Human Brain Mapping最新文献

{"title":"Mapping Contributions of the Anterior Temporal Semantic Hub to the Processing of Abstract and Concrete Verbs","authors":"Emiko J. Muraki,&nbsp;Penny M. Pexman,&nbsp;Richard J. Binney","doi":"10.1002/hbm.70210","DOIUrl":"https://doi.org/10.1002/hbm.70210","url":null,"abstract":"<p>Multiple representation theories of semantic processing propose that word meaning is supported by simulated sensorimotor experience in modality-specific neural regions, as well as in cognitive systems that involve processing of linguistic, emotional, and introspective information. According to the hub-and-spoke model of semantic memory, activity from these distributed cortical areas feeds into a primary semantic hub located in the ventral anterior temporal lobe (vATL). In the present pre-registered study, we examined whether different types of abstract verbs (mental, emotional and nonembodied) and concrete (embodied) verbs all engage the vATL, and also whether they differentially recruit a broader set of distributed neurocognitive systems (consistent with multiple representation theories). Finally, we investigated whether there is information about different verb types distributed across the broader ATL region, consistent with a Graded Semantic Hub Hypothesis. We collected data from 30 participants who completed a syntactic classification task (is it a verb? Yes or no) and a numerical judgment task which served as an active but less semantic baseline task. Whole brain univariate analyses revealed consistent BOLD signal throughout the canonical semantic network, including the left inferior frontal gyrus, left middle temporal gyrus, and the vATL. All types of abstract verbs engaged the vATL except for mental state verbs. Finally, a multivariate pattern analysis revealed clusters within the ATL that were differentially engaged when processing each type of abstract verb. Our findings extend previous research and suggest that the hub-and-spoke hypothesis and the graded semantic hub hypothesis provide a neurobiologically constrained model of semantics that can account for abstract verb representation and processing.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 6","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871471","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":"MRI Distance Measures as a Predictor of Subsequent Clinical Status During the Preclinical Phase of Alzheimer's Disease and Related Disorders","authors":"Xinyi Zhang,&nbsp;Brian S. Caffo,&nbsp;Anja Soldan,&nbsp;Corinne Pettigrew,&nbsp;Erus Guray,&nbsp;Christos Davatzikos,&nbsp;John C. Morris,&nbsp;Tammie L. S. Benzinger,&nbsp;Sterling C. Johnson,&nbsp;Colin L. Masters,&nbsp;Jurgen Fripp,&nbsp;Susan M. Resnick,&nbsp;Murat Bilgel,&nbsp;Walter A. Kukull,&nbsp;Marilyn S. Albert,&nbsp;Zheyu Wang","doi":"10.1002/hbm.70205","DOIUrl":"https://doi.org/10.1002/hbm.70205","url":null,"abstract":"<p>Brain atrophy over time, as measured by magnetic resonance imaging (MRI), has been shown to predict subsequent cognitive impairment among individuals who were cognitively normal when first evaluated, indicating that subtle brain atrophy associated with Alzheimer's disease (AD) may begin years before clinical symptoms appear. Traditionally, atrophy has been quantified by differences in brain volume or thickness over a specified timeframe. Research indicates that the rate of atrophy varies across different brain regions, which themselves exhibit complex spatial and hierarchical organizations. These characteristics collectively emphasize the need for diverse summary measures that can effectively capture the multidimensional nature of degeneration. In this study, we explore the use of distance measurements to quantify brain volumetric changes using processed MRI data from the Preclinical Alzheimer's Disease Consortium (PAC). We conducted a series of analyses to predict future diagnostic status by modeling MRI trajectories for participants who were cognitively normal at baseline and either remained cognitively normal or progressed to mild cognitive impairment (MCI) over time, with adjustments for age, sex, education, and APOE genotype. We consider multiple distance measures and brain regions through a two-step approach. First, we build base models by fitting individual mixed-effect models for each distance metric and brain region pairing, using follow-up diagnosis (normal vs. MCI) as the outcome and volumetric changes from the baseline, as summarized by a given distance measure, as predictors. The second step aggregates these individual region-distance base models to derive an overall estimate of diagnostic status. Our analyses showed that the distance measures approach consistently outperformed the traditional direct volumetric approach in terms of predictive accuracy, both in individual base models and the aggregated models. This work highlights the potential advantage of using distance measures over the traditional direct volumetric approach to capture the multidimensional aspects of atrophy in the development of AD and related disorders.