Schizophrenia is a uniquely human disorder with specific effects on the uniquely human capacity of language. Indeed, the gross and subtle language abnormalities of schizophrenia can be seen as fundamental illness components, perhaps even as part of a "biosignature." Bringing modern linguistics knowledge and tools to this disorder is a promising approach. We have formed a unique, inter-disciplinary collaboration (Dr. Compton, a schizophrenia researcher;Dr. Covington, a computational linguist;Dr. Lunden, a linguist specializing in phonetics;Dr. Cleary, a statistician;and Dr. Blanchard, an expert in measuring negative symptoms) to study some of the most perplexing and disabling facets of schizophrenia, the language/speech abnormalities linked closely to disorganization and negative symptoms. We will analyze speech abnormalities in patients with schizophrenia and unaffected controls. Rather than examining a single linguistic parameter, we will assess speech in "syntactic," "semantic," "pragmatic," and "phonetic" domains of linguistics. We will introduce cutting- edge innovation to this area of study by assessing these indices using psycholinguistics software developed by Dr. Covington's group so that our ratings of speech abnormalities will be highly objective and ultra-reliable. Our long-term goal is to develop multivariable models, and new methods for clinical and research settings, based on computational linguistic indices with inherent reliability from automation and proven validity. In this exploratory/developmental study, we will collect detailed symptom ratings from 100 schizophrenia patients, as well as audio-recorded speech samples and neurocognition scores from these patients and 100 controls. This study involves early/conceptual stages of new tools and models that could have a major translational impact. We strive to acquire new knowledge and then put it into action. For example, our new methods could translate into advanced clinical applications (e.g., highly reliable, voice-based monitoring of symptom progression or remission). Furthermore, our new models and methods could be a first step toward promising predictive models (e.g., combinations of factors useful in risk prediction among at-risk youth). These objectives are highly aligned with the NIMH Strategic Plan. Our 4 aims are to: (1) examine syntactic, semantic, and pragmatic linguistic parameters using computer analysis of speech, and assess their relation to disorganized symptoms;(2) examine phonetic linguistic parameters using computerized Fourier spectrum analysis of speech, and assess their relation to negative symptoms;(3) determine the combination of psycholinguistic parameters that best predicts patient versus control status;and (4) determine the combination of psycholinguistic parameters that best predicts disorganization scores and negative symptom scores among patients. Given the rich data we will collect, we will also be able to covary the effects of medication and substance use;examine variation in findings based on neutral v. emotionally laden content and spontaneous v. read speech;assess variance in linguistic measures attributable to cognitive domains;and compare results in first-episode and chronic patients.
Schizophrenia is an etiologically complex, heterogeneous mental disorder-ranking among the top 10 causes of disability worldwide-with those affected contending with troubling symptoms, major psychosocial problems, unparalleled societal stigma, health disparities, diverse comorbidities, and an average lifespan reduction of 25 years. We propose a study that would significantly advance knowledge of fundamental but under-studied components of the illness-speech/language abnormalities-by examining a broad array of linguistic indices using cutting-edge artificial intelligence (computational linguistics, or computrs objectively, quickly, and ultra- reliably analyzing speech). This exploratory/developmental work could have major public health significance in terms of potential for future, high-impact clinical assessment tools that can be practicably used in routine practice settings, as well as future predictive models for those at high risk of developing schizophrenia.