# Quantifying oxytocin effects on vocal expression in schizophrenia

> **NIH VA IK1** · VETERANS AFFAIRS MED CTR SAN FRANCISCO · 2021 · —

## Abstract

Schizophrenia is a devastating illness associated with lifelong disability and high health care costs that
disproportionately impacts veterans. Negative symptoms, a set of volitional and expressive deficits, are major
contributors to impaired functioning. These deficits are poorly understood and difficult to monitor, in part due to
a lack of effective measurement tools. Negative symptoms are typically measured using interview-based
clinical rating scales, which are imprecise, costly to administer, and rely on behavior observed in constrained
laboratory and clinical environments. Speech is a key indicator of clinical status and an easily collected
resource that can be leveraged to address this gap. Abnormal speech is a hallmark of schizophrenia that
reflects expressive deficits: patients tend to talk less and pause more while talking (i.e. alogia) and have
decreased musicality and emotion in their voice (i.e. blunted vocal affect). Advances in automated analytic
methods and mobile device capability provide an opportunity to dramatically improve quantification of speech
abnormalities with unprecedented efficiency. Automated analysis of veterans’ speech, combined with remote
speech data collection using mobile devices, can enable precise, frequent, and cost-effective measurement of
negative symptoms across laboratory, clinical, and real-world settings. The ability to obtain rich, quantitative
characterizations of negative symptoms at the individual level will serve to elucidate pathophysiology of
specific deficits and transform our ability to monitor veterans’ clinical status, thus impacting both research and
clinical care. This CDA-1 leverages already-collected laboratory data and adds novel mobile data collection
methods to Dr. Josh Woolley’s Merit-funded clinical trial to generate preliminary data on the clinical relevance
and feasibility of using automated methods to measure speech abnormalities in veterans with schizophrenia.
The program aims to: (1) investigate how automatically quantified speech abnormalities relate to gold standard
clinical ratings of negative symptoms and functioning in people with schizophrenia (n=50); (2) examine the
potential of oxytocin (OT)—a candidate treatment for expressive deficits—to improve speech abnormalities in
men with schizophrenia (n=30) who have already completed a randomized, placebo-controlled, cross-over
trial; (3) pilot the collection of speech data (both recorded audio samples and passively-extracted vocal signals)
outside the laboratory via mobile devices in veterans with schizophrenia (n=20); and (4) explore the links
between functional neural connectivity, speech abnormalities, and clinically rated negative symptoms in
veterans with schizophrenia (n=20) who will complete neuroimaging as part of the Merit trial. The training plan
will focus on developing critical quantitative and logistical skills; specifically: (1) automated speech analysis
using an established analytic approach; (2) remote speech ...

## Key facts

- **NIH application ID:** 10197770
- **Project number:** 5IK1CX002092-02
- **Recipient organization:** VETERANS AFFAIRS MED CTR SAN FRANCISCO
- **Principal Investigator:** Ellen R Bradley
- **Activity code:** IK1 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2021
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2020-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10197770

## Citation

> US National Institutes of Health, RePORTER application 10197770, Quantifying oxytocin effects on vocal expression in schizophrenia (5IK1CX002092-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10197770. Licensed CC0.

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