Human Brain Mapping最新文献

{"title":"Random Walk-Based Node Feature Learning for Major Depressive Disorder Identification Through Multi-Site rs-fMRI Data","authors":"Wanting Xi,&nbsp;Zijian Guo,&nbsp;Ting Mei,&nbsp;Qilin Zhou,&nbsp;Mingsi Xue,&nbsp;Weifeng Yang,&nbsp;Yue Guo,&nbsp;Xuan He","doi":"10.1002/hbm.70326","DOIUrl":"https://doi.org/10.1002/hbm.70326","url":null,"abstract":"<p>Major depressive disorder (MDD) is a globally prevalent psychiatric disorder that significantly impairs quality of life and increases suicide risk. Accurate identification of MDD is critical for clinically assisted diagnosis. Although substantial progress has been made in MDD identification, extracting region of interest (ROI) features from functional brain networks remains underexplored. Furthermore, most studies rely on small-scale resting-state functional magnetic resonance imaging (rs-fMRI) datasets, which limits the generalizability of their findings to large-scale brain networks. To address these issues, we propose a novel graph embedding-based feature selection classification framework (GEF-FSC) to identify MDD through multi-site rs-fMRI data. The framework employs the node2vec algorithm to learn local and global functional connectivity (FC) features of ROIs via flexible random walks, capturing structural information in functional brain networks. Random Forest is then applied for feature selection on the learned embedding features, followed by classification using an ensemble classifier. This approach captures complex, higher-order structural information between ROIs and retains important features, enhancing classification accuracy by minimizing redundancy in high-dimensional FC features. Evaluated on the REST-meta-MDD dataset, our framework achieved 81.65% accuracy under the Dosenbach template and 75.30% under the AAL atlas. Comparative experiments with eight benchmark methods and six state-of-the-art classifiers demonstrated superior accuracy, sensitivity, specificity, and F1-score. Interpretability analysis highlighted key brain regions and networks consistent with previous findings. The GEF-FSC framework effectively classifies MDD and identifies key brain regions and networks associated with the disorder, emphasizing the importance of higher-order structural information in improving diagnostic accuracy.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 12","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881118","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":"Aberrant Modular Dynamics of Functional Networks in Schizophrenia and Their Relationship With Neurotransmitter and Gene Expression Profiles","authors":"Yi Zhen,&nbsp;Yaqian Yang,&nbsp;Yi Zheng,&nbsp;Zhiming Zheng,&nbsp;Hongwei Zheng,&nbsp;Shaoting Tang","doi":"10.1002/hbm.70304","DOIUrl":"https://doi.org/10.1002/hbm.70304","url":null,"abstract":"<p>Numerous studies have emphasized the time-varying modular architecture of functional brain networks and its relevance to cognitive functions in healthy participants. However, how modular dynamics of resting-state functional networks change in schizophrenia and how these alterations relate to neurotransmitter and transcriptomic signatures have not been well elucidated. We harmonized resting-state fMRI data from a multi-site sample including 223 patients and 279 healthy controls and applied the multilayer network method to estimate the regional module switching rate (flexibility) of functional brain connectomes. We examined aberrant flexibility in patients relative to controls and explored its relations to neurotransmitter systems and postmortem gene expression. Compared with controls, patients with schizophrenia had significantly higher flexibility in the somatomotor and right visual regions, and lower flexibility in the left parahippocampal gyrus, right supramarginal gyrus, right frontal-operculum-insula, bilateral precuneus, posterior cingulate cortex, and bilateral inferior parietal gyrus. These alterations were associated with multiple neurotransmitter systems and weighted gene transcriptomic profiles. The most relevant genes were preferentially enriched for biological processes of transmembrane transport and brain development, specific cell types, and previously identified schizophrenia-related genes. This study reveals aberrant modular dynamics in schizophrenia and its relations to neurotransmitter systems and schizophrenia-related transcriptomic profiles, providing insights into the understanding of the pathophysiology underlying schizophrenia.