Colin Hawco1, Robert W Buchanan1, Navona Calarco1, Benoit H Mulsant1, Joseph D Viviano1, Erin W Dickie1, Miklos Argyelan1, James M Gold1, Marco Iacoboni1, Pamela DeRosse1, George Foussias1, Anil K Malhotra1, Aristotle N Voineskos1. 1. Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto (Hawco, Calarco, Mulsant, Viviano, Dickie, Foussias, Voineskos); the Department of Psychiatry, University of Toronto, Toronto (Hawco, Foussias, Mulsant, Voineskos); Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Md. (Buchanan, Gold); Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, N.Y. (Argyelan, DeRosse, Malhotra); Zucker School of Medicine at Hofstra/Northwell, Hempstead, N.Y. (Argyelan, DeRosse, Malhotra); the Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at the University of California, Los Angeles (Iacoboni).
Abstract
OBJECTIVE: Case-control study design and disease heterogeneity may impede biomarker discovery in brain disorders, including serious mental illnesses. To identify biologically and/or behaviorally driven as opposed to diagnostically driven subgroups of individuals, the authors used hierarchical clustering to identify individuals with similar patterns of brain activity during a facial imitate/observe functional MRI task. METHODS: Participants in the Social Processes Initiative in Neurobiology of the Schizophrenia(s) study (N=179; 109 with a schizophrenia spectrum disorder and 70 healthy control participants) underwent MRI scanning at three sites. Hierarchical clustering was used to identify new data-driven groups of participants; differences on social and neurocognitive tests completed outside the scanner were compared among the new groups. RESULTS: Three clusters with distinct patterns of neural activity were found. Cluster membership was not related to diagnosis or scan site. The largest cluster consisted of "typical activators," with activity in the canonical "simulation" circuit. The other clusters represented a "hyperactivating" group and a "deactivating" group. Between-participants Euclidean distances were smaller within clusters than within site or diagnostics groups. The deactivating group had the highest social cognitive and neurocognitive test scores. The hierarchical clustering analysis was repeated on a replication sample (N=108; 32 schizophrenia spectrum disorder, 37 euthymic bipolar disorder, and 39 healthy control participants), which exhibited the same three cluster patterns. CONCLUSIONS: The study findings demonstrate replicable differing patterns of neural activity among individuals during a socio-emotional task, independent of DSM diagnosis or scan site. The findings may provide objective neuroimaging endpoints (biomarkers) for subgroups of individuals in target engagement research aimed at enhancing cognitive performance independent of diagnostic category.
OBJECTIVE: Case-control study design and disease heterogeneity may impede biomarker discovery in brain disorders, including serious mental illnesses. To identify biologically and/or behaviorally driven as opposed to diagnostically driven subgroups of individuals, the authors used hierarchical clustering to identify individuals with similar patterns of brain activity during a facial imitate/observe functional MRI task. METHODS:Participants in the Social Processes Initiative in Neurobiology of the Schizophrenia(s) study (N=179; 109 with a schizophrenia spectrum disorder and 70 healthy control participants) underwent MRI scanning at three sites. Hierarchical clustering was used to identify new data-driven groups of participants; differences on social and neurocognitive tests completed outside the scanner were compared among the new groups. RESULTS: Three clusters with distinct patterns of neural activity were found. Cluster membership was not related to diagnosis or scan site. The largest cluster consisted of "typical activators," with activity in the canonical "simulation" circuit. The other clusters represented a "hyperactivating" group and a "deactivating" group. Between-participants Euclidean distances were smaller within clusters than within site or diagnostics groups. The deactivating group had the highest social cognitive and neurocognitive test scores. The hierarchical clustering analysis was repeated on a replication sample (N=108; 32 schizophrenia spectrum disorder, 37 euthymic bipolar disorder, and 39 healthy control participants), which exhibited the same three cluster patterns. CONCLUSIONS: The study findings demonstrate replicable differing patterns of neural activity among individuals during a socio-emotional task, independent of DSM diagnosis or scan site. The findings may provide objective neuroimaging endpoints (biomarkers) for subgroups of individuals in target engagement research aimed at enhancing cognitive performance independent of diagnostic category.
Entities:
Keywords:
Clustering; Individual Variability; Schizophrenia Spectrum Disorder; Social Cognition; fMRI
Authors: Benoit H Mulsant; Aristotle N Voineskos; Neda Rashidi-Ranjbar; Tarek K Rajji; Colin Hawco; Sanjeev Kumar; Nathan Herrmann; Linda Mah; Alastair J Flint; Corinne E Fischer; Meryl A Butters; Bruce G Pollock; Erin W Dickie; Christopher R Bowie; Matan Soffer Journal: Neuropsychopharmacology Date: 2022-04-11 Impact factor: 7.853
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