Linking speech patterns to brain structure in affective and psychotic disorders: an integrative natural language processing approach

Abstract

Language disturbances are central features of serious mental illnesses, yet traditional clinical assessments often rely on subjective evaluation that may overlook subtle speech anomalies. This study employs natural language processing (NLP) to objectively analyze spontaneous speech in a transdiagnostic sample comprising individuals with affective (n = 119 Major Depressive Disorder, n = 27 Bipolar Disorder) and psychotic disorders (n = 37 Schizoaffective Disorder, n = 11 Schizophrenia), as well as healthy controls (n = 178). Participants provided approximately 12 min of speech elicited via four pictures from the Thematic Apperception Test, which were transcribed and analyzed for semantic and syntactic features. Explorative factor analysis identified three latent linguistic dimensions: (1) Syntactic Complexity, (2) Lexical Diversity and Fluency, and (3) Narrow Thematic Focus. These dimensions were differentially associated with clinical ratings of formal thought disorder and neuroanatomical measures obtained through structural and diffusion-weighted MRI. Notably, Syntactic Complexity and Lexical Diversity correlated with decreased fractional anisotropy (FA) in frontotemporal white matter tracts, while Narrow Thematic Focus was linked to reduced gray matter volume in the right posterior insula. Importantly, these associations persisted after controlling for diagnosis, medication status, and verbal IQ. These findings suggest that NLP-derived speech metrics can serve as sensitive indicators for language dysfunction in psychiatric conditions, offering a scalable approach to elucidate brain-behavior relationships and advance models of psychopathology.