Human Brain Mapping最新文献

{"title":"Improving the Quality of Brain PET Images Using MR-Guided PET Reconstruction Technique With Integrated PET/MR","authors":"Hongda Shao,&nbsp;Gan Huang,&nbsp;Yue Wang,&nbsp;Yan Zhang,&nbsp;Qiaoyi Xue,&nbsp;Jiaxin Hao,&nbsp;Jianjun Liu","doi":"10.1002/hbm.70292","DOIUrl":"https://doi.org/10.1002/hbm.70292","url":null,"abstract":"<p>To evaluate the performance of an MR-guided PET reconstruction method in enhancing the image quality of brain PET images acquired with an integrated PET/MR system. We implemented an MR-guided PET reconstruction (MRg) algorithm in an integrated PET/MR system and conducted both phantom and clinical studies. The PET images of a Hoffman phantom were reconstructed using the MRg algorithm and routine Ordered Subsets Expectation Maximization (OSEM) algorithm. Image quality metrics were calculated to investigate the dose effects, number of iterations, and penalization factor (<i>β</i>). In the clinical study, the PET images of 30 patients without brain lesions, two patients with diffuse large B cell lymphoma (DLBCL), 18 patients with Parkinson's disease (PD) and one patient with cavernous angioma were reconstructed using MRg and OSEM, respectively. Qualitative and quantitative assessments were performed. In the phantom study, MRg images demonstrated improved image quality metrics (higher contrast and lower variability) compared to conventional OSEM, particularly at lower doses. The penalization factor <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 <mo>=</mo>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 <annotation>$$ beta =2 $$</annotation>\u0000 </semantics></math> maximized the advantages of MRg. In the clinical study of patients without brain lesions, MRg significantly outperformed OSEM in image quality evaluation. For patients with DLBCL, the lesions' signal/contrast-to-background/noise ratios exhibited significant improvements using the MRg. For patients with PD, the standardized uptake value ratio and contrast-to-noise ratio of bilateral putamen were significantly improved by MRg. The MRg has convincingly demonstrated significant advantages over the conventional OSEM in brain PET images, indicating great potential in clinical routines for brain PET/MR examinations.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773844","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":"Cerebral Amyloid Deposition With 18F-Florbetapir PET Mediates Retinal Vascular Density and Cognitive Impairment in Alzheimer's Disease","authors":"Zhigeng Chen,&nbsp;Hai-Long He,&nbsp;Zhongyuan Qi,&nbsp;Sheng Bi,&nbsp;Hongwei Yang,&nbsp;Xuanyu Chen,&nbsp;Tianze Xu,&nbsp;Zi-Bing Jin,&nbsp;Shaozhen Yan,&nbsp;Jie Lu","doi":"10.1002/hbm.70310","DOIUrl":"https://doi.org/10.1002/hbm.70310","url":null,"abstract":"<p>Alzheimer's disease (AD) is accompanied by alterations in retinal vascular density (VD), but the mechanisms remain unclear. This study investigated the relationship among cerebral amyloid-β (Aβ) deposition, VD, and cognitive decline. We enrolled 92 participants, including 47 AD patients and 45 healthy control (HC) participants. VD across retinal subregions was quantified using deep learning-based fundus photography, and cerebral Aβ deposition was measured with ¹⁸F-florbetapir (¹⁸F-AV45) PET/MRI. Using the minimum bounding circle of the optic disc as the diameter (papilla-diameter, PD), VD (total, 0.5–1.0 PD, 1.0–1.5 PD, 1.5–2.0 PD, 2.0–2.5 PD) was calculated. Standardized uptake value ratio (SUVR) for Aβ deposition was computed for global and regional cortical areas, using the cerebellar cortex as the reference region. Cognitive performance was assessed with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Pearson correlation, multiple linear regression, and mediation analyses were used to explore Aβ deposition, VD, and cognition. AD patients exhibited significantly lower VD in all subregions compared to HC (<i>p</i> &lt; 0.05). Reduced VD correlated with higher SUVR in the global cortex and a decline in cognitive abilities (<i>p</i> &lt; 0.05). Mediation analysis indicated that VD influenced MMSE and MoCA through SUVR in the global cortex, with the most pronounced effects observed in the 1.0–1.5 PD range. Retinal VD is associated with cognitive decline, a relationship primarily mediated by cerebral Aβ deposition measured via ¹⁸F-AV45 PET. These findings highlight the potential of retinal VD as a biomarker for early detection in AD.