2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Heritability of individualized cortical network topography

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Significance

          The widespread use of population-average cortical parcellations has provided important insights into broad properties of human brain organization. However, the size, location, and spatial arrangement of regions comprising functional brain networks can vary substantially across individuals. Here, we demonstrate considerable heritability in both the size and spatial organization of individual-specific network topography across cortex. Genetic factors had a regionally variable influence on brain organization, such that heritability in network size, but not topography, was greater in unimodal relative to heteromodal cortices. These data suggest individual-specific network parcellations may provide an avenue to understand the genetic basis of variation in human cognition and behavior.

          Abstract

          Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and is predictive of behavior, it is not yet clear to what extent genetic factors underlie interindividual differences in network topography. Here, leveraging a nonlinear multidimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project ( n = 1,023), we find increased variability and reduced heritability in the size of heteromodal association networks ( h 2 : M = 0.34, SD = 0.070), relative to unimodal sensory/motor cortex ( h 2 : M = 0.40, SD = 0.097). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multidimensional estimation of heritability ( h 2 -multi; M = 0.14, SD = 0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging.

          Related collections

          Most cited references84

          • Record: found
          • Abstract: found
          • Article: not found

          The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

          Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The minimal preprocessing pipelines for the Human Connectome Project.

            The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines. Copyright © 2013 Elsevier Inc. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.

              An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (< 0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10(-3)) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.
                Bookmark

                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                02 March 2021
                23 February 2021
                23 February 2021
                : 118
                : 9
                : e2016271118
                Affiliations
                [1] aDepartment of Psychology, Yale University , New Haven, CT 06520;
                [2] bPsychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital , Boston, MA 02114;
                [3] cStanley Center for Psychiatric Research, Broad Institute of Massachusetts Institute of Technology and Harvard , Cambridge, MA 02142;
                [4] dDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School , Boston, MA 02114;
                [5] eDepartment of Electrical and Computer Engineering, Centre for Sleep and Cognition, National University of Singapore , Singapore 119077;
                [6] fDepartment of Electrical and Computer Engineering, Centre for Translational Magnetic Resonance Research, National University of Singapore , Singapore 119077;
                [7] gN.1 Institute for Health, National University of Singapore , Singapore 119077;
                [8] hInstitute for Digital Medicine, National University of Singapore , Singapore 119077;
                [9] iDepartment of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University , Montreal, QC H3A 0G4, Canada;
                [10] jMcConnell Brain Imaging Centre, McGill University, Montreal, QC H3A 0G4, Canada;
                [11] kSchool of Electrical and Computer Engineering, Cornell University , Ithaca, NY 14850;
                [12] lMeinig School of Biomedical Engineering, Cornell University , Ithaca, NY 14850;
                [13] mMartinos Center for Biomedical Imaging, Massachusetts General Hospital , Charlestown, MA 02129;
                [14] nNational University of Singapore Graduate School for Integrative Sciences and Engineering, National University of Singapore , Singapore 119077;
                [15] oDepartment of Psychiatry, Yale University , New Haven, CT 06520
                Author notes
                1To whom correspondence may be addressed. Email: kevin.anderson@ 123456yale.edu .

                Edited by Daniel H. Geschwind, University of California, Los Angeles, CA, and accepted by Editorial Board Member Michael S. Gazzaniga January 19, 2021 (received for review August 5, 2020)

                Author contributions: K.M.A., T.G., R.K., L.M.P., R.N.S., M.R.S., B.T.T.Y., and A.J.H. designed research; K.M.A. performed research; K.M.A., T.G., R.K., and B.T.T.Y. contributed new reagents/analytic tools; K.M.A. analyzed data; and K.M.A., T.G., R.K., L.M.P., R.N.S., M.R.S., B.T.T.Y., and A.J.H. wrote the paper.

                Author information
                https://orcid.org/0000-0002-9446-7386
                https://orcid.org/0000-0001-7842-0329
                https://orcid.org/0000-0002-7432-7802
                https://orcid.org/0000-0003-1530-8916
                https://orcid.org/0000-0002-0119-3276
                https://orcid.org/0000-0001-6583-803X
                Article
                202016271
                10.1073/pnas.2016271118
                7936334
                33622790
                6866e0cb-081c-40b5-a357-9c8cb6984eca
                Copyright © 2021 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 9
                Funding
                Funded by: HHS | NIH | National Institute of Mental Health (NIMH) 100000025
                Award ID: R01MH120080
                Award Recipient : Tian Ge Award Recipient : Avram J Holmes
                Funded by: HHS | NIH | National Institute of Mental Health (NIMH) 100000025
                Award ID: R00AG054573
                Award Recipient : Tian Ge Award Recipient : Avram J Holmes
                Funded by: HHS | NIH | U.S. National Library of Medicine (NLM) 100000092
                Award ID: R01LM012719
                Award Recipient : Mert R. Sabuncu
                Funded by: HHS | NIH | National Institute on Aging (NIA) 100000049
                Award ID: R01AG053949
                Award Recipient : Mert R. Sabuncu
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: DGE-1122492
                Award Recipient : Kevin M Anderson Award Recipient : Mert R. Sabuncu
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: CAREER 1748377
                Award Recipient : Kevin M Anderson Award Recipient : Mert R. Sabuncu
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: NeuroNex 1707312
                Award Recipient : Kevin M Anderson Award Recipient : Mert R. Sabuncu
                Funded by: National Research Foundation Singapore (NRF) 501100001381
                Award ID: Fellowship (Class of 2017)
                Award Recipient : Ru Kong Award Recipient : B.T. Thomas Yeo
                Funded by: National University of Singapore (NUS) 501100001352
                Award ID: NUHSRO/2020/124/TMR/LOA).
                Award Recipient : Ru Kong Award Recipient : B.T. Thomas Yeo
                Categories
                431
                Biological Sciences
                Psychological and Cognitive Sciences
                Social Sciences
                Psychological and Cognitive Sciences

                heritability,individualized parcellation,resting-state,function brain networks,functional connectome

                Comments

                Comment on this article