Acoustic biomarkers for schizophrenia spectrum disorders and their associations with symptoms and cognitive...

Acoustic biomarkers for schizophrenia spectrum disorders (SSDs) hold great promise due to their capacity to capture emotional information, which is often impaired in these patients. These biomarkers are easily accessible, noninvasive, objective, and cost-effective. This study investigated the accuracy of different machine learning (ML) models in classifying patients with SSDs or schizophrenia (SZ) versus healthy controls (HCs), as well as patients with cognitive-deficit (Cog-D) versus cognitive-non-deficit (Cog-ND) versus HCs. Additionally, correlations of the top 25 features contributing to these classifications with psychopathology and cognitive functioning were explored.

Speech data were collected from patients with SSDs (n = 238) and HCs (n = 157) using multiple tasks, including the reading of emotional sentences. The Extrapyramidal Symptom Rating Scale (ESRS) was used to control for potential medication effects on speech. Acoustic features were extracted using the openSMILE toolkit, and models were trained with 10-fold cross-validation. Partial correlation analysis, adjusted for ESRS and chlorpromazine (CPZ) equivalent, was conducted between the top 25 features and measures of psychopathology and cognitive functioning.

Among the five ML models, accuracy of support vector machine (SVM) model was the best. It classified SSDs versus HCs with 83 % accuracy when using all 7 tasks, and 85 % when using only the happy sentences task. The SVM classification accuracy for Cog-D versus Cog-ND within SSDs was poor across all tasks; however, the accuracy for Cog-D versus HCs was 79 % when using free speech or happy sentences. The accuracy for classifying SZ versus HCs and Cog-D versus Cog-ND versus HCs exhibited variations. Several of the top 25 acoustic features correlated significantly with attention and verbal memory in patients with SSDs.

Our findings suggested that acoustic analysis, combined with a ML approach, could be used to classify successfully SSDs or the Cog-D subtype versus HCs. Features related to pitch, loudness, and timbre were particularly associated with attention in patients with SSDs. Future research should explore further the potential applications of acoustic biomarkers in multi-class classification, treatment response, and relapse detection in patients with SSDs.