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      A small number of abnormal brain connections predicts adult autism spectrum disorder

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          Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.


          Autism spectrum disorder (ASD) is manifested by subtle but significant changes in the brain. Here, Yahata and colleagues devise a novel machine learning algorithm and develop a reliable ASD classifier based on brain functional connectivity, with which they quantitatively measure neuroimaging dimensions between ASD and other mental disorders.

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          Most cited references 40

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          Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization.

          Resting state functional connectivity MRI (fcMRI) is widely used to investigate brain networks that exhibit correlated fluctuations. While fcMRI does not provide direct measurement of anatomic connectivity, accumulating evidence suggests it is sufficiently constrained by anatomy to allow the architecture of distinct brain systems to be characterized. fcMRI is particularly useful for characterizing large-scale systems that span distributed areas (e.g., polysynaptic cortical pathways, cerebro-cerebellar circuits, cortical-thalamic circuits) and has complementary strengths when contrasted with the other major tool available for human connectomics-high angular resolution diffusion imaging (HARDI). We review what is known about fcMRI and then explore fcMRI data reliability, effects of preprocessing, analysis procedures, and effects of different acquisition parameters across six studies (n = 98) to provide recommendations for optimization. Run length (2-12 min), run structure (1 12-min run or 2 6-min runs), temporal resolution (2.5 or 5.0 s), spatial resolution (2 or 3 mm), and the task (fixation, eyes closed rest, eyes open rest, continuous word-classification) were varied. Results revealed moderate to high test-retest reliability. Run structure, temporal resolution, and spatial resolution minimally influenced fcMRI results while fixation and eyes open rest yielded stronger correlations as contrasted to other task conditions. Commonly used preprocessing steps involving regression of nuisance signals minimized nonspecific (noise) correlations including those associated with respiration. The most surprising finding was that estimates of correlation strengths stabilized with acquisition times as brief as 5 min. The brevity and robustness of fcMRI positions it as a powerful tool for large-scale explorations of genetic influences on brain architecture. We conclude by discussing the strengths and limitations of fcMRI and how it can be combined with HARDI techniques to support the emerging field of human connectomics.
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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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              A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis.

              We present a penalized matrix decomposition (PMD), a new framework for computing a rank-K approximation for a matrix. We approximate the matrix X as circumflexX = sigma(k=1)(K) d(k)u(k)v(k)(T), where d(k), u(k), and v(k) minimize the squared Frobenius norm of X - circumflexX, subject to penalties on u(k) and v(k). This results in a regularized version of the singular value decomposition. Of particular interest is the use of L(1)-penalties on u(k) and v(k), which yields a decomposition of X using sparse vectors. We show that when the PMD is applied using an L(1)-penalty on v(k) but not on u(k), a method for sparse principal components results. In fact, this yields an efficient algorithm for the "SCoTLASS" proposal (Jolliffe and others 2003) for obtaining sparse principal components. This method is demonstrated on a publicly available gene expression data set. We also establish connections between the SCoTLASS method for sparse principal component analysis and the method of Zou and others (2006). In addition, we show that when the PMD is applied to a cross-products matrix, it results in a method for penalized canonical correlation analysis (CCA). We apply this penalized CCA method to simulated data and to a genomic data set consisting of gene expression and DNA copy number measurements on the same set of samples.

                Author and article information

                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                14 April 2016
                : 7
                [1 ]Department of Youth Mental Health, Graduate School of Medicine, The University of Tokyo , Tokyo 113-0033, Japan
                [2 ]Diagnostic Imaging Program, Molecular Imaging Center, National Institute of Radiological Sciences , Chiba 263-8555, Japan
                [3 ]Department of Decoded Neurofeedback, ATR Brain Information Communication Research Laboratory Group , Kyoto 619-0288, Japan
                [4 ]Department of Brain Robot Interface, ATR Brain Information Communication Research Laboratory Group , Kyoto 619-0288, Japan
                [5 ]Medical Institute of Developmental Disabilities Research, Showa University Karasuyama Hospital , Tokyo 157-8577, Japan
                [6 ]Department of Language Sciences, Tokyo Metropolitan University , Tokyo 192-0397, Japan
                [7 ]Department of Cognitive, Linguistic and Psychological Sciences, Brown University , Rhode Island 02912, USA
                [8 ]Department of Child Neuropsychiatry, Graduate School of Medicine, The University of Tokyo , Tokyo 113-0033, Japan
                [9 ]Disability Services Office, The University of Tokyo , Tokyo 113-0033, Japan
                [10 ]Child Mental Health-Care Center, Fukushima University , Fukushima 960-1296, Japan
                [11 ]Institute of Cognitive Neuroscience, University College London , London WC1N 3AZ, UK
                [12 ]Department of Psychology, Graduate School of Humanities and Sociology, The University of Tokyo , Tokyo 113-0033, Japan
                [13 ]School of Social and Behavioral Sciences, Arizona State University , Phoenix, Arizona 85306, USA
                [14 ]Department of Psychiatry, Kyoto University Graduate School of Medicine , Kyoto 606-8507, Japan
                [15 ]Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University , Hiroshima 734-8553, Japan
                [16 ]Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo , Tokyo 113-0033, Japan
                Author notes

                These authors contributed equally to this work

                Copyright © 2016, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

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