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 12","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881115","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":"A Contrast-Agnostic Method for Ultra-High Resolution Claustrum Segmentation","authors":"Chiara Mauri,&nbsp;Ryan Fritz,&nbsp;Jocelyn Mora,&nbsp;Benjamin Billot,&nbsp;Juan Eugenio Iglesias,&nbsp;Koen Van Leemput,&nbsp;Jean Augustinack,&nbsp;Douglas N. Greve","doi":"10.1002/hbm.70303","DOIUrl":"https://doi.org/10.1002/hbm.70303","url":null,"abstract":"<p>The claustrum is a band-like gray matter structure located between putamen and insula whose exact functions are still actively researched. Its sheet-like structure makes it barely visible in in vivo magnetic resonance imaging (MRI) scans at typical resolutions, and neuroimaging tools for its study, including methods for automatic segmentation, are currently very limited. In this paper, we propose a contrast- and resolution-agnostic method for claustrum segmentation at ultra-high resolution (0.35 mm isotropic); the method is based on the SynthSeg segmentation framework, which leverages the use of synthetic training intensity images to achieve excellent generalization. In particular, SynthSeg requires only label maps to be trained, since corresponding intensity images are synthesized on the fly with random contrast and resolution. We trained a deep learning network for automatic claustrum segmentation, using claustrum manual labels obtained from 18 ultra-high resolution MRI scans (mostly ex vivo). We demonstrated the method to work on these 18 high resolution cases (Dice score = 0.632, mean surface distance = 0.458 mm, and volumetric similarity = 0.867 using 6-fold cross validation (CV)), and also on in vivo T1-weighted MRI scans at typical resolutions (≈1 mm isotropic). We also demonstrated that the method is robust in a test–retest setting and when applied to multimodal imaging (T2-weighted, proton density, and quantitative T1 scans). To the best of our knowledge this is the first accurate method for automatic ultra-high resolution claustrum segmentation, which is robust against changes in contrast and resolution. The method is released at https://github.com/chiara-mauri/claustrum_segmentation and as part of the neuroimaging package FreeSurfer.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 12","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881119","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":"Predicting Real-Life Cognitive Scores From Functional Connectivity","authors":"Maya Kadushin,&nbsp;Asaf Madar,&nbsp;Niv Tik,&nbsp;Michal Bernstein-Eliav,&nbsp;Ido Tavor","doi":"10.1002/hbm.70319","DOIUrl":"https://doi.org/10.1002/hbm.70319","url":null,"abstract":"<p>Over the past decade, functional connectivity patterns, derived from functional magnetic resonance imaging (fMRI), have been widely used to predict various cognitive traits. However, most studies have focused on measures assessed under controlled laboratory conditions, which may not fully reflect the complexity of the traits in real-world environments. In this study, we investigated connectome-based predictions of cognitive performance in ecologically valid, real-world settings. Participants (<i>n</i> = 194) performed the Psychometric Entrance Test, a standardized exam used for admission to higher education institutions in Israel and a strong predictor of undergraduate academic success. Using functional connectivity patterns, we significantly predicted overall test performance, as well as its three cognitive-specific domains: quantitative reasoning, verbal reasoning, and proficiency in a foreign language. Significant predictions were consistent across four different prediction approaches, demonstrating the robustness of the relations between functional connectivity and cognition. Additionally, we examined which connectivity features mostly contributed to predictions, analyzing both edge- and node-level contributions. We found that different cognitive abilities (i.e., quantitative skills vs. language-related skills) were primarily predicted by unique connectivity patterns. Yet, predictive features were more similar for scores that were more strongly correlated at the behavioral level. Last, we implemented a transfer learning approach in which the predicted cognitive-specific scores were used as features for prediction of the global score, resulting in an improved prediction compared to that derived directly from functional connectivity. Overall, our results demonstrate that the functional connectome captures real-world variability in both global and domain-specific cognitive abilities, emphasizing its potential to serve as an objective marker of real-world cognitive performance.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 12","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70319","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861940","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":"Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation","authors":"Yee-Fan Tan,&nbsp;Fuad Noman,&nbsp;Raphaël C.