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      Functional Connectome Organization Predicts Conversion to Psychosis in Clinical High-Risk Youth from the SHARP Program

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          Abstract

          The emergence of prodromal symptoms of schizophrenia and their evolution into overt psychosis may stem from an aberrant functional reorganization of the brain during adolescence. To examine whether abnormalities in connectome organization precede psychosis onset, we performed a functional connectome analysis in a large cohort of medication-naïve youth at risk for psychosis from the Shanghai At Risk for Psychosis (SHARP) study. The SHARP program is a longitudinal study of adolescents and young adults at Clinical High Risk (CHR) for psychosis, conducted at the Shanghai Mental Health Center in collaboration with neuroimaging laboratories at Harvard and MIT. Our study involved a total of 251 subjects, including 158 CHRs and 93 age-, sex-, and education-matched healthy controls. During one-year follow-up, 23 CHRs developed psychosis. CHRs who would go on to develop psychosis were found to show abnormal modular connectome organization at baseline, while CHR non-converters did not. In all CHRs, abnormal modular connectome organization at baseline was associated with a three-fold conversion rate. A region-specific analysis showed that brain regions implicated in early-course schizophrenia, including superior temporal gyrus and anterior cingulate cortex, were most abnormal in terms of modular assignment. Our results show that functional changes in brain network organization precede the onset of psychosis and may drive psychosis development in at-risk youth.

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          Most cited references44

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          Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks.

          Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.
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            Is Open Access

            Fast unfolding of communities in large networks

            We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2.6 million customers and by analyzing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad-hoc modular networks. .
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              Rethinking schizophrenia.

              How will we view schizophrenia in 2030? Schizophrenia today is a chronic, frequently disabling mental disorder that affects about one per cent of the world's population. After a century of studying schizophrenia, the cause of the disorder remains unknown. Treatments, especially pharmacological treatments, have been in wide use for nearly half a century, yet there is little evidence that these treatments have substantially improved outcomes for most people with schizophrenia. These current unsatisfactory outcomes may change as we approach schizophrenia as a neurodevelopmental disorder with psychosis as a late, potentially preventable stage of the illness. This 'rethinking' of schizophrenia as a neurodevelopmental disorder, which is profoundly different from the way we have seen this illness for the past century, yields new hope for prevention and cure over the next two decades.
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                Author and article information

                Journal
                9607835
                20545
                Mol Psychiatry
                Mol. Psychiatry
                Molecular psychiatry
                1359-4184
                1476-5578
                15 October 2018
                08 November 2018
                26 October 2019
                : 10.1038/s41380-018-0288-x
                Affiliations
                [1 ]Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
                [2 ]McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
                [3 ]Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
                [4 ]Department of Linguistics and Cognitive Science, University of Delaware, Newark, DE, USA
                [5 ]Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
                [6 ]Florida A&M University, Department of Psychology, Tallahassee, FL, USA
                [7 ]Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital, Boston MA, USA
                [8 ]Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, USA
                [9 ]Research and Development, VA Boston Healthcare System, Brockton Division, Brockton, MA, USA
                [10 ]Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
                [11 ]Poitras Center for Affective Disorders, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
                [] Dr. Larry Seidman passed away on September 7, 2017 and Dr. Robert McCarley passed away on May 27, 2017. Professors Seidman and McCarley were two of the initiators and principal investigators of the Shanghai At Risk for Psychosis (SHARP) study.
                Author notes
                Correspondence to: Guusje Collin, Beth Israel Deaconess Medical Center, Department of Psychiatry, Massachusetts Mental Health Center, 75 Fenwood Road, Boston, MA 02115, USA; Phone: +1 (617) 626 9300; gcollin@ 123456bidmc.harvard.edu. JiJun Wang, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, China. Phone: +86 (21) 3477 3065. jijunwang27@ 123456163.com.
                Article
                NIHMS1509233
                10.1038/s41380-018-0288-x
                6813871
                30410064
                4fe981b5-c35d-4d8d-8a14-20574abbeed9

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                Molecular medicine
                Molecular medicine

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