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      Early brain development in infants at high risk for autism spectrum disorder

      research-article
      , Ph.D. 1 , 2 , , Ph.D. 1 , , Ph.D. 3 , , Ph.D. 1 , , Ph.D. 1 , , Ph.D. 4 , , Ph.D. 5 , , Ph.D. 2 , , Ph.D. 6 , , M.D. 7 , 8 , , Ph.D. 9 , , M.D. 7 , , M.D. 10 , 11 , , Ph.D. 11 , 12 , , Ph.D. 9 , , Ph.D. 9 , , Ph.D. 13 , , Ph.D. 9 , , M.D., Ph.D. 14 , , Ph.D. 15 , , Ph.D. 16 , , M.D., Ph.D. 15 , , Ph.D. 7 , , M.D. 10 , , M.D. 16 , , M.D. 1 , 2 , for the IBIS Network
      Nature
      autism, brain, neuroimaging, development

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          Summary

          Brain enlargement has been observed in children with Autism Spectrum Disorder (ASD), but the timing of this phenomenon and its relationship to the appearance of behavioral symptoms is unknown. Retrospective head circumference and longitudinal brain volume studies of 2 year olds followed up at age 4 years, have provided evidence that increased brain volume may emerge early in development. 1, 2 Studies of infants at high familial risk for autism can provide insight into the early development of autism and have found that characteristic social deficits in ASD emerge during the latter part of the first and in the second year of life 3, 4 . These observations suggest that prospective brain imaging studies of infants at high familial risk for ASD might identify early post-natal changes in brain volume occurring before the emergence of an ASD diagnosis. In this prospective neuroimaging study of 106 infants at high familial risk of ASD and 42 low-risk infants, we show that cortical surface area hyper-expansion between 6-12 months of age precedes brain volume overgrowth observed between 12-24 months in the 15 high-risk infants diagnosed with autism at 24 months. Brain volume overgrowth was linked to the emergence and severity of autistic social deficits. A deep learning algorithm primarily using surface area information from brain MRI at 6 and 12 months of age predicted the diagnosis of autism in individual high-risk children at 24 months (with a positive predictive value of 81%, sensitivity of 88%). These findings demonstrate that early brain changes unfold during the period in which autistic behaviors are first emerging.

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          Most cited references53

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          Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

          An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
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            Reducing the dimensionality of data with neural networks.

            High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such "autoencoder" networks, but this works well only if the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.
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              Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders

              Describes the Autism Diagnostic Interview-Revised (ADI-R), a revision of the Autism Diagnostic Interview, a semistructured, investigator-based interview for caregivers of children and adults for whom autism or pervasive developmental disorders is a possible diagnosis. The revised interview has been reorganized, shortened, modified to be appropriate for children with mental ages from about 18 months into adulthood and linked to ICD-10 and DSM-IV criteria. Psychometric data are presented for a sample of preschool children.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                13 January 2017
                15 February 2017
                15 August 2017
                : 542
                : 7641
                : 348-351
                Affiliations
                [1 ]Dept. of Psychiatry, University of North Carolina
                [2 ]Carolina Institute for Developmental Disabilities
                [3 ]College of Charleston, University of Minnesota
                [4 ]Dept. of Educational Psychology, University of Minnesota
                [5 ]Institute of Child Development, University of Minnesota
                [6 ]Dept. of Biostatistics, University of North Carolina
                [7 ]Dept. of Psychiatry, Washington University School of Medicine
                [8 ]Dept. of Radiology, University of Washington
                [9 ]Center on Human Development and Disability, University of Washington
                [10 ]Dept. of Speech and Hearing Sciences, University of Washington
                [11 ]Montreal Neurological Institute, McGill University
                [12 ]Tandon School of Engineering, New York University
                [13 ]Mallinckrodt Institute of Radiology, Washington University
                [14 ]Center for Autism Research, The Children's Hospital of Philadelphia, and University of Pennsylvania
                [15 ]Dept. of Psychology, Temple University
                [16 ]Dept. of Pediatrics, University of Alberta
                Author notes
                Corresponding Author: Heather Cody Hazlett, CB#3367, Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC 27599-3367, hcody@ 123456med.unc.edu
                [*]

                IBIS Network: The IBIS (Infant Brain Imaging Study) Network is an NIH funded Autism Center of Excellence (HDO55741) and consists of a consortium of 8 Universities in the U.S. and Canada. Clinical Sites: University of North Carolina: J. Piven (IBIS Network PI), H.C. Hazlett, C. Chappell; University of Washington: S. Dager, A. Estes, D. Shaw; Washington University: K. N. Botteron, R. C. McKinstry, J. Constantino, J. Pruett; Children's Hospital of Philadelphia: R. T. Schultz, S. Paterson; University of Alberta: L. Zwaigenbaum; University of Minnesota: J.T. Elison, J.J. Wolff. Data Coordinating Center: Montreal Neurological Institute: A.C. Evans, D.L. Collins, G.B. Pike, V. Fonov, P. Kostopoulos, S. Das. Image Processing Core: New York University: G. Gerig; University of North Carolina: M. Styner. Statistical Analysis Core: University of North Carolina: H. Gu.

                Article
                NIHMS841823
                10.1038/nature21369
                5336143
                28202961
                fad9a0eb-11e8-4275-b6f1-f9ba1927fb12

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                autism,brain,neuroimaging,development
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                autism, brain, neuroimaging, development

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