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Brain-Behavior Participant Similarity Networks Among Youth and Emerging Adults with Schizophrenia Spectrum, Autism Spectrum, or Bipolar Disorder and Matched Controls

Institution:
1Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
2Department of Computer Science, University of Toronto, Toronto, ON, Canada.
3Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
4Schulich School of Medicine & Dentistry, The University of Western Ontario, London, ON, Canada.
Publication Date:
Apr-2018
Journal:
Neuropsychopharmacology
Volume Number:
43
Issue Number:
5
Pages:
1180-8
Citation:
Neuropsychopharmacology. 2018 Apr;43(5):1180-8.
PubMed ID:
29105664
PMCID:
PMC5854811
Appears in Collections:
NAC, NCIGT, SPL
Sponsors:
P41 EB015898/EB/NIBIB NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
U01 CA199459/CA/NCI NIH HHS/United States
Generated Citation:
Stefanik L., Erdman L., Ameis S.H., Foussias G., Mulsant B.H., Behdinan T., Goldenberg A., O'Donnell L.J., Voineskos A.N. Brain-Behavior Participant Similarity Networks Among Youth and Emerging Adults with Schizophrenia Spectrum, Autism Spectrum, or Bipolar Disorder and Matched Controls. Neuropsychopharmacology. 2018 Apr;43(5):1180-8. PMID: 29105664. PMCID: PMC5854811.
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There is considerable heterogeneity in social cognitive and neurocognitive performance among people with schizophrenia spectrum disorders (SSD), autism spectrum disorders (ASD), bipolar disorder (BD), and healthy individuals. This study used Similarity Network Fusion (SNF), a novel data-driven approach, to identify participant similarity networks based on relationships among demographic, brain imaging, and behavioral data. T1-weighted and diffusion-weighted magnetic resonance images were obtained for 174 adolescents and young adults (aged 16-35 years) with an SSD (n=51), an ASD without intellectual disability (n=38), euthymic BD (n=34), and healthy controls (n=51). A battery of social cognitive and neurocognitive tasks were administered. Data integration, cluster determination, and biological group formation were then obtained using SNF. We identified four new groups of individuals, each with distinct neural circuit-cognitive profiles. The most influential variables driving the formation of the new groups were robustly reliable across embedded resampling techniques. The data-driven groups showed considerably greater differentiation on key social and neurocognitive circuit nodes than groups generated by diagnostic analyses or dimensional social cognitive analyses. The data-driven groups were validated through functional outcome and brain network property measures not included in the SNF model. Cutting across diagnostic boundaries, our approach can effectively identify new groups of people based on a profile of neuroimaging and behavioral data. Our findings bring us closer to disease subtyping that can be leveraged toward the targeting of specific neural circuitry among participant subgroups to ameliorate social cognitive and neurocognitive deficits.