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 6","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865917","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":"Sensitivity of Quantitative Susceptibility Mapping for Clinical Research in Deep Gray Matter","authors":"Fahad Salman,&nbsp;Abhisri Ramesh,&nbsp;Thomas Jochmann,&nbsp;Mirjam Prayer,&nbsp;Ademola Adegbemigun,&nbsp;Jack A. Reeves,&nbsp;Gregory E. Wilding,&nbsp;Junghun Cho,&nbsp;Dejan Jakimovski,&nbsp;Niels Bergsland,&nbsp;Michael G. Dwyer,&nbsp;Robert Zivadinov,&nbsp;Ferdinand Schweser","doi":"10.1002/hbm.70187","DOIUrl":"https://doi.org/10.1002/hbm.70187","url":null,"abstract":"<p>Quantitative susceptibility mapping (QSM) is an advanced MRI technique for assessing iron, calcium, and myelin tissue levels based on magnetic susceptibility. QSM consists of multiple processing steps, with various choices for each step. While QSM is increasingly applied in neurodegenerative disease research, its reproducibility and sensitivity in detecting susceptibility changes across groups or over time, which underpin the interpretation of clinical outcomes, have not been thoroughly quantified. This study aimed to evaluate how choices in background field removal (BFR), dipole inversion algorithms, and anatomical referencing impact the detection of changes in deep gray matter susceptibility. We used aging-related changes in brain iron, established in earlier foundational studies, as a surrogate model to test the sensitivity and reproducibility of 378 different QSM pipelines toward the detection of longitudinal susceptibility changes in a clinical setting. We used 10-year follow-up data and scan-rescan data of healthy adults scanned at 3T. Our results demonstrated high variability in the sensitivity of QSM pipelines toward detecting susceptibility changes. While most pipelines detected the same over-time changes, the choice of the BFR algorithm and the referencing strategy influenced reproducibility error and sensitivity substantially. Notably, pipelines using RESHARP with AMP-PE, HEIDI, or LSQR inversion showed the highest overall sensitivity. The findings suggest a strong impact of algorithmic choices in QSM processing on the ability to detect physiological changes in the brain. Careful consideration should be given to the pipeline configuration for reliable clinical outcomes.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 6","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70187","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856937","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":"WMH-DualTasker: A Weakly Supervised Deep Learning Model for Automated White Matter Hyperintensities Segmentation and Visual Rating Prediction","authors":"Yilei Wu,&nbsp;Zijian Dong,&nbsp;Hongwei Bran Li,&nbsp;Yao Feng Chong,&nbsp;Fang Ji,&nbsp;Joanna Su Xian Chong,&nbsp;Nathanael Ren Jie Tang,&nbsp;Saima Hilal,&nbsp;Huazhu Fu,&nbsp;Christopher Li-Hsian Chen,&nbsp;Juan Helen Zhou,&nbsp;Alzheimer's Disease Neuroimaging Initiative","doi":"10.1002/hbm.70212","DOIUrl":"https://doi.org/10.1002/hbm.70212","url":null,"abstract":"<p>White matter hyperintensities (WMH) are neuroimaging markers linked to an elevated risk of cognitive decline. WMH severity is typically assessed via visual rating scales and through volumetric segmentation. While visual rating scales are commonly used in clinical practice, they offer limited descriptive power. In contrast, supervised volumetric segmentation requires manually annotated masks, which are labor-intensive and challenging to scale for large studies. Therefore, our goal was to develop an automated deep-learning model that can provide accurate and holistic quantification of WMH severity with minimal supervision. We developed WMH-DualTasker, a deep learning model that simultaneously performs voxel-wise segmentation and visual rating score prediction. The model employs self-supervised learning with transformation-invariant consistency constraints, using WMH visual ratings (ARWMC scale, range 0–30) from clinical settings as the sole supervisory signal. Additionally, we assessed its clinical utility by applying it to identify individuals with mild cognitive impairment (MCI) and to predict dementia conversion. The volumetric quantification performance of WMH-DualTasker was either superior to or on par with existing supervised methods, as demonstrated on the MICCAI-WMH dataset (<i>N</i> = 60, Dice = 0.602) and the SINGER dataset (<i>N</i> = 64, Dice = 0.608). Furthermore, the model exhibited strong agreement with clinical visual rating scales on an external dataset (SINGER, MAE = 1.880, <i>K</i> = 0.77). Importantly, WMH severity metrics derived from WMH-DualTasker improved predictive performance beyond conventional clinical features for MCI classification (AUC = 0.718, <i>p</i> &lt; 0.001) and MCI conversion prediction (AUC = 0.652, <i>p</i> &lt; 0.001) using the ADNI dataset. WMH-DualTasker substantially reduces the reliance on labor-intensive manual annotations, facilitating more efficient and scalable quantification of WMH severity in large-scale population studies. This innovative approach has the potential to advance preventive and precision medicine by enhancing the assessment and management of vascular cognitive impairment associated with WMH.