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      Examining overlap and homogeneity in ASD, ADHD, and OCD: a data-driven, diagnosis-agnostic approach

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          Abstract

          The validity of diagnostic labels of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive compulsive disorder (OCD) is an open question given the mounting evidence that these categories may not correspond to conditions with distinct etiologies, biologies, or phenotypes. The objective of this study was to determine the agreement between existing diagnostic labels and groups discovered based on a data-driven, diagnosis-agnostic approach integrating cortical neuroanatomy and core-domain phenotype features. A machine learning pipeline, called bagged-multiview clustering, was designed to discover homogeneous subgroups by integrating cortical thickness data and measures of core-domain phenotypic features of ASD, ADHD, and OCD. This study was conducted using data from the Province of Ontario Neurodevelopmental Disorders (POND) Network, a multi-center study in Ontario, Canada. Participants ( n = 226) included children between the ages of 6 and 18 with a diagnosis of ASD ( n = 112, median [IQR] age = 11.7[4.8], 21% female), ADHD ( n = 58, median [IQR] age = 10.2[3.3], 14% female), or OCD ( n = 34, median [IQR] age = 12.1[4.2], 38% female), as well as typically developing controls ( n = 22, median [IQR] age = 11.0[3.8], 55% female). The diagnosis-agnostic groups were significantly different than each other in phenotypic characteristics (SCQ: χ 2(9) = 111.21, p < 0.0001; SWAN: χ 2(9) = 142.44, p < 0.0001) as well as cortical thickness in 75 regions of the brain. The analyses revealed disagreement between existing diagnostic labels and the diagnosis-agnostic homogeneous groups (normalized mutual information < 0.20). Our results did not support the validity of existing diagnostic labels of ASD, ADHD, and OCD as distinct entities with respect to phenotype and cortical morphology.

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          Nonparametric estimation of Shannon’s index of diversity when there are unseen species in sample

          Journal of Autism and Developmental Disorders, 30(3), 205-223
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            Distinct genetic influences on cortical surface area and cortical thickness.

            Neuroimaging studies examining the effects of aging and neuropsychiatric disorders on the cerebral cortex have largely been based on measures of cortical volume. Given that cortical volume is a product of thickness and surface area, it is plausible that measures of volume capture at least 2 distinct sets of genetic influences. The present study aims to examine the genetic relationships between measures of cortical surface area and thickness. Participants were men in the Vietnam Era Twin Study of Aging (110 monozygotic pairs and 92 dizygotic pairs). Mean age was 55.8 years (range: 51-59). Bivariate twin analyses were utilized in order to estimate the heritability of cortical surface area and thickness, as well as their degree of genetic overlap. Total cortical surface area and average cortical thickness were both highly heritable (0.89 and 0.81, respectively) but were essentially unrelated genetically (genetic correlation = 0.08). This pattern was similar at the lobar and regional levels of analysis. These results demonstrate that cortical volume measures combine at least 2 distinct sources of genetic influences. We conclude that using volume in a genetically informative study, or as an endophenotype for a disorder, may confound the underlying genetic architecture of brain structure.
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              Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space

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                Author and article information

                Contributors
                +1-416-425-6220 , akushki@hollandbloorview.ca
                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group UK (London )
                2158-3188
                26 November 2019
                26 November 2019
                2019
                : 9
                : 318
                Affiliations
                [1 ]ISNI 0000 0004 0572 4702, GRID grid.414294.e, Autism Research Centre, Bloorview Research Institute, , Holland Bloorview Kids Rehabilitation Hospital, ; Toronto, ON Canada
                [2 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, University of Toronto, Institute of Biomaterial and Biomedical Engineering, ; Toronto, ON Canada
                [3 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Paediatrics, , University of Toronto, ; Toronto, ON Canada
                [4 ]ISNI 0000 0004 0473 9646, GRID grid.42327.30, Mouse Imaging Centre, The Hospital for Sick Children, ; Toronto, ON Canada
                [5 ]ISNI 0000 0004 0572 4702, GRID grid.414294.e, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, ; Toronto, ON Canada
                [6 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Psychiatry, , University of Toronto, ; Toronto, ON Canada
                [7 ]ISNI 0000 0004 0473 9646, GRID grid.42327.30, Department of Psychiatry, , The Hospital for Sick Children, ; Toronto, ON Canada
                [8 ]ISNI 0000 0004 1936 7697, GRID grid.22072.35, Hotchkiss Brain Institute, Departments of Psychiatry & Medical Genetics, , University of Calgary, ; Calgary, AB Canada
                [9 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Program in Neuroscience and Mental Health, The Hospital for Sick Children, Department of Medical Biophysics, , University of Toronto, ; Toronto, Canada
                [10 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, , University of Oxford, ; Oxford, UK
                Author information
                http://orcid.org/0000-0003-1510-8818
                Article
                631
                10.1038/s41398-019-0631-2
                6880188
                31772171
                b2ae29c4-2aea-45a1-9a6d-90370686149e
                © The Author(s) 2019

                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/.

                History
                : 12 March 2019
                : 4 October 2019
                : 20 October 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100008914, Ontario Brain Institute (Institut Ontarien du Cerveau);
                Award ID: IDS-I l-02
                Award ID: IDS-I l-02
                Award ID: IDS-I l-02
                Award ID: IDS-I l-02
                Award ID: IDS-I l-02
                Award ID: IDS-I l-02
                Award ID: IDS-I l-02
                Award ID: IDS-I l-02
                Award ID: IDS-I l-02
                Award Recipient :
                Categories
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                © The Author(s) 2019

                Clinical Psychology & Psychiatry
                adhd,autism spectrum disorders
                Clinical Psychology & Psychiatry
                adhd, autism spectrum disorders

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