دورية أكاديمية
أعرض في EDS

Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.

التفاصيل البيبلوغرافية
العنوان: Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.
المؤلفون: Fu Z; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States. Electronic address: fzn198637@gmail.com., Batta I; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States., Wu L; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States., Abrol A; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States., Agcaoglu O; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States., Salman MS; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States., Du Y; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States., Iraji A; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States., Shultz S; Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States., Sui J; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China., Calhoun VD; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States.
المصدر: NeuroImage [Neuroimage] 2024 Apr 15; Vol. 292, pp. 120617. Date of Electronic Publication: 2024 Apr 16.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Academic Press Country of Publication: United States NLM ID: 9215515 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-9572 (Electronic) Linking ISSN: 10538119 NLM ISO Abbreviation: Neuroimage Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Orlando, FL : Academic Press, c1992-
مواضيع طبية MeSH: Magnetic Resonance Imaging*/methods , Magnetic Resonance Imaging*/standards , Brain*/diagnostic imaging, Humans ; Adult ; Adolescent ; Young Adult ; Male ; Aged ; Female ; Middle Aged ; Infant ; Child ; Aging/physiology ; Child, Preschool ; Reproducibility of Results ; Image Processing, Computer-Assisted/methods ; Image Processing, Computer-Assisted/standards ; Aged, 80 and over ; Neuroimaging/methods ; Neuroimaging/standards ; Diffusion Magnetic Resonance Imaging/methods ; Diffusion Magnetic Resonance Imaging/standards
مستخلص: A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier Inc.)
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معلومات مُعتمدة: R01 EB020407 United States EB NIBIB NIH HHS; R01 MH117107 United States MH NIMH NIH HHS; R01 MH118695 United States MH NIMH NIH HHS; R01 MH119251 United States MH NIMH NIH HHS
فهرسة مساهمة: Keywords: NeuroMark; diffusion template; hybrid; spatiotemporal functional template; structural template
تواريخ الأحداث: Date Created: 20240418 Date Completed: 20240427 Latest Revision: 20240923
رمز التحديث: 20240923
مُعرف محوري في PubMed: PMC11416721
DOI: 10.1016/j.neuroimage.2024.120617
PMID: 38636639
قاعدة البيانات: MEDLINE
الوصف
تدمد:1095-9572
DOI:10.1016/j.neuroimage.2024.120617