PT - JOURNAL ARTICLE AU - Grace R. Jacobs AU - Aristotle N. Voineskos AU - Colin Hawco AU - Laura Stefanik AU - Natalie J. Forde AU - Erin W. Dickie AU - Meng-Chuan Lai AU - Peter Szatmari AU - Russell Schachar AU - Jennifer Crosbie AU - Paul D. Arnold AU - Anna Goldenberg AU - Lauren Erdman AU - Jason P. Lerch AU - Evdokia Anagnostou AU - Stephanie H. Ameis TI - Integration of Brain and Behavior Measures for Identification of Data-Driven Groups Cutting Across Children with ASD, ADHD, or OCD AID - 10.1101/2020.02.11.944744 DP - 2020 Jan 01 TA - bioRxiv PG - 2020.02.11.944744 4099 - http://biorxiv.org/content/early/2020/02/12/2020.02.11.944744.short 4100 - http://biorxiv.org/content/early/2020/02/12/2020.02.11.944744.full AB - Autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD) are clinically and biologically heterogeneous neurodevelopmental disorders (NDDs). The objective of the present study was to integrate brain imaging and behavioral measures to identify new brain-behavior subgroups cutting across these disorders. A subset of the data from the Province of Ontario Neurodevelopmental Disorder (POND) Network including participants with different NDDs (aged 6-16 years) that underwent cross-sectional T1-weighted and diffusion-weighted magnetic resonance imaging (MRI) scanning on the same 3T scanner, and behavioral/cognitive assessments was used. Similarity Network Fusion was applied to integrate cortical thickness, subcortical volume, white matter fractional anisotropy (FA), and behavioral measures in 176 children with ASD, ADHD or OCD with complete data that passed quality control. Normalized mutual information (NMI) was used to determine top contributing model features. Bootstrapping, out-of-model outcome measures and supervised machine learning were each used to examine stability and evaluate the new groups. Cortical thickness in socio-emotional and attention/executive networks and inattention symptoms comprised the top ten features driving participant similarity and differences between four transdiagnostic groups. Subcortical volumes (pallidum, nucleus accumbens, thalamus) were also different among groups, although white matter FA showed limited differences. Features driving participant similarity remained stable across resampling, and the new groups showed significantly different scores on everyday adaptive functioning. Our findings open the possibility of studying new data-driven groups that represent children with NDDs more similar to each other than others within their own diagnostic group. Such new groups can be evaluated longitudinally for prognostic utility and could be stratified for clinical trials targeted toward each group’s unique brain and behavioral profiles.