The diagnostic system for neuropsychiatric conditions embodied in the Diagnostic and Statistical Manual of Psychiatric Disorders (DSM) is based on clusters of symptoms rather than on underlying etiology or pathophysiology. The establishment of reliable diagnoses was a critical step in the advancement of psychiatric science three decades ago, but now it holds the field back by concealing relationships between brain biology and individual patients'symptoms - relationships that are obscure under the best of circumstances. This realization motivates a search for an alternative, brain-based diagnostic system, in the form of the NIMH's Research Domain Criteria (RDoC) initiative. The development of such an alternative diagnostic framework is in its infancy, and new strategies are needed for the rational categorization of pathophysiological states. We have successfully used data-driven analysis of functional connectivity data, derived from functional neuroimaging of the brain at rest. This approach has revealed neural dysconnectivity across several neuropsychiatric conditions. We will apply these data-driven approaches, in conjunction with leading machine learning algorithms, to quantify dysconnectivity patterns across and within major DSM disorders. We have assembled a dataset of 707 resting-state scans, performed on state-of-the-art 3T scanners and passing rigorous quality control standards, comprising five major DSM diagnoses: schizophrenia, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, and post-traumatic stress disorder, with matched controls for each. Accompanying symptom assessments were administered by highly skilled personnel. This large hybrid dataset permits an unprecedented cross-diagnostic, data-driven search for shared or distinct dysconnectivity across diagnoses. Specifically, we will employ a powerful multi-tiered analytic approach using: fully data-driven connectivity analysis, focusing on networks defined a priori by work in healthy subjects, and a seed-based approach focused on circuits associated with the constituent DSM diagnoses. We hypothesize several possible outcomes. First, patient groups derived from the data-driven connectivity analyses may indeed map onto symptom-based DSM diagnoses. This would be a validation of a symptom- focused nosology, at least across these conditions. Second, data-driven analysis may identify new categories that cut across DSM diagnoses. Third, results may follow continua of dysconnectivity, such as those proposed by the RDoC framework. A more complex outcome that blends these patterns is also probable. Finally, emergent patterns will be correlated against symptom measures, within and across disorders. Irrespective of the ultimate pattern, results of this project will critically inform ongoing effort to refine a diagnostic scheme for psychiatric disorders that is firmly grounded in their pathophysiology. Furthermore, the methodology will be applicable to other datasets. We anticipate that this approach will provide a key pillar to the development of a brain-based understanding of the heterogeneity of psychiatric disease.
Current psychiatric nosology, embodied in the Diagnostic and Statistical Manual of Psychiatric Disorders (DSM), is based on symptom clusters rather than underlying etiology or pathophysiology, which fundamentally limits attempts to develop novel rationally guided and biologically informed interventions. We propose to use neuroimaging measures of functional brain connectivity from multiple DSM disorders to identify fully data- driven patterns of dysconnectivity across and within existing diagnostic entities. The identified patterns will reveal abnormalities in neural network function independent of DSM diagnoses and will critically inform new brain-based diagnostic proposals, such as that envisioned by the NIMH's RDoC initiative.
