 7.5 years among individuals. Maturational trajectories also differed by sex. Our findings quantify overall trajectories, as well as individual and sex differences in volumetric development in subcortical and global brain volumes. Future research can build upon these findings to investigate the extrinsic and intrinsic factors that influence interindividual variations in developmental trajectories of adolescent brain structure, as well as how they relate to later-life outcomes including mental health.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 14","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70348","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111189","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":"Selective Attention Dynamically Modulates the Hierarchical Order of Perceptual and Conceptual Representations","authors":"Yu Zhou,&nbsp;Liang Zhang,&nbsp;Nikolai Axmacher,&nbsp;Daniel Pacheco Estefan,&nbsp;Dahui Wang,&nbsp;Yujian Dai,&nbsp;Xiaojing Peng,&nbsp;Shixiang Liu,&nbsp;Gui Xue","doi":"10.1002/hbm.70359","DOIUrl":"10.1002/hbm.70359","url":null,"abstract":"<p>Mounting evidence suggests that information processing in the visual hierarchy involves a progression from low-level perceptual to high-level conceptual features during visual perception, and a reverse traversal during memory retrieval. However, the nature of this processing hierarchy and its modulation by selective attention remain unclear. By using the drift-diffusion model, we found that slower reaction times for conceptual versus perceptual tasks were primarily due to differences in decision boundaries and nondecision times, which were not compensated by faster evidence accumulation for conceptual features. Using single-trial multivariate decoding of magnetoencephalography (MEG) data, we tracked the temporal dynamics of feature representation during visual perceptual and mnemonic tasks. During perception, selective attention reversed the onset times of perceptual and conceptual features in occipital and parietal lobes, enabling earlier detection of conceptual features. Stronger theta oscillation interactions between occipital and temporal regions during task preparation correlated with earlier onset times of target features in the occipital lobe. During retrieval, selective attention led to earlier peak times for perceptual compared to conceptual features in the frontal lobe. These findings provide novel insights into the dynamic nature of hierarchical processing during perception and memory retrieval, highlighting the critical role of selective attention in modulating information accumulation speed.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 14","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145091598","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":"The Impact of Multiband and In-Plane Acceleration on White Matter Microstructure Analysis","authors":"Zhengwu Zhang,&nbsp;Arun Venkataraman,&nbsp;Martin Cole,&nbsp;Tianrui Ye,&nbsp;Deqiang Qiu,&nbsp;Feng V. Lin,&nbsp;Benjamin B. Risk","doi":"10.1002/hbm.70353","DOIUrl":"10.1002/hbm.70353","url":null,"abstract":"&lt;p&gt;Accelerated imaging in diffusion MRI has been widely used to reduce scan time. This can be particularly important in reducing the burden in patients, such as those with mild cognitive impairment (MCI). However, the impact on reliability is not fully understood. Moreover, the impact on effect sizes in group comparisons has not been examined. We conducted a test–retest study of the impact of simultaneous multislice (SMS, also called multiband) and in-plane acceleration (IPA, also called phase acceleration) on reliability and effect sizes in diffusion imaging in MCI, healthy older adults, and young adults. We evaluated diffusion tensor imaging measures (fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) and neurite orientation and dispersion measures (orientation dispersion, isotropic volume fraction, intracellular volume fraction) under no acceleration (S1P1), SMS = 3 with no in-plane acceleration (S3P1), SMS = 3 with IPA = 2 (S3P2), S6P1, and S6P2, with scan times varying from over 20 min in S1P1 to under 4 min in S6P2. In white matter voxels, the ranking of the accelerations with respect to intraclass correlations (ICCs) was S1P1 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;≈&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ approx $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; S3P1 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;≥&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ ge $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; S3P2 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;&gt;&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ &gt; $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; S6P1 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;&gt;&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ &gt; $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; S6P2, with ICCs in the good range across most DWI measures in S1P1, S3P1, and S3P2, moderate to good in S6P1, and poor to moderate in S6P2. In-plane acceleration did not improve ICC in areas of high susceptibility distortion. Acceleration significantly impacted the values of white matter microstructure with an overall trend of increase in fractional anisotropy and decrease in orientation dispersion with increasing multiband acceleration. In group comparisons, effect sizes tended to be similar across S1P1, S3P1, S3P2, and S6P1, including medium effect sizes in MCI versus healthy older adults and large effect sizes in young versus healthy older adults. Our results provide guidance regarding the costs of acceleration (reduced ICC from high acceleration) while also characterizing the benefits (S3P1 has similar reliability as S1P1 while requiring one third of the acquisition time, ROI-level group comparisons si","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 14","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145091519","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":"Integration of Genetic Information to Improve Brain Age Gap Estimation Models