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      Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain

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          Abstract

          The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions, in conjunction with modular transcriptomic profiles, which are essential for comprehensive biological interpretation. To mitigate this limitation, our contribution encompasses the provision of an open-access dataset consisting of derivative matrices of functional and structural connectivity across multiple scales, accompanied by code that facilitates the investigation of their interrelations. We also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.

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          Most cited references62

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          An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

          In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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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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              A multi-modal parcellation of human cerebral cortex

              Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.
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                Author and article information

                Contributors
                jesus.m.cortes@gmail.com
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                29 February 2024
                29 February 2024
                2024
                : 11
                : 256
                Affiliations
                [1 ]Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
                [2 ]GRID grid.11480.3c, ISNI 0000000121671098, Biomedical Research Doctorate Program, , University of the Basque Country (UPV/EHU), ; Leioa, Spain
                [3 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, , Massachusetts General Hospital and Harvard Medical School, ; Boston, United States of America
                [4 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Gordon Center for Medical Imaging, Department of Radiology, , Massachusetts General Hospital and Harvard Medical School, ; Boston, United States of America
                [5 ]GRID grid.38142.3c, ISNI 000000041936754X, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, , Harvard Medical School, ; Boston, United States of America
                [6 ]GRID grid.424810.b, ISNI 0000 0004 0467 2314, IKERBASQUE Basque Foundation for Science, ; Bilbao, Spain
                [7 ]Dipartamento Interateneo di Fisica, Universita Degli Studi di Bari Aldo Moro, INFN, ( https://ror.org/027ynra39) Bari, Italy
                [8 ]Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), ( https://ror.org/000xsnr85) Leioa, Spain
                Author information
                http://orcid.org/0000-0001-5769-0178
                http://orcid.org/0000-0002-9402-2184
                http://orcid.org/0000-0002-9059-8194
                Article
                3060
                10.1038/s41597-024-03060-2
                10904384
                38424112
                5d8a8380-9ca1-41dd-a70a-9d08e89cb8e9
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 August 2023
                : 12 February 2024
                Funding
                Funded by: Health Department of the Basque Country (grant 2022111031)
                Funded by: Department of Education of the Basque Country (PRE-2019-1-0070)
                Categories
                Analysis
                Custom metadata
                © Springer Nature Limited 2024

                biomedical engineering,network models
                biomedical engineering, network models

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