# Thought disorder and social cognition in clinical risk states for schizophrenia

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2020 · $676,651

## Abstract

Project Summary
 In an effort to intervene before psychosis onset and prevent morbidity, a major recent focus in
schizophrenia research has been the identification of young people during a putative prodromal period, so as
to develop safe and effective interventions to modify disease course. Over the past decade, studies at
Columbia and elsewhere have evaluated clinical high-risk (CHR) individuals across a wide range of cognitive
processes to try to identify core deficits of schizophrenia evident before psychosis onset. Subthreshold thought
disorder and impaired emotion recognition have emerged as profound deficits that predate, rather than follow,
psychosis onset and thus may be indicators of schizophrenia liability, consistent with studies in other risk
cohorts, including genetic high risk. Further, subthreshold thought disorder and emotion recognition deficit are
significantly correlated, suggesting shared neural substrates in temporoparietal regions.
 This study aims to identify the neural mechanisms that underlie subthreshold thought disorder and
emotion recognition deficit in 125 CHR individuals followed prospectively for psychosis outcome. CHR cohorts
are enriched with early cases of schizophrenia, as 20-25% develop schizophrenia and related psychotic
disorders within 1-2 years. CHR cohorts may be optimal for studying core characteristics of illness as they
otherwise have low-level symptoms, less illness chronicity and minimum exposure to antipsychotics. 25
individuals with schizophrenia and 50 healthy volunteers are included for comparison.
 Subthreshold thought disorder and emotion recognition deficits will be studied across behavioral,
physiological and circuit levels. For thought disorder, we will use automated speech analysis approaches
developed in collaboration with IBM to identify constituent impairments in semantics and syntax, and a listening
task that elicits reliable activation in language circuits. Our automated machine-learning approach to speech
analysis, informed by artificial intelligence, derives the semantic meaning of words and phrases by drawing on
a large corpus of text, similar to how humans assign meaning to what they read or hear. Emotion recognition
will be measured using standard tasks, naturalistic tasks with dynamic face stimuli and parametric face morph
tasks that discriminate between perception and appraisal; task-related BOLD activity will be used to identify
relevant circuits. Associations with basic sensory impairment will be tested, including novel auditory mismatch
negativity paradigms. Resting state functional connectivity (RSFC) methods will be used for circuit-level
analysis of language production and emotion recognition across stages of illness, to determine unique and
shared substrates of these constructs in early schizophrenia. If successful, this proposal will identify neural
targets for remediation of cognitive impairments.

## Key facts

- **NIH application ID:** 9920230
- **Project number:** 5R01MH107558-06
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** CHERYL MARY CORCORAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $676,651
- **Award type:** 5
- **Project period:** 2017-12-19 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9920230

## Citation

> US National Institutes of Health, RePORTER application 9920230, Thought disorder and social cognition in clinical risk states for schizophrenia (5R01MH107558-06). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9920230. Licensed CC0.

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