in the UK Biobank","authors":"Aashka Mohite,&nbsp;Karen Ardila,&nbsp;Pattarawut Charatpangoon,&nbsp;Emily Munro,&nbsp;Qingrun Zhang,&nbsp;Quan Long,&nbsp;Charlotte Curtis,&nbsp;M. Ethan MacDonald","doi":"10.1002/hbm.70331","DOIUrl":"10.1002/hbm.70331","url":null,"abstract":"<p>Neurodegeneration occurs when the body's central nervous system becomes impaired as a person ages, which can happen at an accelerated pace. Neurodegeneration impairs quality of life, affecting essential functions, including memory and the ability to self-care. Genetics play an important role in neurodegeneration and longevity. Brain age gap estimation (BrainAGE) is a biomarker that quantifies the difference between a machine learning model-predicted biological age of the brain and the true chronological age for healthy subjects; however, a large portion of the variance remains unaccounted for in these models, attributed to individual differences. This study focuses on predicting the BrainAGE more accurately, aided by genetic information associated with neurodegeneration. To achieve this, a BrainAGE model was developed based on MRI measures, and then the associated genes were determined with a Genome-Wide Association Study. Subsequently, genetic information was incorporated into the models. The incorporation of genetic information yielded improvements in the model performances by 7% to 12%, showing that the incorporation of genetic information can notably reduce unexplained variance. This work helps to define new ways of determining persons susceptible to neurological aging decline and reveals genes for targeted precision medicine therapies.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 14","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145091595","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":"Imaging Developmental Trajectories of Laminar Magnetic Susceptibility Throughout Adulthood in Human Brain Cortex","authors":"Xin Wang,&nbsp;Jiangjie Wu,&nbsp;Xiaojun Guan,&nbsp;Xiaojun Xu,&nbsp;Hongjiang Wei,&nbsp;Yuyao Zhang","doi":"10.1002/hbm.70325","DOIUrl":"10.1002/hbm.70325","url":null,"abstract":"<p>Iron, myelin, and proteins are critical for neural integrity and cognitive function, yet their concentrations and distributions in brain tissue vary significantly across the lifespan. This natural variability necessitates age-specific reference ranges that account for developmental and aging-related changes. Noninvasive imaging techniques, such as Quantitative Susceptibility Mapping (QSM) and R2* imaging, provide complementary insights into these dynamics. Although age-related susceptibility trajectories have been well characterized in subcortical regions, systematic investigations of laminar susceptibility across the entire cortex remain limited. In this study, we analyzed QSM and R2* images from 447 healthy adults (18–80 years) to characterize cortical susceptibility profiles. Rather than averaging values across entire cortical regions, we normalized cortical depth to a percentage scale (1%–100%), where 1% corresponds to the pial surface and 100% to the white matter boundary. Image intensities at varying cortical depths were extracted and averaged to estimate depth-specific iron levels, motivated by known layer-dependent iron distribution in the cortex. Our results support depth-specific spatial patterns of iron in QSM and R2*: QSM peaked in deep cortical layers (80%–99%), with higher frontal and occipital values compared with temporal and parietal regions. Conversely, elevated R2* in superficial temporal and occipital layers suggests region-specific microstructural variations. Both QSM and R2* exhibited quadratic age-related trajectories, increasing until midlife before declining slightly. Unlike in white matter (WM) and deep gray matter (GM), cortical iron and myelin distributions exhibit distinct patterns, rendering conventional susceptibility-iron models for WM/GM unsuitable for cortical regions. To address this limitation, we focused on high-iron cortical layers, where diamagnetic confounds are minimized. By calibrating our in vivo age-QSM data against the ex vivo age-iron reference curve, we developed an optimized linear transformation and used a specific combination of layers across regions. This refined model achieved superior predictive accuracy (<i>R</i>² = 0.43) compared with the standard framework. Our findings underscore the necessity of laminar-specific analysis for establishing accurate cortical susceptibility benchmarks, improving the discrimination of normal aging from neurodegenerative pathology.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 14","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12441809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145075207","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":"The Influence of Sex and Sex-Steroid Hormones on Cerebellar Structure and Functional Connectivity Across Adulthood","authors":"Thamires N. C. Magalhães,&nbsp;Tracey H. Hicks,&nbsp;T. Bryan Jackson,&nbsp;Hannah K. Ballard,&nbsp;Ivan A. Herrejon,&nbsp;Jessica A. Bernard","doi":"10.1002/hbm.70356","DOIUrl":"10.1002/hbm.70356","url":null,"abstract":"<p>Aging involves complex biological changes that affect disease susceptibility and aging trajectories. Although females typically live longer than males, they have a higher susceptibility to diseases like Alzheimer's, speculated to be influenced by menopause, and reduced ovarian hormone production. Understanding sex-specific differences is crucial for personalized medical interventions and sex equality in health. Our study aims to elucidate sex differences in regional cerebellar structure and connectivity during normal aging by investigating both structural and functional connectivity variations in the context of sex-steroid hormones. The study included 138 participants (mean age = 57 (13.3) years, age range = 35–86 years, 54% women). The cohort was divided into three groups: 38 early middle-aged (EMA) individuals (mean age = 41 (4.7) years), 48 late middle-aged (LMA) individuals (mean age = 58 (4) years), and 42 older adults (OAs) (mean age = 72 (6.3) years). All participants underwent