-W. Phan,&nbsp;Hernando Ombao,&nbsp;Chee-Ming Ting","doi":"10.1002/hbm.70322","DOIUrl":"https://doi.org/10.1002/hbm.70322","url":null,"abstract":"<p>Common measures of brain functional connectivity (FC) including covariance and correlation matrices are symmetry-positive definite (SPD) matrices residing on a cone-shaped Riemannian manifold. Despite its remarkable success for Euclidean-valued data generation, the use of standard generative adversarial networks (GANs) to generate manifold-valued FC data neglects its inherent SPD structure and hence the inter-relatedness of edges in real FC. We propose a novel graph-regularized manifold-aware conditional Wasserstein GAN (GR-SPD-GAN) for FC data generation on the SPD manifold that can preserve the global FC structure. Specifically, we optimize a generalized Wasserstein distance between the real and generated SPD data under adversarial training, conditioned on the class labels. The resulting generator can synthesize new SPD-valued FC matrices associated with different classes of brain networks, for example, brain disorder or healthy control. Furthermore, we introduce additional population graph-based regularization terms on both the SPD manifold and its tangent space to encourage the generator to respect the inter-subject similarity of FC patterns in the real data. This also helps in avoiding mode collapse and produces more stable GAN training. Evaluated on resting-state functional magnetic resonance imaging (fMRI) data of major depressive disorder (MDD), qualitative and quantitative results show that the proposed GR-SPD-GAN clearly outperforms several state-of-the-art GANs in generating more realistic fMRI-based FC samples. When applied to FC data augmentation for MDD identification, classification models trained on augmented data generated by our approach achieved the largest margin of improvement in classification accuracy among the competing GANs over baselines without data augmentation.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 12","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861739","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":"Mind the Gap: Does Brain Age Improve Alzheimer’s Disease Prediction?","authors":"Trevor Wei Kiat Tan,&nbsp;Kim-Ngan Nguyen,&nbsp;Chen Zhang,&nbsp;Ru Kong,&nbsp;Susan F. Cheng,&nbsp;Fang Ji,&nbsp;Joanna Su Xian Chong,&nbsp;Eddie Jun Yi Chong,&nbsp;Narayanaswamy Venketasubramanian,&nbsp;Csaba Orban,&nbsp;Michael W. L. Chee,&nbsp;Christopher Chen,&nbsp;Juan Helen Zhou,&nbsp;B. T. Thomas Yeo,&nbsp;The Alzheimer's Disease Neuroimaging Initiative,&nbsp;The Australian Imaging Biomarkers and Lifestyle Study of Aging","doi":"10.1002/hbm.70276","DOIUrl":"https://doi.org/10.1002/hbm.70276","url":null,"abstract":"<p>Brain age is widely regarded as a powerful marker of <i>general</i> brain health. Brain age models are typically trained on large datasets to predict chronological age, which may offer advantages in predicting <i>specific</i> health outcomes, much like the success of finetuning large language models for specific applications. However, it is also well accepted that machine learning models trained to directly predict specific outcomes (i.e., direct models) often outperform those trained on surrogate objectives. Therefore, despite their much larger training data, it is unclear whether brain age models outperform direct models in predicting specific brain health outcomes. Here, we compare large-scale brain age models (pretrained on 53,542 participants) and direct models for predicting specific health outcomes related to Alzheimer’s disease (AD) dementia. Using anatomical T1 scans from three continents (<i>N</i> = 1,848), we find that summarizing brain age with a single scalar (i.e., brain age gap) led to poor prediction performance. Using higher-dimensional intermediate representations of brain age models led to better prediction, but was still worse than direct models without finetuning. Using intermediate representations of finetuned brain age models was necessary to achieve similar performance to direct models. Overall, our results do not discount brain age as a useful marker of general brain health but suggest that using chronological age as a pretraining target might be suboptimal for predicting specific health outcomes.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 12","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70276","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861939","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":"My Baby Versus the World: Fathers' Neural Processing of Own-Infant, Unfamiliar-Infant, and Romantic Partner Stimuli","authors":"Philip Newsome,&nbsp;Anthony G. Vaccaro,&nbsp;Sofia I. Cárdenas,&nbsp;Narcis A. Valen,&nbsp;Yael H. Waizman,&nbsp;Elizabeth C. Aviv,&nbsp;Gabriel A. León,&nbsp;Jonas T. Kaplan,&nbsp;Darby E. Saxbe","doi":"10.1002/hbm.70324","DOIUrl":"https://doi.org/10.1002/hbm.70324","url":null,"abstract":"<p>Parents activate brain regions linked with social cognition, reward processing, and emotion when viewing their own infant. Neural responses to own-infant stimuli may be driven by familiarity, self-relevance, or by the unique features of infant faces. The current study sought to clarify these distinctions in first-time fathers by contrasting video stimuli of their infant, an unfamiliar infant, and their pregnant partner. In addition, we examined associations with fathers' self-reported bonding and parenting stress. Fathers (<i>n</i> = 32) scanned approximately 8 months after the birth of their first child completed an fMRI scan while watching videos of their infant, an unfamiliar infant, their pregnant partner, and an unfamiliar pregnant woman. We compared neural responses to the participant's own infant to these other stimuli using both traditional univariate methods