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767888","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":"Vertical Topography in EEG Microstates: Physiology or Artifact Manifestation?","authors":"Tomáš Jordánek,&nbsp;Martin Lamoš,&nbsp;Radek Mareček","doi":"10.1002/hbm.70294","DOIUrl":"https://doi.org/10.1002/hbm.70294","url":null,"abstract":"<p>The analysis of EEG microstates offers a valuable approach for investigating large-scale brain networks and dynamics. Beyond the commonly reported “canonical microstates,” prior literature has identified another distinct topography: the vertical topography (VT). This VT is characterized by a prominent straight line dividing positive and negative values, extending from the nasion to the inion. Notably, our own simultaneous EEG/fMRI and shielded cabin EEG data, collected from 77 participants, also revealed the presence of this topography. Based on our subsequent analyses of both human and phantom data, we conclude that VT partly represents artifacts arising from unspecified movements of the EEG cap and its metallic components. This conclusion is strongly supported by our evaluation of VT's spatiotemporal characteristics, derived from EEG recorded under diverse conditions. Specifically, we found a significant correlation between framewise displacement (obtained from human EEG/fMRI) and VT's temporal characteristics. Therefore, we advocate for a prudent interpretation of VT when it appears in data. Its mere existence as a resulting topography can impact the spatiotemporal parameters of other microstates and even distort the shapes of the other topographies.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70294","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773845","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-Dataset Evaluation of Dementia Longitudinal Progression Prediction Models","authors":"Chen Zhang,&nbsp;Lijun An,&nbsp;Naren Wulan,&nbsp;Kim-Ngan Nguyen,&nbsp;Csaba Orban,&nbsp;Pansheng Chen,&nbsp;Christopher Chen,&nbsp;Juan Helen Zhou,&nbsp;Keli Liu,&nbsp;B. T. Thomas Yeo,&nbsp;Alzheimer's Disease Neuroimaging Initiative and Australian Imaging Biomarkers and Lifestyle Study of Aging","doi":"10.1002/hbm.70280","DOIUrl":"https://doi.org/10.1002/hbm.70280","url":null,"abstract":"<p>Accurately predicting Alzheimer's Disease (AD) progression is useful for clinical care. The 2019 TADPOLE (The Alzheimer's Disease Prediction Of Longitudinal Evolution) challenge evaluated 92 algorithms from 33 teams worldwide. Unlike typical clinical prediction studies, TADPOLE accommodates (1) a variable number of observed time points across patients, (2) missing data across modalities and visits, and (3) prediction over an open-ended time horizon, which better reflects real-world data. However, TADPOLE only used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, so how well top algorithms generalize to other cohorts remains unclear. We tested five algorithms in three external datasets covering 2312 participants and 13,200 time points. The algorithms included FROG, the overall TADPOLE winner, which utilized a unique Longitudinal-to-Cross-sectional (L2C) transformation to convert variable-length longitudinal histories into feature vectors of the same length across participants (i.e., same-length feature vectors). We also considered two FROG variants. One variant unified all XGBoost models from the original FROG with a single feedforward neural network (FNN), which we referred to as L2C-FNN. We also included minimal recurrent neural networks (MinimalRNN), which were ranked second at publication time, as well as AD Course Map (AD-Map), which outperformed MinimalRNN at publication time. All five models—three FROG variants, MinimalRNN, and AD-Map—were trained on ADNI and tested on the external datasets. L2C-FNN performed the best overall. In the case of predicting cognition and ventricle volume, L2C-FNN and AD-Map were the best. For clinical diagnosis prediction, L2C-FNN was the best, while AD-Map was the worst. L2C-FNN also maintained its edge over other models, regardless of the number of observed time points and regardless of the prediction horizon from 0 to 6 years into the future. L2C-FNN shows strong potential for both short-term and long-term dementia progression prediction. Pretrained ADNI models are available: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/predict_phenotypes/Zhang2025_L2CFNN.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70280","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751286","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":"Maturation of Hippocampal Subfields in Young Adulthood and Its Relationship With Cognition","authors":"Klara Mareckova,&nbsp;Gabriel A. Devenyi,&nbsp;Lenka Andryskova,&nbsp;Mallar M. Chakravarty,&nbsp;Yuliya S. Nikolova","doi":"10.1002/hbm.70296","DOIUrl":"https://doi.org/10.1002/hbm.70296","url":null,"abstract":"<p>The hippocampus is a key brain region for memory and cognitive functions, which consists of distinct subregions with different developmental trajectories throughout adolescence. However, trajectories of hippocampal subfield change in young adulthood remain uncharacterized, as is their potential relationship with cortical brain aging and cognitive ability during this time. We conducted two magnetic resonance imaging (MRI) follow-ups of a prenatal birth cohort in young adulthood and studied the effects of chronological age and cortical brain age on the volume of hippocampal subfields in the early 20s (<i>n</i> = 109; 51% men) and late 20s (<i>n</i> = 251; 53% men) and how these age-related volumetric changes might relate to full-scale IQ (FSIQ). We showed that CA1 and CA4DG subfields continue to grow in the third decade of life and that this growth can be observed both at the level of chronological age as well as estimated cortical brain age at both MRI timepoints. Moreover, in men, a larger size of these age-related subfields was associated with higher FSIQ, and the deviations between cortical brain age and chronological age mediated the relationships between right CA1 and FSIQ, as well as right CA4DG and FSIQ. These findings reveal that coordinated patterns of advanced cortical brain aging and hippocampal maturation may confer a cognitive advantage in young adulthood.