MRI scans, and saliva samples were collected for sex-steroid hormone quantification (17β-estradiol (E), progesterone (P), and testosterone (T)). We found less connectivity in females between Lobules I and IV and the cuneus, and greater connectivity in females between Crus I, Crus II, and the precuneus with increased age. Higher 17β-estradiol levels were linked to greater connectivity in Crus I and Crus II cerebellar subregions. Analyzing all participants together, testosterone was associated with both higher and lower connectivity in Lobules I–IV and Crus I, respectively, while higher progesterone levels were linked to lower connectivity in females. Structural differences were observed, with EMA males having larger volumes compared to LMA and OA groups, particularly in the Right I–IV, Right Crus I, Right V, and Right VI. EMA females showed higher volumes in the Right Lobules V and VI. These results highlight the significant role of sex steroid hormones in modulating cerebellar connectivity and structure across adulthood, emphasizing the need to consider sex and hormonal status in neuroimaging studies to better understand age-related cognitive decline and neurological disorders.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 14","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12442050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145075255","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":"Anatomy-Guided, Modality-Agnostic Segmentation of Neuroimaging Abnormalities","authors":"Diala Lteif,&nbsp;Divya Appapogu,&nbsp;Sarah A. Bargal,&nbsp;Bryan A. Plummer,&nbsp;Vijaya B. Kolachalama","doi":"10.1002/hbm.70329","DOIUrl":"10.1002/hbm.70329","url":null,"abstract":"<p>Magnetic resonance imaging (MRI) offers multiple sequences that provide complementary views of brain anatomy and pathology. However, real-world datasets often exhibit variability in sequence availability due to clinical and logistical constraints. This variability complicates radiological interpretation and limits the generalizability of machine learning models that depend on a consistent multimodal input. Here, we propose an anatomy-guided, modality-agnostic framework to assess disease-related abnormalities in brain MRI, leveraging structural context to ensure robustness in diverse input configurations. Central to our approach is Region ModalMix (RMM), an augmentation strategy that integrates anatomical priors during training to improve model performance under missing or variable modality conditions. Using the BraTS 2020 dataset (<i>n</i> = 369), our framework outperformed state-of-the-art methods, achieving a 9.68 mm average reduction in 95th percentile Hausdorff Distance (HD95) and a 1.36 percentage point improvement in Dice Similarity Coefficient (DSC) over baselines with only one available modality. To evaluate out-of-distribution generalization, we tested RMM on the MU-Glioma-Post dataset (<i>n</i> = 593), which includes heterogeneous post-operative glioma cases. Despite distribution shifts, RMM maintained strong performance, reducing HD95 by 18.24 mm and improving DSC by 9.54% points in the most severe missing-modality scenario. Our framework is applicable to multimodal neuroimaging pipelines, enabling more generalizable abnormality detection under heterogeneous data availability.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 14","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12441204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145075222","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":"Connectome-Based Predictive Models of General and Specific Executive Functions","authors":"Shijie Qu,&nbsp;Yueyue Lydia Qu,&nbsp;Kwangsun Yoo,&nbsp;Marvin M. Chun","doi":"10.1002/hbm.70358","DOIUrl":"10.1002/hbm.70358","url":null,"abstract":"<p>Executive functions, the set of cognitive control processes that facilitate adaptive thoughts and actions, are composed primarily of three distinct yet interrelated cognitive components: Inhibition, Shifting, and Updating. While prior brain research has examined the nature of different components as well as their interrelationships, fewer studies examined whole-brain connectivity to predict individual differences for the three cognitive components and associated task scores. Here, using the Connectome-based Predictive Modelling (CPM) approach and open-access data from the Human Connectome Project, we built brain network models to successfully predict individual performance differences on the Flanker task, the Dimensional Change Card Sort task, and the 2-back task, each putatively corresponding to Inhibition, Shifting, and Updating. We focused on grayordinate fMRI data collected during the 2-back tasks after confirming superior predictive performance over resting-state and volumetric data. High cross-task prediction accuracy as well as joint recruitment of canonical networks, such as the frontoparietal and default-mode networks, suggest the existence of a common executive function factor. To investigate the relationships among the three executive function components, we developed new measures to disentangle their shared and unique aspects. Our analysis confirmed that a shared executive function component can be predicted from functional connectivity patterns densely located around the frontoparietal, default-mode, and dorsal attention networks. The Updating-specific component showed significant cross-prediction with the general executive function factor, suggesting a relatively stronger role than the other components. In contrast, the Shifting-specific and Inhibition-specific components exhibited lower cross-prediction accuracy, indicating more distinct and specialized roles. Given the limitation that individual behavioral measures do not purely reflect the intended cognitive constructs, our study demonstrates a novel approach to infer common and specific components of executive function.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 14","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439485/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069410","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}