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 6","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856938","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":"Simultaneous EEG-fMRI Reveals a Visual Working Memory Encoding Network Related to Theta Oscillatory Activity in Healthy Subjects","authors":"Gregor Leicht,&nbsp;Jonas Rauh,&nbsp;Marius Mußmann,&nbsp;Sebastian Vauth,&nbsp;Saskia Steinmann,&nbsp;Moritz Haaf,&nbsp;Corinna Haenschel,&nbsp;Christoph Mulert","doi":"10.1002/hbm.70216","DOIUrl":"https://doi.org/10.1002/hbm.70216","url":null,"abstract":"<p>Working memory (WM) is crucially involved in many aspects of higher cognitive functions and goal-directed behavior. The encoding of sensory information necessitates the conversion of sensory stimuli into maintainable constructs. Oscillatory activity in the theta frequency range (4–8 Hz) of the human electroencephalogram (EEG) has been related to this. However, so far, no study has investigated the neurophysiological mechanisms and the brain network structure underlying the WM encoding process simultaneously. Thus, this study aimed to test whether theta oscillatory activity would be specifically related to the activity within a WM encoding brain network in healthy subjects by means of simultaneous recordings of EEG and functional magnetic resonance imaging (fMRI). Simultaneous recordings of EEG and fMRI were conducted in 32 healthy subjects during the performance of a visual working memory delayed matched to sample task. The fMRI analysis was informed by single-trial theta oscillatory responses to encoding stimuli. This analysis revealed a working memory encoding network mediated by theta oscillatory activity. The network included regions within the dorsolateral prefrontal cortex and parietal areas. Our results give reason to assume that the formation of a working memory network might take place during the encoding of information utilizing theta synchrony as a binding mechanism.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 6","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143853020","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":"Volumetric Changes in Cerebellar Transverse Zones: Age and Sex Effects in Health and Neurological Disorders","authors":"Farshid Ghiyamihoor,&nbsp;Payam Paymani,&nbsp;Jarrad Perron,&nbsp;Azam Asemi-Rad,&nbsp;Mehdi Marzban,&nbsp;Aashka Mohite,&nbsp;Karen Ardila,&nbsp;Bara Aljada,&nbsp;Asghar Marzban,&nbsp;Mehnosh Toback,&nbsp;Sherif Eltonsy,&nbsp;Ji Hyun Ko,&nbsp;Tabrez J. Siddiqui,&nbsp;Christopher J. Steele,&nbsp;Jiming Kong,&nbsp;Mario Manto,&nbsp;M. Ethan MacDonald,&nbsp;Jason S. Gill,&nbsp;Roy V. Sillitoe,&nbsp;Fuat Balcı,&nbsp;Iman Beheshti,&nbsp;Hassan Marzban","doi":"10.1002/hbm.70214","DOIUrl":"https://doi.org/10.1002/hbm.70214","url":null,"abstract":"<p>Cerebellar volumetric changes are intricately linked to aging, with distinct patterns across its <i>transverse zones</i>, the functional subdivisions characterized by unique cytoarchitectural and connectivity profiles. Despite research efforts, the cerebellar aging process in health and neurological disorders remains poorly understood. In this study, we investigated the effects of age and sex on total cerebellum, <i>transverse zone</i>, and lobule volumes using MRI data from over 45,000 participants compiled from six neuroimaging datasets. We also propose a framework for estimating cerebellum age as an indicator of cerebellar health. Significant age-dependent volume reductions were observed across <i>transverse zones</i>, with the central zone (<i>CZ</i>; lobules VI and VII) exhibiting the steepest decline in both health and neurological disorders. This finding highlights the <i>CZ's</i> vulnerability to aging and its critical role in cognitive and emotional processing. We also found prominent sex differences in age-dependent volumetric changes. Males exhibited smaller total intracranial volume (TIV)-adjusted cerebellum volume and faster age-dependent volume reduction than females in both health and mild cognitive impairment (MCI), Alzheimer disease (AD), and Parkinson disease (PD). In contrast, females with schizophrenia (SZ) and cocaine use disorder (CUD) revealed faster age-dependent cerebellar volume reduction than males. Patients with MCI, AD, and PD experienced more