Unique signatures in verbal fluency task performance in schizophrenia and depression

Area of Science:

• Cognitive Neuroscience
• Psychiatric Disorders
• Linguistics

Background:

• Verbal fluency tasks are crucial for understanding semantic and cognitive processing in schizophrenia (SSD).
• Limited research compares verbal fluency in SSD with other psychiatric conditions, hindering the identification of unique versus shared impairments.
• This study aims to differentiate cognitive-linguistic deficits by comparing verbal fluency in individuals with SSD, unipolar depression, and healthy volunteers.

Purpose of the Study:

• To compare verbal fluency performance across individuals with schizophrenia, unipolar depression, and healthy volunteers.
• To investigate whether observed verbal fluency impairments are specific to schizophrenia or common across diagnoses.
• To explore the utility of automated analysis in identifying distinct cognitive-linguistic patterns in psychiatric populations.

Main Methods:

• Sixty-four individuals with SSD, 70 with unipolar depression, and 37 healthy volunteers completed letter- and category-guided verbal fluency tasks.
• Automated pipelines extracted metrics including total responses, pause durations, response latency, semantic/phonemic distances, and cluster characteristics.
• Associations between fluency metrics and clinical ratings of speech disturbances were analyzed within patient groups.

Main Results:

• Significant differences in total fluency scores were observed, with the greatest impairment in SSD and intermediate effects in depression, especially on category fluency tasks.
• The number of semantic and phonemic clusters differed significantly between groups, while cluster size showed fewer variations.
• Healthy volunteers demonstrated strategic cluster switching (increased semantic/phonemic distances with longer pauses), a pattern absent in SSD and depression groups. SSD showed prolonged latency and pauses, while depression showed prolonged pauses but normal latency.

Conclusions:

• Schizophrenia and depression exhibit distinct verbal fluency patterns, with category tasks highlighting more pronounced group differences.
• Automated verbal fluency analysis provides a scalable method for distinguishing cognitive-linguistic impairments across diverse psychiatric populations.
• These findings contribute to a better understanding of the heterogeneity of cognitive deficits in mental health disorders.