The Research Domain Criteria (RDoC) matrix delineates general constructs, that reflect basic dimensions of human behavioral functioning that can range from normal to abnormal. The RDoC matrix organizes these constructs by domains (e.g., positive valence and social processing systems) and units of analysis (i.e., from genes, to molecules, cells, circuits, physiology, behavior, self-report, paradigms) such that they can be systematically studied at multiple levels of analysis. Most clinical research studies, to date, have employed standardized symptom assessments, which are often disorder specific and not directly linked to RDoC constructs. In schizophrenia (SZ), negative symptom domains, including avolition, anhedonia, asociality, alogia, and blunted affect (5 factor model), have been studied in some detail. Recently a theoretical mapping between negative symptom domains and RDoC constructs linked avolition, anhedonia, and avolition to positive valence system, and alogia and flat affect to the social processes system. However, the proposed mappings between behavior (negative symptom domains) and brain structures/circuitry have not been tested or validated; either in SZ, or in other neuropsychiatric illnesses such as bipolar disorder (BD) or major depressive disorder (MDD). Earlier work suggested a more parsimonious 2-factor model of negative symptoms, in which avolition, anhedonia, and asociality were linked to a motivation and pleasure (MAP) factor, and and blunted affect andalogia linked to an expressive (EXP) factor. Of note, with the exception of asociality, these factors appear to map onto positive valence and social processes systems in the RDoC matrix; lending additional support to the proposed RDoC matrix structure related to negative symptoms. Mappings between different interpretations of negative symptom domains (e.g., 5-factor and 2-factor models) and brain structures/circuitry have also not been conducted. Leveraging the worldwide collaborative ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) consortium and the COINSTAC (Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation) computational platform, this proposal will combine neuroimaging and clinical measures of negative symptoms across schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD), to validate and extend the RDoC matrix representation of negative symptom domains in major mental illness. We extract joint multimodal features for each separable (sub)construct, evaluate them for their relationship with the behavior, and then use them in a subsequent cross-validation analysis. Subsequently, we evaluate their single subject prediction power. Through these powerful computational methods, we will map structural, diffusion tensor imaging, and resting state functional magnetic resonance imaging measures of brain structures/circuitry to negative symptom behavioral measures. Successful completion of this proposal?s aims will identify distinct and overlapping neural circuits associated with negative symptom domains, will test integrative models of functioning, and identify dysregulation in psychopathology-related mechanisms that cut across traditional diagnostic boundaries.

Public Health Relevance

This study is an unprecedented effort that leverages multiple worldwide working groups along with machine learning via a sophisticated decentralized analysis framework. The study findings will validate and extend the Research Domain Criteria (RDoC) matrix framework that links negative symptom domains (behavior) -via positive valence and social processing systems and their subconstructs- to brain structures/circuitry (physiology). The findings will yield novel classification approaches for negative symptom severity, may identify novel treatment targets (circuitry), and may yield classifications to stratify patients to treatment conditions.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Ferrante, Michele
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Georgia State University
Schools of Arts and Sciences
United States
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