Construct validity for computational linguistic metrics in individuals at clinical risk for psychosis: associations with clinical ratings

• Type of Material: Article
• Authors: Zarina R Bilgrami, Icahn School of Medicine atDepartment of Psychology, Emory University; Cansu Sarac, Icahn School of Medicine at Mount Sinai New York, NY, USA; Agrima Srivastava, Icahn School of Medicine at Mount Sinai New York, NY, USA; Shaynna N Herrera, Icahn School of Medicine at Mount Sinai New York, NY, USA; Matilda Azis, Department of Psychology, Emory University, Atlanta, GA, USA; Shalaila S Haas, Icahn School of Medicine at Mount Sinai New York, NY, USA; Riaz B Shaik, Icahn School of Medicine at Mount Sinai New York, NY, USA; Muhammad A Parvaz, Icahn School of Medicine at Mount Sinai New York, NY, USA; Vijay A Mittal, Northwestern University, Department of Psychology, Evanston, IL, USA; Guillermo Cecchi, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA; Cheryl M Corcoran, Icahn School of Medicine at Mount Sinai New York, NY, USA
• Language: English
• Date: 2022-07-01
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: Elsevier B.V.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.schres.2022.01.019
• Title of Journal or Parent Work: SCHIZOPHRENIA RESEARCH
• Volume: 245
• Start Page: 90
• End Page: 96
• Grant/Funding Information: The funding source had no role in study design; collection, analysis and interpretation of data; writing of the report; or the decision to submit the article for publication.
• Research Categories: Health Sciences, Medicine and Surgery

Abstract

Language deficits are prevalent in psychotic illness, including its risk states, and are related to marked impairment in functioning. It is therefore important to characterize language impairment in the psychosis spectrum in order to develop potential preventive interventions. Natural language processing (NLP) metrics of semantic coherence and syntactic complexity have been used to discriminate schizophrenia patients from healthy controls (HC) and predict psychosis onset in individuals at clinical high-risk (CHR) for psychosis. To date, no studies have yet examined the construct validity of key NLP features with respect to clinical ratings of thought disorder in a CHR cohort. Herein we test the association of key NLP metrics of coherence and complexity with ratings of positive and negative thought disorder, respectively, in 60 CHR individuals, using Andreasen’s Scale of Assessment of Thought, Language and Communication (TLC) Scale to measure of positive and negative thought disorder. As hypothesized, in CHR individuals, the NLP metric of semantic coherence was significantly correlated with positive thought disorder severity and the NLP metrics of complexity (sentence length and determiner use) were correlated with negative thought disorder severity. The finding of construct validity supports the premise that NLP analytics, at least in respect to core features of reduction of coherence and complexity, are capturing clinically relevant language disturbances in risk states for psychosis. Further psychometric study is required, in respect to reliability and other forms of validity.