# Cognition Trajectories in Cognitive Training and Early Intervention Treatment Programs in Schizophrenia

> **NIH NIH R03** · UNIVERSITY OF MINNESOTA · 2020 · $77,000

## Abstract

PROJECT SUMMARY
The Recovery After an Initial Schizophrenia Episode (RAISE) Early Treatment Program (ETP) is an exciting new
evidence-based intervention for schizophrenia spectrum disorders. However, several questions arise from this
study. Why are benefits in Quality of Life seen primarily during the first 6 months of treatment, and only for individuals
whose duration of untreated psychosis is <74 weeks? Does a deeper understanding of treatment trajectories lead to
new insights about prognosis or about novel leverage points to refine and optimize the treatment approach?
The purpose of this project is to answer these questions by performing state-of-the-art computational analyses on data
from RAISE-ETP and from our own cognitive training trial. Our interdisciplinary team of clinical investigators and data
scientists will use data-driven predictive, causal clustering, and causal discovery analyses to examine clinical and
cognitive response patterns in the RAISE-ETP data set. We will then validate the baseline causal model(s), describing
the mechanistic relationship among patients' baseline characteristics derived from the RAISE-ETP data, against our
own early psychosis data set, and explore the effects of cognitive training on key predictive variables identified in the
RAISE-ETP analyses. We seek to understand whether data from our experimental neuroscience-informed cognitive
training studies in recent-onset schizophrenia can suggest ways of enriching the RAISE-ETP approach in order to
optimize outcomes for as many individuals as possible, not just those with a shorter duration of untreated psychosis.
One of our main areas of focus will be cognition, since cognitive deficits predict psychosocial dysfunction even when
symptoms are in remission and are unresponsive to current medications. In an exploratory Causal Graph Analysis
performed on RAISE-ETP data, we find that baseline Cognition Composite performance is causally associated with
Quality of Life (QLS) Total Score at 6 months, which in turn predicts the two-year QLS trajectory. Baseline Cognition
Composite is also related to Positive and Negative Syndrome Scale (PANSS) Total Symptoms score at 6 months, via
an unidentified latent variable linking QLS and PANSS Total Symptoms. Consistent with these causal relationships,
exploratory growth curve modeling in our cognitive training data show that individuals assigned to training demonstrate
a linear improvement in PANSS Total Symptoms 6 months after the intervention, while those control condition subjects
show no such change. Our goal is to examine these findings in depth in order to inform the design of the next generation
of “first episode” treatments.

## Key facts

- **NIH application ID:** 9906914
- **Project number:** 5R03MH117254-02
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Sophia Vinogradov
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $77,000
- **Award type:** 5
- **Project period:** 2019-04-05 → 2021-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9906914

## Citation

> US National Institutes of Health, RePORTER application 9906914, Cognition Trajectories in Cognitive Training and Early Intervention Treatment Programs in Schizophrenia (5R03MH117254-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9906914. Licensed CC0.

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