Computational analysis on verbal fluency reveals heterogeneity in subjective language interests and brain structure

Abstract

Language is an essential higher cognitive function in humans and is often affected by psychiatric and neurological disorders. Objective measures like the verbal fluency test are often used to determine language dysfunction. Recent applications of computational approaches broaden insights into language-related functions. In addition, individuals diagnosed with a psychiatric or neurological disorder also often report subjective difficulties in language-related functions. Therefore, we investigated the association between objective and subjective measures of language functioning, on the one hand, and inter-individual structural variations in language-related brain areas, on the other hand. We performed a Latent Semantic analysis (LSA) on a semantic verbal fluency task in 101 healthy adult participants. To investigate if these objective measures are associated with a subjective one, we examined assessed subjective natural tendency of interest in language-related activity with a study-specific questionnaire. Lastly, a voxel-based brain morphometry (VBM) was conducted to reveal associations between objective (LSA) measures and structural changes in language-related brain areas. We found a positive correlation between the LSA measure cosine similarity and the subjective interest in language. Furthermore, we found that higher cosine similarity corresponds to higher gray matter volume in the right cerebellum. The results suggest that people with higher interests in language access semantic knowledge in a more organized way exhibited by higher cosine similarity and have larger grey matter volume in the right cerebellum, when compared to people with lower interests. In conclusion, we demonstrate that there is inter-individual diverseness of accessing the semantic knowledge space and that it is associated with subjective language interests as well as structural differences in the right cerebellum.

Keywords: Healthy Cohort; LSA; Language; SyNoPsis; VBM; computational analysis; psychosis.

Fig. 1

Distribution of the ratings of each participant on their perceived language interest. Negative scores correspond to low or no interest in language, whereas positive scores correspond to high interest in language.

Fig. 2

Statistical parametric map for correlation between LSA cosine similarity and gray matter volume. Right cerebellum was significantly correlated with LSA cosine similarity (peak: [x, y, z] = [22.5, −54, −46.5]; t-value = 5.11; p = 0.034, voxel-wise whole-brain FWE corrected for multiple comparison). For visualization purposes, voxels that survive at p < 0.001 uncorrected (t = 3.16, cluster size = 506) are depicted.