# Developing an ecologically valid virtual reality-EEG paradigm for biomarker assessment of procognitive treatment sensitivity in schizophrenia

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $433,646

## Abstract

Project Summary
 After decades of procognitive research in SZ, an FDA approved procognitive drug is yet to be identified.
However, a bottom-up targeted cognitive training (TCT) is found to be effective in improving cognition and
functional outcome in SZ patients. Nonetheless, TCT is time and labor-intensive, expensive, and only
beneficial to about half of the patients. This underscores the need to identify predictive biomarkers of TCT
sensitivity. In a recent report, mismatch negativity (MMN), an electroencephalogram (EEG)- based measure of
early auditory information processing (EAIP) predicted TCT response in treatment-refractory SZ patients,
albeit, with modest power. To this end, an important question is, can we enhance the ability of EAIP
“biomarkers” to predict cognitive and functional gains from TCT in SZ patients?
 EAIP measures, MMN and auditory steady state response (ASSR), are presumably measuring
information processing capacity within complex, real-life environments. In everyday life, complex sound stimuli
are processed in the context in which they are perceived. However, all methods to measure MMN and ASSR
have utilized isolated sound fragments (clicks, tones), removed from any environmental context beyond a
laboratory test chair. It is possible that EEG measures generated using contextually relevant naturalistic sound
stimuli might be better predictors of TCT sensitivity. However, MMN and ASSR measures require millisecond-
level stimulus control within a structured test session – that is not easily achieved in a naturalistic setting.
Virtual reality (VR) technology provides both the naturalistic context and tight experimental control needed to
generate and assess more informative measures of EAIP.
 This application takes the critical step toward developing an ecologically valid VR-based EEG paradigm
to measure EAIP by using naturalistic sound stimuli (e.g. footsteps, jack hammer) presented in familiar VR-
delivered contexts (e.g. walking from point A to B, construction site). The VR-EEG task will be developed in
collaboration with the Computer Sciences Department during months 0-4. The validity, reliability and biomarker
potential of the VR-based MMN and ASSR will be determined in healthy subjects (HS) and SZ patients (N=40,
HS:SZ= 20:20) during project months 5-20. Carefully screened, eligible study participants will complete a
comprehensive neurocognitive and functional assessment, laboratory- and VR- based EEG tasks before and
after a one-hour “sound sweeps” TCT session in a randomized, order-balanced, single test day study. Findings
will test the hypotheses that: 1) VR-based MMN and ASSR are reliable and valid measures of EAIP deficits in
SZ, 2) the VR-based EEG measures will predict auditory perceptual learning after 1-hour of TCT session and
3) VR-based EEG measures will predict cognitive and functional state in SZ. Evidence suggesting ecological
validity of these VR-based biomarkers of EAIP will provide basis for future VR-EEG...

## Key facts

- **NIH application ID:** 10038925
- **Project number:** 1R21MH123707-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Savita G Bhakta
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $433,646
- **Award type:** 1
- **Project period:** 2020-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10038925

## Citation

> US National Institutes of Health, RePORTER application 10038925, Developing an ecologically valid virtual reality-EEG paradigm for biomarker assessment of procognitive treatment sensitivity in schizophrenia (1R21MH123707-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10038925. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*