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70296","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751269","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":"Enhanced Detection of Age-Related and Cognitive Declines Using Automated Hippocampal-To-Ventricle Ratio in Alzheimer's Patients","authors":"Sofia Fernandez-Lozano,&nbsp;Vladimir Fonov,&nbsp;Dorothee Schoemaker,&nbsp;Jens Pruessner,&nbsp;Olivier Potvin,&nbsp;Simon Duchesne,&nbsp;D. Louis Collins,&nbsp;Alzheimer's Disease Neuroimaging Initiative","doi":"10.1002/hbm.70265","DOIUrl":"https://doi.org/10.1002/hbm.70265","url":null,"abstract":"<p>The hippocampal-to-ventricle ratio (HVR) is a biomarker of medial temporal atrophy, particularly useful in the assessment of neurodegeneration in diseases such as Alzheimer's disease (AD). To minimize subjectivity and inter-rater variability, an automated, accurate, precise, and reliable segmentation technique for the hippocampus (HC) and surrounding cerebro-spinal fluid (CSF) filled spaces—such as the temporal horns of the lateral ventricles—is essential. We trained and evaluated three automated methods for the segmentation of both HC and CSF (Multi-Atlas Label Fusion (MALF), Nonlinear Patch-Based Segmentation (NLPB), and a Convolutional Neural Network (CNN)). We then evaluated these methods, including the widely used <i>FreeSurfer</i> technique, using baseline T1w MRIs of 1641 participants from the AD Neuroimaging Initiative study with various degree of atrophy associated with their cognitive status on the spectrum from cognitively healthy to clinically probable AD. Our gold standard consisted in manual segmentation of HC and CSF from 80 cognitively healthy individuals. We calculated HC volumes and HVR and compared all methods in terms of segmentation reliability, similarity across methods, sensitivity in detecting between-group differences and associations with age, scores of the learning subtest of the Rey Auditory Verbal Learning Test (RAVLT) and the Alzheimer's Disease Assessment Scale 13 (ADAS13) scores. Cross validation demonstrated that the CNN method yielded more accurate HC and CSF segmentations when compared to MALF and NLPB, demonstrating higher volumetric overlap (Dice Kappa = 0.94) and correlation (rho = 0.99) with the manual labels. It was also the most reliable method in clinical data application, showing minimal failures. Our comparisons yielded high correlations between FreeSurfer, CNN and NLPB volumetric values. HVR yielded higher control:AD effect sizes than HC volumes among all segmentation methods, reinforcing the significance of HVR in clinical distinction. The positive association with age was significantly stronger for HVR compared to HC volumes on all methods except FreeSurfer. Memory associations with HC volumes or HVR were only significant for individuals with mild cognitive impairment. Finally, the HC volumes and HVR showed comparable negative associations with ADAS13, particularly in the mild cognitive impairment cohort. This study provides an evaluation of automated segmentation methods centered to estimate HVR, emphasizing the superior performance of a CNN-based algorithm. The findings underscore the pivotal role of accurate segmentation in HVR calculations for precise clinical applications, contributing valuable insights into medial temporal lobe atrophy in neurodegenerative disorders, especially AD.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70265","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751244","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":"Effects of Oral Contraceptive Pills on Brain Networks: A Conceptual Replication and Extension.","authors":"Gino Haase, Jason Liu, Timothy Jordan, Andrea Rapkin, Edythe D London, Nicole Petersen","doi":"10.1002/hbm.70318","DOIUrl":"10.1002/hbm.70318","url":null,"abstract":"<p><p>Neuroimaging studies have reported that oral contraceptive pills (OCPs) alter brain connectivity, but findings have varied widely and are often derived from observational designs. Here, we used a randomized, double-blind, placebo-controlled crossover design to test for conceptual replication of three prior studies and to explore broader network-level effects of OCPs. Replication analyses largely did not confirm previously