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      The Human Connectome Project’s Neuroimaging Approach

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          Abstract

          Non-invasive human neuroimaging has yielded many exciting discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis, and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The “HCP-style” paradigm has seven core tenets: (1) collect multimodal imaging data from many subjects; (2) acquire data at high spatial and temporal resolution; (3) preprocess data to minimize distortions, blurring, and temporal artifacts; (4) represent data using the natural geometry of cortical and subcortical structures; (5) accurately align corresponding brain areas across subjects and studies; (6) analyze data using neurobiologically accurate brain parcellations; and (7) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP datasets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease.

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

          B. Yeo, Fenna M. Krienen, Jorge Sepulcre … (2011)
          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.
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            Cortical surface-based analysis. I. Segmentation and surface reconstruction.

            Anders Dale, Bruce Fischl, Martin I. Sereno (1999)
            Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.
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              Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

              Jonathan D Power, Kelly A Barnes, Abraham Z Snyder … (2012)
              Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements. Copyright © 2011 Elsevier Inc. All rights reserved.
              Article 0 comments Cited 1364 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 9809671
                Journal ID (pubmed-jr-id): 21092
                Journal ID (nlm-ta): Nat Neurosci
                Journal ID (iso-abbrev): Nat. Neurosci.
                Title: Nature neuroscience
                ISSN (Print): 1097-6256
                ISSN (Electronic): 1546-1726
                Publication date Nihms-submitted: 27 September 2018
                Publication date (Print): 26 August 2016
                Publication date PMC-release: 05 October 2018
                Volume: 19
                Issue: 9
                Pages: 1175-1187
                Affiliations
                [1 ]Department of Neuroscience, Washington University Medical School, Saint Louis, MO, USA;
                [2 ]FMRIB Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK;
                [3 ]Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA;
                [4 ]Department of Psychiatry, Washington University Medical School, Saint Louis, MO;
                [5 ]Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                Author notes
                Corresponding author: David C. Van Essen; address correspondence to glasserm@ 123456wustl.edu and vanessen@ 123456wustl.edu .

                Author Contributions M.F.G., S.M.S., D.S.M., K.U., and D.C.V.E. framed the issues and generated the initial draft. M.F.G., S.M.S., D.S.M., J.A., E.J.A., T.E.J.B, T.S.C., M.P.H., M.J., S.M., E.C.R., S.N.S., J.X., E.Y., K.U., and D.C.V.E. contributed novel methods or analyses. M.F.G., S.M.S., D.S.M., T.E.J.B, T.S.C., M.P.H., E.C.R., S.N.S., J.X., E.Y., K.U., and D.C.V.E. wrote the manuscript.

                Article
                Accession ID: PMC6172654 Pmcid ID: PMC6172654 Pmc-uid ID: 6172654 Manuscript ID: nihpa990058
                DOI: 10.1038/nn.4361
                PMC ID: 6172654
                PubMed ID: 27571196
                SO-VID: 25e7fe92-770c-4ebe-ae82-ce16dd1795a0
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