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      A naturalistic neuroimaging database for understanding the brain using ecological stimuli

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          Abstract

          Neuroimaging has advanced our understanding of human psychology using reductionist stimuli that often do not resemble information the brain naturally encounters. It has improved our understanding of the network organization of the brain mostly through analyses of ‘resting-state’ data for which the functions of networks cannot be verifiably labelled. We make a ‘ Naturalistic Neuroimaging Database’ (NNDb v1.0) publically available to allow for a more complete understanding of the brain under more ecological conditions during which networks can be labelled. Eighty-six participants underwent behavioural testing and watched one of 10 full-length movies while functional magnetic resonance imaging was acquired. Resulting timeseries data are shown to be of high quality, with good signal-to-noise ratio, few outliers and low movement. Data-driven functional analyses provide further evidence of data quality. They also demonstrate accurate timeseries/movie alignment and how movie annotations might be used to label networks. The NNDb can be used to answer questions previously unaddressed with standard neuroimaging approaches, progressing our knowledge of how the brain works in the real world.

          Abstract

          Measurement(s) functional brain measurement • Behavioral Assessment
          Technology Type(s) functional magnetic resonance imaging • NIH Toolbox
          Factor Type(s) movie
          Sample Characteristic - Organism Homo sapiens
          Sample Characteristic - Environment laboratory facility

          Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12869855

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          Most cited references114

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          ImageNet Large Scale Visual Recognition Challenge

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            FreeSurfer.

            FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source. Copyright © 2012 Elsevier Inc. All rights reserved.
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              Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

              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.
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                Author and article information

                Contributors
                sarah.aliko.17@ucl.ac.uk
                jeremy.skipper@ucl.ac.uk
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                13 October 2020
                13 October 2020
                2020
                : 7
                Affiliations
                [1 ]GRID grid.83440.3b, ISNI 0000000121901201, London Interdisciplinary Biosciences Consortium, , University College London, ; London, UK
                [2 ]GRID grid.83440.3b, ISNI 0000000121901201, Experimental Psychology, , University College London, ; London, UK
                [3 ]GRID grid.9435.b, ISNI 0000 0004 0457 9566, School of Psychology and Clinical Language Sciences, , University of Reading, ; Reading, UK
                Article
                680
                10.1038/s41597-020-00680-2
                7555491
                33051448
                f491eedc-36d0-4ee2-8cfe-168173e71bb3
                © The Author(s) 2020

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.

                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000266, RCUK | Engineering and Physical Sciences Research Council (EPSRC);
                Award ID: EP/M026965/1
                Award Recipient :
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                © The Author(s) 2020

                cognitive neuroscience,databases
                cognitive neuroscience, databases

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