reported seed-based connectivity changes in edges containing amygdala, putamen, or dorsal anterior cingulate nodes (ps > 0.05). In the absence of a clear replication, we pursued exploratory analyses using functional connectome fingerprinting, a multivariate, data-driven method that allows parsimonious interrogation of connectivity changes simultaneously across the whole by detecting whole-brain connectivity patterns that distinguish individuals from one another. This approach revealed network-level changes during OCP use, especially within subcortical, executive, and somatomotor circuits. OCPs also increased between-subject similarity in functional connectomes (I_other, p_FWE < 0.001), suggesting a loss of individual idiosyncrasy while using OCPs. Intraclass correlations indicated that idiosyncrasy was significantly lowered in the default mode, executive, limbic, salience, somatomotor, and subcortical networks (all p_FWE < 0.05). Finally, changes in functional connectivity were significantly associated with increases in negative affect, with 13 connectivity edges showing significant (p < 0.001) correlations with DRSP symptom scores. These findings suggest that OCPs induce widespread and individually meaningful alterations to brain network organization, which may underlie mood-related side effects and should be considered in future neuroimaging research involving hormonal contraceptive users.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":"e70318"},"PeriodicalIF":3.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12335007/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144803979","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":"Along-Tract Diffusion Alterations in the Dentato-Rubro-Thalamic Tract Correlate With Motor and Cognitive Decline in Huntington's Disease","authors":"Zexi Wang,&nbsp;Alexia Solomon,&nbsp;Janine M. Lupo,&nbsp;Jingwen Yao","doi":"10.1002/hbm.70305","DOIUrl":"https://doi.org/10.1002/hbm.70305","url":null,"abstract":"<p>Huntington's disease (HD) is a progressive neurodegenerative disorder caused by cytosine-adenine-guanine repeat expansion in the huntingtin (HTT) gene, leading to widespread brain atrophy and white matter degeneration. Although cortico-striatal pathways have been extensively studied, the dentato-rubro-thalamic tract (DRTT), a key cerebellar efferent pathway integrating motor and cognitive functions, remains largely unexplored, despite increasing evidence of cerebellar involvement in these functions. By investigating microstructural alterations along the DRTT, we aim to elucidate its role in HD progression and its association with motor and cognitive impairments, providing insights into the potential contribution of the DRTT to disease severity and clinical outcomes. We retrospectively analyzed 1392 scans from 638 participants across three multinational HD cohorts (TRACK-HD/ON, PREDICT-HD, and IMAGE-HD) with standardized inclusion criteria, and applied the HD-ISS to categorize disease stages. Probabilistic tractography was performed on diffusion MRI data to reconstruct ipsilateral and decussating DRTT pathways ending in the motor or pre-frontal cortices, with fractional anisotropy (FA) and mean diffusivity (MD) values extracted along 100 nodes per tract. Along-tract analyses were conducted using linear mixed-effects models to assess group differences and correlations with motor and cognitive scores, while controlling for covariates. Significantly decreased FA and increased MD were observed in premanifest HD (PM, HD-ISS Stage 0 and 1) and manifest HD (HD, HD-ISS Stage 2 and 3) groups and over time compared to healthy controls (HC) across multiple regions along the DRTT, particularly in the dentate nucleus region and dentate nucleus-red nucleus projection. These microstructural changes were correlated to the greater motor and cognitive impairments. Conversely, the DRTT thalamo-cortical projection exhibited an opposite pattern, with higher FA in PM and HD than in HC. Both FA and MD were also positively correlated with motor score within this segment. Along-tract analysis revealed microstructural disruptions across DRTT in both premanifest and manifest HD individuals, suggesting that the DRTT plays a role in HD progression. Our findings also highlight the value of assessing regional changes along the tract. These segment-specific white matter alterations provide additional insights into HD pathology and may serve as biomarkers for motor and cognitive impairments in HD.