Brett A. Clementz, Ph.D. - US grants

DESCRIPTION (provided by applicant): The major psychoses (SZ, SAD, BDP), when defined by clinical phenomenology alone, overlap extensively on neurobiological, biomarker, co-morbid, symptomatic, and genetic characteristics. Our field may benefit from transformational re-conceptualizations of disease seen in other areas of medicine when biological variables are considered in disease definitions and identification. This approach in psychiatry will depend on: (i) use of well- defined disease domains, (ii) large samples that capture clinical heterogeneity and support statistical approaches, and (iii) ability to acquire quantifiable laboratory measures t inform re-conceptualization of disease characteristics. The 5-site B-SNIP focus is psychosis, an ideal clinical phenotype for this purpose. B- SNIP1 recruited over 2500 volunteers and performed dense phenotyping across multiple levels of analysis (cognitive, psychophysiological, brain imaging, social and clinical). The overall data described a continuum of phenotypic alterations across the DSM psychosis diagnoses (BDP, SAD, SZ) with little evidence of diagnostic specificity. In an attempt to use these dense phenotypic characteristics to define biologically based subgroups, we re-grouped probands using biomarkers and a multistage multivariate analysis procedure. We identified 3 psychosis Biotypes based on core phenotypic features. Biotypes showed unique differences across external validators that were not used in the initial construction of the categories. B-SNIP2 will replicate and extend B- SNIP1 using enhanced proband number, biomarker panel, and sophistication of multivariate statistical approaches. We will accomplish our goals within the context of two specific aims. SA(1) Construct a 'Psychosis Biomarker Database' (PBD): Recruit 3000 new psychosis probands and 600 healthy volunteers and collect data including clinical, psychosocial, electrophysiological, ocular motor, imaging and blood biomarkers. Core biomarkers (used for Biotype definition) and external validators (used for verifying neurobiological distinctiveness of Biotypes) will be collected as specified. Genetic characteristics of the participants will be obtained in collaboratin with the Broad Institute. SA(2) Contrast and test taxometric approaches to categorizing psychosis: Evaluate the ability of different taxonomic structures to define psychosis subgroups, based on data in the PBD: (i) DSM, (ii) B-SNIP2 biotypes based on clinical variables, (iii) B-SNIP1 Biotypes, (iv) B-SNIP2-generated biotypes based on biomarkers, and (v) B-SNIP2 biotypes based on both clinical variables and biomarkers. Beginning with traditional DSM diagnostic criteria as the taxonomy and testing (i)-(v) we will use linear, quadratic and nonparametric discriminant function analysis applied to external biomarker validators to examine the association between the traditional diagnostic system and the biologically- derived classification (imaging, psychosocial and genetic external validators). We will be able to determine the strongest taxonomic approach based on biological characteristics. We seek a rational classification of psychotic disorders that will be successful in identifying novel disease targets and treatments approaches.

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).

Project Summary Treatment advances in psychosis may be limited by the use of phenomenology-defined diagnoses based on symptomatic outcomes, rather than by neurobiological constructs monitored by quantitative characteristics. The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) uses biomarkers to define psychosis subgroups with the goal of testing the advantages of B-SNIP biomarkers for diagnostic and therapeutic decisions, consistent with principles in the NIMH Strategic Plan (NSP). With >3000 phenotyped psychosis probands, relatives and healthy controls, B-SNIP has a multilevel biomarker library for psychosis and used that library to re-conceptualize psychosis subgroups as biomarker-defined Biotypes (B1, B2, B3), where B1 and B2 are the low cognition/high symptom groups and B3 shows lower symptoms and relatively normal cognition. We replicated Biotypes in a new sample, ?forging a future where measures of an individual?s ? neural and physiological state will form the basis of an increasingly specific and informative diagnosis? (NSP). In this grant we propose that B1, with its low cognition and low cortical activity, will respond uniquely to clozapine, a drug which will generate active cortical attractor networks in B1 to support symptomatic improvement. Clozapine is the most effective antipsychotic drug (APD) with unique clinical efficacy. It is the least used APD because its side effects are serious (neutropenia, myocarditis, seizures) and its administration complex. A predictive biomarker would allow targeting of cases most likely to respond and improve prognosis in psychosis. B-SNIP has shown that clozapine is associated with increases in EEG measures of alpha/theta power, and we identify this increase in time periods without stimulus processing requirements as intrinsic EEG activity (IEA), across all Biotypes. Because B1 cases express low IEA, clozapine?s action to increase EEG power will be normalizing for this psychosis subgroup, with increased cortical attractor states. Because B2 express accentuated IEA, clozapine is associated with more deviant IEA in B2. We propose to test B1 psychosis cases with clozapine vs. risperidone (n=40/group clinical trial completers), over a 6 week cross-titration (to therapeutic plasma levels) and a 9 week stable dose extension, predicting that the B1/clozapine group will respond significantly better, as measured with total PANSS, than the B1/risperidone group and also better than either B2 group. It is our hypothesis that the cortical attractor networks will be normalized and their function increased by the increase in intrinsic EEG activity.