pronounced atrophy in the posterior (<i>PZ</i>) and nodular (<i>NZ</i>) zones compared to age-matched healthy controls, while SZ patients were characterized by a more prominent reduction in <i>CZ</i>. In CUD, a non-significant volume decline was observed in all zones compared to the controls. Moreover, our framework for estimating cerebellum age revealed a notable difference in cerebellar aging between healthy individuals and neurological patients. Finally, we charted age-dependent changes in cerebellar volume in healthy individuals, focusing on <i>transverse zones</i> capturing the functional subdivisions. These findings underscore the potential of cerebellar volumetric analysis as a biomarker for early detection and monitoring of neurodegenerative and neuropsychiatric disorders. Our novel approach complements and enhances MRI-based analyses, providing essential insights into the pathogenesis of aging, neurodegeneration, and chronic neuropsychiatric conditions.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 6","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70214","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840542","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":"Cross-Sectional Brain Age Assessments Are Limited in Predicting Future Brain Change","authors":"Max Korbmacher,&nbsp;Didac Vidal-Pineiro,&nbsp;Meng-Yun Wang,&nbsp;Dennis van der Meer,&nbsp;Thomas Wolfers,&nbsp;Hajer Nakua,&nbsp;Eli Eikefjord,&nbsp;Ole A. Andreassen,&nbsp;Lars T. Westlye,&nbsp;Ivan I. Maximov","doi":"10.1002/hbm.70203","DOIUrl":"https://doi.org/10.1002/hbm.70203","url":null,"abstract":"<p>The concept of brain age (BA) describes an integrative imaging marker of brain health, often suggested to reflect aging processes. However, the degree to which cross-sectional MRI features, including BA, reflect past, ongoing, and future brain changes across different tissue types from macro- to microstructure remains controversial. Here, we use multimodal imaging data of 39,325 UK Biobank participants, aged 44–82 years at baseline and 2,520 follow-ups within 1.12–6.90 years to examine BA changes and their relationship to anatomical brain changes. We find insufficient evidence to conclude that BA reflects the rate of brain aging. However, modality-specific differences in brain ages reflect the state of the brain, highlighting diffusion and multimodal MRI brain age as potentially useful cross-sectional markers.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 6","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836469","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":"Dynamic Functional Network Connectivity Pattern of the Amygdalohippocampal Complex in Individuals With Subjective Cognitive Decline","authors":"Chih-Kai Lee,&nbsp;Xiao-Ya Wei,&nbsp;Ze-Yi Wang,&nbsp;Hang Zhou,&nbsp;Chao-Qun Yan,&nbsp;Xin-Yuan Jiang,&nbsp;Guang-Xia Shi,&nbsp;Xu Wang,&nbsp;Cun-Zhi Liu","doi":"10.1002/hbm.70194","DOIUrl":"https://doi.org/10.1002/hbm.70194","url":null,"abstract":"<p>Subjective cognitive decline (SCD) is a potential early marker of cognitive decline and dementia. The amygdalohippocampal structure and function are closely related to cognitive decline, but few studies have investigated large-scale amygdalohippocampal brain functional network connectivity in individuals with SCD. Here, we aim to explore how the dynamic functional network connectivity (dFNC) between the amygdalohippocampal complex and other brain networks contributes to the understanding of early cognitive decline. Independent component analysis (ICA) and dFNC analysis were applied to functional magnetic resonance imaging (fMRI) data from 66 individuals with SCD to extract the amygdalohippocampal complex and identify distinct connectivity states. Cognitive performance was assessed through a composite Z score derived from a battery of neuropsychological tests. Correlation analyses were performed to examine the associations between the dFNC patterns and cognitive performance. Three distinct dFNC states were identified, each characterized by varying levels of within- and inter-network connectivity, with occurrences of 65%, 18%, and 17% respectively. Cognitive function, measured using a composite Z score, was positively correlated with amygdalohippocampal-sensorimotor network (SM) and amygdalohippocampal-visual network (VI) dFNC in State 2. Significant correlations were observed between the amygdalohippocampal complex and the left precentral gyrus (<i>r</i> = 0.517, FDR-corrected <i>p</i> = 0.005), postcentral gyrus (<i>r</i> = 0.487, FDR-corrected <i>p</i> = 0.034), and multiple visual network regions, including the lingual gyrus and lateral occipital cortex (all <i>P</i>s &lt; 0.05, FDR-corrected). These associations remained significant after adjusting for sex and age. These findings extend the current understanding of amygdalohippocampal dysfunction in cognitive decline and demonstrate that cognitive function is associated with distinct large-scale amygdalohippocampal network dynamics.