and multivariate searchlight analyses. Lastly, we ran additional multivariate pattern analyses to determine discriminability between infant and adult stimuli, as well as familiar and unfamiliar stimuli. Consistent with previous studies, fathers showed greater activation to their own infant versus an unfamiliar infant in regions including the precuneus, posterior cingulate, orbitofrontal cortex, and inferior frontal gyrus. In addition, fathers exhibited heightened activation to their own infant versus their partner in the precuneus. Fathers who reported stronger antenatal and postpartum bonding and lower parenting stress 3 months after birth subsequently showed stronger activation in the precuneus and posterior cingulate to their own infant. Multivariate pattern analyses revealed that in addition to these regions, the parahippocampus differentiated own-infant stimuli versus other conditions. Neural patterns distinguished infant/adult and familiar/unfamiliar stimuli in mentalizing, visual, and affective areas. These findings replicate and extend previous research on the parental brain and suggest that cortical midline mentalizing network regions, as well as visual and reward areas, are particularly important in first-time fathers' processing of their own infants.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814796","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":"Criminal Behavior in Frontotemporal Dementia: A Multimodal MRI Study","authors":"Karsten Mueller,&nbsp;Nico Scherf,&nbsp;Timo Grimmer,&nbsp;Janine Diehl-Schmid,&nbsp;Adrian Danek,&nbsp;Johannes Levin,&nbsp;Jens Wiltfang,&nbsp;Sarah Anderl-Straub,&nbsp;Markus Otto,&nbsp;Matthias L. Schroeter,&nbsp;FTLD Consortium Germany","doi":"10.1002/hbm.70308","DOIUrl":"https://doi.org/10.1002/hbm.70308","url":null,"abstract":"<p>The behavioral variant of frontotemporal dementia (bvFTD) is related to a variety of social misbehaviors, including criminal behavior (CB) due to deep changes in cognition, behavior, and personality. Recent work suggests that impairment in emotional processing, along with disinhibition, constitutes the necessary elements for CB in bvFTD. However, the underlying neurobiological mechanisms are still unclear. Therefore, we aim at investigating structural and functional brain changes related to CB in bvFTD using magnetic resonance imaging (MRI) with the German Consortium for Frontotemporal Lobar Degeneration (FTLD). Our study comprised 87 patients with bvFTD and 26 healthy controls recruited within different locations of the FTLD Consortium. A subset of 21 patients with bvFTD showed CB, including theft, physical violence, sexual assault, drug abuse, and violations against traffic law. Voxel-based morphometry was performed, generating gray matter density (GMD) images obtained from high-resolution T1-weighted MR images. In addition, surface-based morphometry was performed by reconstruction of cortical thickness using a projection-based thickness approach. Both GMD and cortical thickness were further analyzed in order to detect group differences between bvFTD with and without CB. Resting-state functional MRI was available for a subgroup of 56 patients with bvFTD, including 16 patients showing CB. On a behavioral level, CB in bvFTD was associated with a higher frequency of disinhibition, lower frequency of apathy, and better performance in verbal fluency. Comparing bvFTD with and without CB, we obtained reduced GMD and reduced cortical thickness in the temporal lobe, predominantly in the left hemisphere. Impairment in brain structure was correlated with the Frontal Systems Behavior Scale, particularly with disinhibition, in the left superior temporal gyrus in interaction with CB in bvFTD. Investigating functional MRI data, CB was associated with significant functional brain dysconnectivity, particularly between the left anterior superior temporal gyrus and widely distributed cortical regions, including areas in the vicinity of the precentral sulcus and the inferior frontal junction, related to executive functions. Our study revealed structural and functional brain differences between bvFTD with and without CB, showing CB-related reduced GMD and cortical thickness in the left temporal lobe, indicating disinhibition as the main driver for CB. Interestingly, brain degeneration in the temporal lobe is discussed with CB in bvFTD in the current literature, dominantly affecting the right hemisphere. Our study investigates specifically the neural correlates of CB in bvFTD with MRI, modifying this view. Further work is necessary to shed more light on the role of the temporal lobe in bvFTD with CB.