Linking language features to clinical symptoms and multimodal imaging in individuals at clinical high risk for psychosis

Abstract

Background. Abnormalities in the semantic and syntactic organization of speech have been reported in individuals at clinical high-risk (CHR) for psychosis. The current study seeks to examine whether such abnormalities are associated with changes in brain structure and functional connectivity in CHR individuals. Methods. Automated natural language processing analysis was applied to speech samples obtained from 46 CHR and 22 healthy individuals. Brain structural and resting-state functional imaging data were also acquired from all participants. Sparse canonical correlation analysis (sCCA) was used to ascertain patterns of covariation between linguistic features, clinical symptoms, and measures of brain morphometry and functional connectivity related to the language network. Results. In CHR individuals, we found a significant mode of covariation between linguistic and clinical features (r = 0.73; p = 0.003), with negative symptoms and bizarre thinking covarying mostly with measures of syntactic complexity. In the entire sample, separate sCCAs identified a single mode of covariation linking linguistic features with brain morphometry (r = 0.65; p = 0.05) and resting-state network connectivity (r = 0.63; p = 0.01). In both models, semantic and syntactic features covaried with brain structural and functional connectivity measures of the language network. However, the contribution of diagnosis to both models was negligible. Conclusions. Syntactic complexity appeared sensitive to prodromal symptoms in CHR individuals while the patterns of brain-language covariation seemed preserved. Further studies in larger samples are required to establish the reproducibility of these findings.

Towards Unlocking the Linguistic Code of Post-Traumatic Stress Disorder: A Comprehensive Analysis and Diagnostic Approach

“…Speci cally, we posit that gures of speech wield signi cant in uence, yet their extraction using quantitative methodologies is challenged by their reliance on external world knowledge. Moreover, in alignment with the suggestions and practices of prior research 45,46 , we hold the belief that a multimodal model incorporating speech and video data holds immense potential in yielding insights in spite of drawbacks associated with data privacy.…”

Speech disturbances in schizophrenia: assessing cross-linguistic generalizability of NLP automated measures of coherence

“…Andreasen, 1979). However, this assumption has only inconsistently been supported by the literature, with highly varying statistical significance and size of the effects found across studies: while some studies found a strong correlation between semantic coherence and measures of formal thought disorder (Bilgrami et al, 2022; Elvevåg et al, 2007), most others reported uncertain results (Bedi et al, 2015; Haas et al, 2020; Just et al, 2020; Morgan et al, 2021; Pauselli et al, 2018; Sarzynska-Wawer et al, 2021; Tang et al, 2021). Our study emphasized the lack of a clear picture.…”

“…However, a critical obstacle to any concrete use of these findings is that it is not clear whether the findings would replicate and generalize to new samples and populations, an overarching problem for clinical and social sciences (Hitczenko et al, 2021; Parola et al, 2020; Rocca & Yarkoni, 2021; Rybner et al, 2021). Indeed, a closer look reveals clearly contradictory results: linguistic measures are inconsistently associated with symptoms, and findings vary across different rating scales and samples (Bedi et al, 2015; Corcoran et al, 2018; Haas et al, 2020; Morgan et al, 2021; Pauselli et al, 2018; Sarzynska-Wawer et al, 2021; Tang et al, 2021).…”

Latent Factors of Language Disturbance and Relationships to Quantitative Speech Features

“…22 Therefore, greater precision may be achieved by modeling specific types of speech disturbance, rather than diagnostic phenotypes as a whole. [22][23][24] Second, speech disturbances may be shared across disorders, with implications for underlying neurobiology. We need to define cross-diagnostically diagnoses.…”

“…22 Therefore, greater precision may be achieved by modeling specific types of speech disturbance, rather than diagnostic phenotypes as a whole. 22–24 Second, speech disturbances may be shared across disorders, with implications for underlying neurobiology. We need to define cross-diagnostically valid constructs in order to determine whether speech features can be used consistently across disorders as biomarkers for particular speech phenotypes.…”