A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework
Brain maturation studies typically examine relationships linking a single morphometric feature with cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior ar...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811923000344 |
| _version_ | 1811169505240416256 |
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| author | Hadis Kalantar-Hormozi Raihaan Patel Alyssa Dai Justine Ziolkowski Hao-Ming Dong Avram Holmes Armin Raznahan Gabriel A. Devenyi M. Mallar Chakravarty |
| author_facet | Hadis Kalantar-Hormozi Raihaan Patel Alyssa Dai Justine Ziolkowski Hao-Ming Dong Avram Holmes Armin Raznahan Gabriel A. Devenyi M. Mallar Chakravarty |
| author_sort | Hadis Kalantar-Hormozi |
| collection | DOAJ |
| description | Brain maturation studies typically examine relationships linking a single morphometric feature with cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the NIMH developmental cohort (ages 5–25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. Using this technique to examine covariance in rates of change to identify longitudinal sources of covariance highlighted preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV) that recapitulated patterns of reduced CT, GI, and SA related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. Finally, we situated the components in the changing architecture of cortical gradients. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment. |
| first_indexed | 2024-04-10T16:43:30Z |
| format | Article |
| id | doaj.art-a927cf2dae6941a1b294727a4a335e93 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-04-10T16:43:30Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-a927cf2dae6941a1b294727a4a335e932023-02-08T04:16:31ZengElsevierNeuroImage1095-95722023-03-01268119885A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical frameworkHadis Kalantar-Hormozi0Raihaan Patel1Alyssa Dai2Justine Ziolkowski3Hao-Ming Dong4Avram Holmes5Armin Raznahan6Gabriel A. Devenyi7M. Mallar Chakravarty8Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada; Corresponding author at: Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada.Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, CanadaIntegrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, CanadaIntegrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, CanadaState Key Laboratory of Cognitive Neuroscience and Learning, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Department of Psychology, Yale University, New Haven, USADepartment of Psychology, Yale University, New Haven, USASection on Developmental Neurogenomics, National Institute of Mental Health (NIMH), Bethesda, MD, USAComputational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, CanadaIntegrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, CanadaBrain maturation studies typically examine relationships linking a single morphometric feature with cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the NIMH developmental cohort (ages 5–25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. Using this technique to examine covariance in rates of change to identify longitudinal sources of covariance highlighted preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV) that recapitulated patterns of reduced CT, GI, and SA related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. Finally, we situated the components in the changing architecture of cortical gradients. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment.http://www.sciencedirect.com/science/article/pii/S1053811923000344Brain developmentCortical morphometryMRINon-negative matrix factorizationDemographicsMaturational gradients |
| spellingShingle | Hadis Kalantar-Hormozi Raihaan Patel Alyssa Dai Justine Ziolkowski Hao-Ming Dong Avram Holmes Armin Raznahan Gabriel A. Devenyi M. Mallar Chakravarty A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework NeuroImage Brain development Cortical morphometry MRI Non-negative matrix factorization Demographics Maturational gradients |
| title | A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework |
| title_full | A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework |
| title_fullStr | A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework |
| title_full_unstemmed | A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework |
| title_short | A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework |
| title_sort | cross sectional and longitudinal study of human brain development the integration of cortical thickness surface area gyrification index and cortical curvature into a unified analytical framework |
| topic | Brain development Cortical morphometry MRI Non-negative matrix factorization Demographics Maturational gradients |
| url | http://www.sciencedirect.com/science/article/pii/S1053811923000344 |
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