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751243","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":"Towards an Informed Choice of Diffusion MRI Image Contrasts for Cerebellar Segmentation.","authors":"Jon Haitz Legarreta, Zhou Lan, Yuqian Chen, Fan Zhang, Edward H Yeterian, Nikos Makris, Richard J Rushmore, Yogesh Rathi, Lauren J O'Donnell","doi":"10.1002/hbm.70317","DOIUrl":"10.1002/hbm.70317","url":null,"abstract":"<p><p>The fine-grained segmentation of cerebellar structures is an essential step towards supplying increasingly accurate anatomically informed analyses, including, for example, white matter diffusion magnetic resonance imaging (MRI) tractography. Cerebellar tissue segmentation is typically performed on structural MRI data, such as T1-weighted data, while connectivity between segmented regions is mapped using diffusion MRI tractography data. Small deviations in structural to diffusion MRI data co-registration may negatively impact connectivity analyses. Reliable segmentation of brain tissue performed directly on diffusion MRI data helps to circumvent such inaccuracies. Diffusion MRI enables the computation of many image contrasts, including a variety of tissue microstructure maps. While multiple methods have been proposed for the segmentation of cerebellar structures using diffusion MRI, little attention has been paid to the systematic evaluation of the performance of different available input image contrasts for the segmentation task. In this work, we evaluate and compare the segmentation performance of diffusion MRI-derived contrasts on the cerebellar segmentation task. Specifically, we include spherical mean (diffusion-weighted image average) and b0 (non-diffusion-weighted image average) contrasts, local signal parameterization contrasts (diffusion tensor and kurtosis fit maps), and the structural T1-weighted MRI contrast that is most commonly employed for the task. We train a popular deep-learning architecture using a publicly available dataset (HCP-YA) on a set of cerebellar white and gray matter region labels obtained from the atlas-based SUIT cerebellar segmentation pipeline employing T1-weighted data. By training and testing using many diffusion-MRI-derived image inputs, we find that the spherical mean image computed from b = 1000 s/mm² shell data provides stable performance across different metrics and significantly outperforms the tissue microstructure contrasts that are traditionally used in machine learning segmentation methods for diffusion MRI.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":"e70317"},"PeriodicalIF":3.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12332183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144798957","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":"Functionally Adaptive Structural Basis Sets of the Brain: A Dynamic Fusion Approach","authors":"Marlena Duda,&nbsp;Jiayu Chen,&nbsp;Aysenil Belger,&nbsp;Judith Ford,&nbsp;Daniel Mathalon,&nbsp;Adrian Preda,&nbsp;Jessica Turner,&nbsp;Theo Van Erp,&nbsp;Godfrey Pearlson,&nbsp;Vince D. Calhoun","doi":"10.1002/hbm.70302","DOIUrl":"https://doi.org/10.1002/hbm.70302","url":null,"abstract":"<p>The precise relationship between brain structure and function has been investigated through a multitude of lenses, but one detail that is held constant across most neuroimaging studies in this space is the identification of a singular structural basis set of the brain, upon which functional activation signals can be reconstructed to examine the linkage between structure and function. Such basis sets can be considered “functionally independent”, as they are derived through structural data alone and have no explicit association to functional data. Recent work in multimodal fusion has facilitated a more integrated view of structure–function linkages by enabling the equal contribution of both modalities to the joint decomposition, resulting in components that are independent within modality but co-vary closely across modalities. These existing symmetric fusion approaches thus identify structural bases given an associated functional context. In this work, we consider an additional layer of precision to the investigation of structure–function coupling by studying these context-dependent linkages in a time-resolved manner. In other words, we ask which features of brain structure become (or remain) salient given the dynamically changing functional contexts (i.e., dynamic functional connectivity states, task structure, etc.) the brain may pass through during a given fMRI scan. We introduce “dynamic fusion”, an ICA-based symmetric fusion approach that enables flexible, time-resolved linkages between brain structure and dynamic brain function. We show evidence that temporally resolved and functionally contextualized structural basis sets can accurately reflect dynamic functional processes and capture diagnostically relevant structure–functional coupling while detecting nuanced functionally driven structural components that cannot be captured with traditionally computed structural bases. Lastly, differential analysis of component stability across repeated scans from a control cohort reveals that the organization of static and dynamic structure-function coupling falls along unimodal/transmodal hierarchical lines.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 11","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70302","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751287","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}