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70194","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831192","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":"Investigating the Association of Concussion and Contact Sport Exposure History With Brain Microstructure Using Quantitative Susceptibility Mapping","authors":"Lezlie Y. España,&nbsp;Benjamin L. Brett,&nbsp;Andrew R. Mayer,&nbsp;Andrew S. Nencka,&nbsp;Brad Swearingen,&nbsp;Kevin M. Koch,&nbsp;Timothy B. Meier","doi":"10.1002/hbm.70213","DOIUrl":"https://doi.org/10.1002/hbm.70213","url":null,"abstract":"<p>A growing body of evidence suggests that repeated concussions and exposure to repetitive head impacts may be associated with subtle abnormalities in neurological health. Prior studies have demonstrated associations of prior concussion and repetitive head impacts with the brain's microstructure, typically using diffusion magnetic resonance imaging, though the direction of these relationships has varied across groups. Quantitative susceptibility mapping is a quantitative extension of susceptibility weighted imaging that is sensitive to pathophysiology associated with neurotrauma and thus represents an alternative method to characterize the effects of concussion and repetitive head impact exposure on brain microstructure. The goal of this work was to characterize the association of prior concussion and years of contact sport exposure (a proxy of repetitive head impacts) with magnetic susceptibility in a cohort of otherwise healthy male and female collegiate-aged athletes. We hypothesized that concussion and contact sport exposure would be independently associated with lower susceptibility in white matter regions. Higher general symptom severity and psychological symptoms were observed in athletes with a greater history of concussion, but not years of contact sport exposure. No associations between concussion or years of exposure with cognitive performance were observed. Voxel-wise analyses found that individuals with more prior concussions also had lower magnetic susceptibility in two predominantly white matter clusters including the superior longitudinal fasciculi and forceps major. No associations of susceptibility and contact sport exposure were observed. Finally, lower susceptibility in the identified regions was associated with worse psychological symptoms, worse general symptoms, and worse performance on a composite measure of fluid cognition tasks. Current results suggest that more prior concussions in otherwise healthy collegiate-aged athletes are associated with decreases in susceptibility that are in turn associated with elevated symptom reporting and poorer cognitive performance.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831201","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":"Identification of Cortical Targets for Modulating Function Supported by the Human Hippocampal Network","authors":"Hsin-Ju Lee,&nbsp;Fa-Hsuan Lin","doi":"10.1002/hbm.70167","DOIUrl":"https://doi.org/10.1002/hbm.70167","url":null,"abstract":"<p>Individualized transcranial magnetic stimulation (TMS) targeting using functional connectivity analysis of functional magnetic resonance imaging (fMRI) has been demonstrated to be advantageous in inducing neuroplasticity. However, how this approach can benefit modulating the episodic memory function supported by the hippocampal network remains elusive. We use the resting-state fMRI data from a large cohort to reveal tentative TMS targets at cortical regions within the hippocampal network. Functional MRI from 1,133 individuals in the Human Connectome Project was used to analyze the hippocampal network using seed-based functional connectivity. Using a weighted sum of time series at the cortex, we identified the average centroids of individualized targets at the medial prefrontal cortex (mPFC) and posterior parietal cortices (PPCs) at (−10, 49, 7) and (−40, −67, 30) in the left hemisphere, respectively. The mPFC and PPC coordinate at the right hemispheres are (11, 51, 6) and (48, −59, 24) in the right hemisphere, respectively. Centroids of the individualized functional connectivity at the mPFC and PPC were reproducible between sessions with separations in average about 2 and 4 mm, respectively. These separations were significantly smaller than the distance to average functional connectivity centroids (~10 mm) and atlas coordinate (~20 mm). These coordinates can be reliably identified (&gt; 90% of individuals) using cortical “seedmaps.” Our results suggest candidate TMS target coordinates to modulate the hippocampal function.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70167","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801801","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}