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

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2021 · $328,000

## Abstract

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% accu...

## Key facts

- **NIH application ID:** 10299189
- **Project number:** 1R01MH124805-01A1
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** ROBERT D GIBBONS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $328,000
- **Award type:** 1
- **Project period:** 2021-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10299189

## Citation

> US National Institutes of Health, RePORTER application 10299189, 2/2 B-SNIP: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT) - Resubmission - 1 (1R01MH124805-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10299189. Licensed CC0.

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