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      Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex

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      PLoS Biology
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          Abstract

          Whole-brain neural communication is typically estimated from statistical associations among electromagnetic or haemodynamic time-series. The relationship between functional network architectures recovered from these 2 types of neural activity remains unknown. Here, we map electromagnetic networks (measured using magnetoencephalography (MEG)) to haemodynamic networks (measured using functional magnetic resonance imaging (fMRI)). We find that the relationship between the 2 modalities is regionally heterogeneous and systematically follows the cortical hierarchy, with close correspondence in unimodal cortex and poor correspondence in transmodal cortex. Comparison with the BigBrain histological atlas reveals that electromagnetic–haemodynamic coupling is driven by laminar differentiation and neuron density, suggesting that the mapping between the 2 modalities can be explained by cytoarchitectural variation. Importantly, haemodynamic connectivity cannot be explained by electromagnetic activity in a single frequency band, but rather arises from the mixing of multiple neurophysiological rhythms. Correspondence between the two is largely driven by MEG functional connectivity at the beta (15 to 29 Hz) frequency band. Collectively, these findings demonstrate highly organized but only partly overlapping patterns of connectivity in MEG and fMRI functional networks, opening fundamentally new avenues for studying the relationship between cortical microarchitecture and multimodal connectivity patterns.

          Abstract

          What is the relationship between functional network architectures inferred from electromagnetic and haemodynamic data? This study shows that superposition of electromagnetic networks at canonical frequency bands manifests as highly structured patterns of haemodynamic functional connectivity in the human brain, reflecting systematic variation in cytoarchitecture.

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          Most cited references159

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            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.
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              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.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                1 August 2022
                August 2022
                1 August 2022
                : 20
                : 8
                : e3001735
                Affiliations
                [001] McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
                University of Glasgow, UNITED KINGDOM
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-2036-5571
                https://orcid.org/0000-0003-0307-2862
                Article
                PBIOLOGY-D-22-00404
                10.1371/journal.pbio.3001735
                9371256
                35914002
                696672d6-f7e2-4ff9-a6a4-def861e792f1
                © 2022 Shafiei et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 19 February 2022
                : 30 June 2022
                Page count
                Figures: 4, Tables: 0, Pages: 27
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000038, Natural Sciences and Engineering Research Council of Canada;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003151, Fonds de recherche du Québec – Nature et technologies;
                Award Recipient :
                Funded by: National Institute of Health (US)
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000038, Natural Sciences and Engineering Research Council of Canada;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001804, Canada Research Chairs;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100009408, Fondation Brain Canada;
                Award Recipient :
                Funded by: Healthy Brains for Healthy Lives
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000038, Natural Sciences and Engineering Research Council of Canada;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001804, Canada Research Chairs;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100009408, Fondation Brain Canada;
                Award Recipient :
                Funded by: Healthy Brains for Healthy Lives
                Award Recipient :
                GS acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Fonds de recherche du Quebec - Nature et Technologies (FRQNT). SB is acknowledges support from the National Institute of Health (NIH), the Natural Science and Engineering Research Council of Canada (NSERC), Canada Research Chairs program (CRC), Brain Canada Foundation, and the Healthy Brains for Healthy Lives initiative (HBHL). BM acknowledges support from the Natural Science and Engineering Research Council of Canada (NSERC), the Canadian Institutes of Health Research (CIHR), Canada Research Chairs program (CRC), Brain Canada Foundation and the Healthy Brains for Healthy Lives initiative (HBHL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                2022-08-11
                Code and data used to conduct the reported analyses is available on GitHub ( https://github.com/netneurolab/shafiei_megfmrimapping). Data used in this study were obtained from the Human Connectome Project (HCP) database (original HCP Young Adult data available at https://db.humanconnectome.org/ via Amazon Web Services (AWS)). The data and code needed to generate all main and supplementary figures can be found in https://github.com/netneurolab/shafiei_megfmrimapping and https://zenodo.org/record/6728338.

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