1/2: B-SNIP: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT)

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Clinical phenomenology alone neither (i) captures biologically based disease entities, nor (ii) allows for individualized treatment prescriptions based on neurobiology. The B-SNIP consortium showed and replicated that schizophrenia, schizoaffective, and bipolar disorder with psychosis lack neurobiological distinctiveness. B- SNIP transitioned to subgrouping psychosis cases based on biomarker homology. We produced and replicated biologically homologous psychosis Biotypes (BT1, BT2, BT3) that may assist treatment targeting for psychosis. This twelve-month project will develop a time and resource efficient algorithm for deriving B-SNIP Biotypes that can be implemented in even under-resourced environments. Like in laboratory medicine, the procedure (ADEPT) will be stepwise (clinical evaluation, then cognition, then electrophysiology) to yield Biotypes for which specific treatments can be either implemented (established interventions) or evaluated (novel treatment development). Aim 1: B-SNIP Biotypes currently require specialized equipment for laboratory testing, and multiple tests with statistical integration across multiple scores. Instead, we will determine the best individual measures that yield the most efficient and highest probability Biotype memberships. ADEPT will be adaptive both within (clinical, cognitive, electrophysiological) and across the domains (clinical features inform selection of cognitive tests which inform selection of electrophysiological tests). At each stage, ADEPT will produce a Biotype classification and confidence. This will allow for Biotype determination in a proportion of cases even when laboratory testing resources are limited. Aim 2: The first contact in medical evaluation involves clinical characterization. Clinical features alone will yield Biotype discriminations sufficient for treatment targeting in a small but significant subset of patients (»15%, mostly BT3). Aim 3: Cognition tests are the least technically demanding laboratory assessments, and are powerful discriminators of Biotypes. B-SNIP uses BACS, Stop Signal (SST), and antisaccades to assess cognition. Addition of cognition to clinical features will yield »80% accuracy for identifying BT3s and »40% of all cases (mostly BT2, although BT1 and BT2 are difficulty to differentiate without electrophysiology). Patients will receive different cognitive tests based on the adaptive algorithm (e.g., SST may be superior for Biotype determination in some cases). The adaptive approach preserves classification precision while reducing clinician and patient burden. Aim 4: The most important Biotype differentiating electrophysiology features are low neural response to salient stimuli (BT1) and exuberant nonspecific neural activity (BT2). We used multiple complex electrophysiology measures, but we will identify tests and measures that yield the most efficient Biotype differentiation. Addition of electrophysiology to clinical and cognition information will yield 90-95% accuracy for identifying Biotypes for all cases. Again, for a given patient, we will adaptively select the specific electrophysiological measures to maximize classification accuracy for that patient (e.g., P300 may be superior for Biotype determination in some cases).