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      An open resource for transdiagnostic research in pediatric mental health and learning disorders

      1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 2 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 ,   1 , 3 , 4 , 1 , 1 , 5 , 1 , 1 , 1 , 1 , 1 , 6 , 1 , 1 , 5 , 7 , 1 , 1 , 1 , 5 , 4 , 8 , 9 , 10 , 11 , 1 , 1 , 1 , 1 , 3 , 12 , 1 , 1 , 1 , 1 , 1 , 13 , 1 , 1 , 1 , 14 , 15 , 1 , 1 , 16 , 17 , 1 , 18 , 1 , 13 , a , 1 , b , 1 , 13
      Scientific Data
      Nature Publishing Group
      Psychology, Neuroscience, Brain imaging, Functional magnetic resonance imaging, Magnetic resonance imaging

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          Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5–21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release ( n=664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).

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          An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.

          Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity.

            Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of participant-level confound regression methods to limit its impact. However, many of the most commonly used techniques have not been systematically evaluated using a broad range of outcome measures. Here, we provide a systematic evaluation of 14 participant-level confound regression methods in 393 youths. Specifically, we compare methods according to four benchmarks, including the residual relationship between motion and connectivity, distance-dependent effects of motion on connectivity, network identifiability, and additional degrees of freedom lost in confound regression. Our results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in distance-dependent artifact. In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom. Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome. Taken together, these results emphasize the heterogeneous efficacy of existing methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.
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              Medicine. Brain disorders? Precisely.


                Author and article information

                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group
                19 December 2017
                : 4
                : 170181
                [1 ]Center for the Developing Brain, Child Mind Institute , New York, NY 10022, USA
                [2 ]Universität Zürich , Zürich 8006, Switzerland
                [3 ]City University of New York , NY 10031, USA
                [4 ]Center for Autism and the Developing Brain, Weill Cornell Medicine, New York-Presbyterian , White Plains, NY 10605, USA
                [5 ]New York State Institute for Basic Research in Developmental Disabilities , Staten Island, NY 10314, USA
                [6 ]Icahn School of Medicine at Mount Sinai , New York, NY 10029, USA
                [7 ]School of Electrical and Electronic Engineering, University College Dublin , Dublin D4, Ireland
                [8 ]Office of Staten Island Borough President , Staten Island, NY 10301, USA
                [9 ]Columbia University Medical Center , New York, NY 10032, USA
                [10 ]Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health , Bethesda, MD 20892, USA
                [11 ]Mercy College , Dobbs Ferry, NY 10522, USA
                [12 ]Yale University , New Haven, CT 06520, USA
                [13 ]Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research , Orangeburg, NY 10962, USA
                [14 ]Division of Child and Adolescent Psychiatric Research, Nathan Kline Institute for Psychiatric Research , Orangeburg, NY 10962, USA
                [15 ]Department of Child and Adolescent Psychiatry, NYU Langone Health , New York, NY 10016, USA
                [16 ]Rotman Research Institute, Baycrest , Toronto M6A 2E1, Canada
                [17 ]Departments of Psychology and Psychiatry, University of Toronto , Toronto M5S, Canada
                [18 ]University of California, San Francisco , San Francisco, CA 94143, USA
                Author notes

                Conception and Experimental Design: C.A., L.M.A., J.E., M.P.M., R.C.C., H.S.K., N.L., C.L., A.M., K.R.M., J.M., A.N., K.R.P., F.X.C., A.K., T.P., B.L.L., S.C., S.P.K., L.C.P., B.K., J.G., M.H., G.M. Implementation and Logistics: A.A., C.A., L.M.A., J.E., M.P.M., A.M., N.V.P., K.F., S.L., B.O.H., M.B., N.G.V., K.K. l, Y.O., B.S., R.T., A.K., T.P., B.L.L., J.C. Data Collection: C.A., K.F., A.A., M.K., S.L., B.O.H., J.A., B.B., A.B., H.B., V.C., N.C., E.C., D.C., M.E., B.F., N.G.V., G.G., C.G., E.H., S.H., D.K., K.K., K.K. l, E.K., D.K., G.M., R.N., A.N., Y.O., A.R., A.R. e, T.S., B.S., R.W., A.W., A.Y. Data Informatics: L.M.A., M.P.M., R.C.C., L.A., B.K. Data Analysis: L.M.A., L.A., M.P.M. Drafting of the Manuscript: L.M.A., M.P.M. Critical Review and Editing of the Manuscript: All authors contributed to the critical review and editing of the manuscript.

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                Copyright © 2017, The Author(s)

                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 made available in this article.

                : 17 July 2017
                : 11 October 2017
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                psychology,neuroscience,brain imaging,functional magnetic resonance imaging,magnetic resonance imaging


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