Integration of brain and behavior measures for identification of data-driven groups cutting across children with ASD, ADHD, or OCD


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 was used 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. 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 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. Future work is needed to build on this early attempt through replication of the current findings in independent samples and testing longitudinally for prognostic value.

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Fig. 1: Relative participant similarities, diagnostic label break-down, and behavior scores for data-driven and NDD groups.
Fig. 2: Data-driven group differences for top contributing features across age and IQ as well as their stability across resampling.
Fig. 3: Data-driven and NDD group differences on out-of-model brain and behavior measures.


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We thank the following individuals for research support and data collection: Tara Goodale, M.Sc., Reva Schachter, M.Sc., Mithula Sriskandarajah, B.Sc., Marlena Colasanto, M.Sc., Jennifer Gomez, M.A., and Laura Park, M.Sc, from The Hospital for Sick Children; Susan Day Fragiadakis, M.A., Naomi Peleg, M.Sc., and Leanne Ristic, B.A., from Holland Bloorview Kids Rehabilitation Hospital; Richa Mehta, B.A., Christina Sommerdyk, M.Sc., from the Lawson Health Research Institute; Carolyn Russell, B.Sc., Alessia Greco, M.A., Mike Chalupka, B.A., B.Sc., Christina Chrysler, B.A., Irene O’Connor, M.Ed. Psych., from McMaster Children’s Hospital. We thank Margot J. Taylor for her review of a draft version of the manuscript. We thank Jason P. Lerch and Evdokia Anagnostou for their input into the analyses undertaken and manuscript preparation. We thank Hajer Nakua for her work on imaging quality control.

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Conceptualization: SHA, ANV, GRJ; Methodology: GRJ, EWD, AG, LE, CH, NF, LS; Formal Analysis: GRJ, CH, NF; Resources: SHA, ANV; Drafting the manuscript: GRJ, SHA, ANV; Manuscript revisions and finalization: GRJ, ANV, CH, LS, NJF, EWD, M-CL, PS, RS, JC, PDA, AG, LE, SHA; Visualizations: GRJ; Supervision: SHA, ANV.

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Correspondence to Stephanie H. Ameis.

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Jacobs, G.R., Voineskos, A.N., Hawco, C. et al. Integration of brain and behavior measures for identification of data-driven groups cutting across children with ASD, ADHD, or OCD. Neuropsychopharmacol. (2020).

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