Language ability in schizophrenia patients and genetic high-risk individuals: neuropsychological and computational investigation

Abstract

The study aimed to define language-related phenotypes in schizophrenia and analyze language in schizophrenia patients (SZ) and their unaffected siblings (SIB) as a possible endophenotype. For experiment 1, language was evaluated with the Thought and Language Disorder Scale (TALD), Thought and Language Index (TLI), phonemic and semantic verbal fluency, Boston Naming Test, and Scale for Scoring the Inclusion and the Quality of the Parts of the Story. Language skills of SIB were higher than those of SZ, but lower than those of healthy controls (HC). The best predictor of SZ and SIB was TLI score in the main regression model compared to HC. For experiment 2, I utilized Natural Language Processing (NLP) to explore whether there are altered linguistic features in Turkish-speaking SZ and whether these possible features as phenotypes are language-dependent or -independent. Analyses was conducted in two parts. Firstly, mean sentence length (MSL), total completed words (TCW), moving average type-token ratio (MATTR), and first- person singular pronoun usage (FPSP) were calculated. Secondly, I used parts-of- speech tagging (POS) and Word2Vec. I found that SZ had lower MSL and MATTR but higher use of FPSP. Results were correlated with the TALD. POS demonstrated that SZ used fewer coordinating conjunctions. Word2Vec detected that SZ had higher semantic similarity than HC and K-Means could differentiate between SZ and HC into two distinct groups with high accuracy, 86.84%. My findings suggest that semantics as subparts of language could be a possible endophenotype in schizophrenia. Thus, their assessment may improve the early diagnosis of the illness. Also, it showed that altered linguistic features in SZ are mostly language- independent.