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70308","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811055","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":"Relative Strength Variability Measures for Brain Structural Connectomes and Their Relationship With Cognitive Functioning","authors":"Hon Wah Yeung,&nbsp;Colin R. Buchanan,&nbsp;Joanna Moodie,&nbsp;Ian J. Deary,&nbsp;Elliot M. Tucker-Drob,&nbsp;Mark E. Bastin,&nbsp;Heather C. Whalley,&nbsp;Keith M. Smith,&nbsp;Simon R. Cox","doi":"10.1002/hbm.70314","DOIUrl":"https://doi.org/10.1002/hbm.70314","url":null,"abstract":"<p>In this work, we propose a new class of graph measures for weighted connectivity information in the human brain based on node relative strengths: relative strength variability (RSV), measuring susceptibility to targeted attacks, and hierarchical RSV (hRSV), a first weighted statistical complexity measure for networks. Using six different network weights for structural connectomes from the UK Biobank, we conduct comprehensive analyses to explore relationships between the RSV and hRSV, and (i) other known network measures, (ii) general cognitive function (‘<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>g</mi>\u0000 </mrow>\u0000 <annotation>$$ g $$</annotation>\u0000 </semantics></math>’). Both measures exhibit low correlations with other graph measures across all connectivity weightings indicating that they capture new information of the brain connectome. We found higher <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>g</mi>\u0000 </mrow>\u0000 <annotation>$$ g $$</annotation>\u0000 </semantics></math> was associated with lower RSV and lower hRSV. That is, higher <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>g</mi>\u0000 </mrow>\u0000 <annotation>$$ g $$</annotation>\u0000 </semantics></math> was associated with higher resistance to targeted attack and lower statistical complexity. Moreover, the proposed measures had consistently stronger associations with <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>g</mi>\u0000 </mrow>\u0000 <annotation>$$ g $$</annotation>\u0000 </semantics></math> than other widely used graph measures including clustering coefficient and global efficiency and were incrementally significant for predicting <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>g</mi>\u0000 </mrow>\u0000 <annotation>$$ g $$</annotation>\u0000 </semantics></math> above other measures for five of the six network weights. Overall, we present a new class of weighted network measures based on variations of relative node strengths which significantly improved prediction of general cognition from traditional weighted structural connectomes.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70314","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144782729","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 Genetics of Cerebellar Structure and Associations With Cognitive Performance: A Twin Magnetic Resonance Imaging Study","authors":"Gretchen Lutz,&nbsp;Simon Smerconish,&nbsp;David Roalf,&nbsp;Michael C. Neale,&nbsp;J. Eric Schmitt","doi":"10.1002/hbm.70300","DOIUrl":"https://doi.org/10.1002/hbm.70300","url":null,"abstract":"<p>The cerebellum, traditionally associated with motor control, is increasingly recognized for its involvement in higher-order cognitive functions. However, the role of cerebellar subregions in cognition remains underexplored, as are the roles of genetic factors on cerebellar structure and brain-behavioral associations. The primary goal of this study was to investigate the relationship between cerebellar subregion volumes and cognitive performance. A secondary aim was to quantify the genetic contributions to cerebellar structure and determine the degree to which any brain-behavior associations were genetically mediated. 3T anatomic MRI data from <i>N</i> = 932 typically developing individuals from the Human Connectome Project were used for this study. Twenty-seven cerebellar regions of interest (ROIs) were automatically parcellated using <i>CerebNet</i>. Three additional lobar-level ROIs were derived from smaller measures. Nine functional domains (six cognitive and three motor) related to known or suspected cerebellar function were selected. Linear regression analyses were conducted to identify correlations between cerebellar volumes and cognitive outcomes, adjusting for age, sex, and overall brain volume. Univariate and bivariate quantitative genetic modeling was then performed in OpenMx. There were numerous statistically significant phenotypic associations between cognitive measures and cerebellar lobar and lobular volumes, particularly in the IPL, AL, bilateral cortices, left lobule V, right lobule VI, vermis, and vermis lobule VIII, each meeting the threshold of <i>p</i> &lt; 0.02 across at least four out of nine cognitive domains. The vermis and vermis lobule VIII were of particular note, showing even stronger associations (<i>p</i> &lt; 0.0009 across three domains). Cognitive measures were modestly heritable, and cerebellar ROIs were highly heritable. Quantitative genetic models suggested that brain–behavior associations are largely driven by shared environmental factors. Our findings identify novel associations between specific cerebellar subregions and cognitive performance, highlighting the vermis as a critical structure. We also provide a detailed map of the quantitative genetics of human cerebellar structure. Future studies are warranted.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70300","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144782640","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}