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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.

SUBMITTER: Haas SS

PROVIDER: S-EPMC7443790 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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

Haas S S SS Doucet G E GE Garg S S Herrera S N SN Sarac C C Bilgrami Z R ZR Shaik R B RB Corcoran C M CM

European psychiatry : the journal of the Association of European Psychiatrists 20200811 1

<h4>Background</h4>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.<h4>Methods</h4>Automated natural language processing analysis was applied to speech samples obtained from 46 CHR and 22 healthy individuals. Brain structural and resting-state func ...[more]

PMID: 32778184

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Project description:Importance:Alterations in multiple neuroimaging phenotypes have been reported in psychotic disorders. However, neuroimaging measures can be influenced by factors that are not directly related to psychosis and may confound the interpretation of case-control differences. Therefore, a detailed characterization of the contribution of these factors to neuroimaging phenotypes in psychosis is warranted. Objective:To quantify the association between neuroimaging measures and behavioral, health, and demographic variables in psychosis using an integrated multivariate approach. Design, Setting, and Participants:This imaging study was conducted at a university research hospital from June 26, 2014, to March 9, 2017. High-resolution multimodal magnetic resonance imaging data were obtained from 100 patients with schizophrenia, 40 patients with bipolar disorder, and 50 healthy volunteers; computed were cortical thickness, subcortical volumes, white matter fractional anisotropy, task-related brain activation (during working memory and emotional recognition), and resting-state functional connectivity. Ascertained in all participants were nonimaging measures pertaining to clinical features, cognition, substance use, psychological trauma, physical activity, and body mass index. The association between imaging and nonimaging measures was modeled using sparse canonical correlation analysis with robust reliability testing. Main Outcomes and Measures:Multivariate patterns of the association between nonimaging and neuroimaging measures in patients with psychosis and healthy volunteers. Results:The analyses were performed in 92 patients with schizophrenia (23 female [25.0%]; mean [SD] age, 27.0 [7.6] years), 37 patients with bipolar disorder (12 female [32.4%]; mean [SD] age, 27.5 [8.1] years), and 48 healthy volunteers (20 female [41.7%]; mean [SD] age, 29.8 [8.5] years). The imaging and nonimaging data sets showed significant covariation (r?=?0.63, P?<?.001), which was independent of diagnosis. Among the nonimaging variables examined, age (r?=?-0.53), IQ (r?=?0.36), and body mass index (r?=?-0.25) were associated with multiple imaging phenotypes; cannabis use (r?=?0.23) and other substance use (r?=?0.33) were associated with subcortical volumes, and alcohol use was associated with white matter integrity (r?=?-0.15). Within the multivariate models, positive symptoms retained associations with the global neuroimaging (r?=?-0.13), the cortical thickness (r?=?-0.22), and the task-related activation variates (r?=?-0.18); negative symptoms were mostly associated with measures of subcortical volume (r?=?0.23), and depression/anxiety was associated with measures of white matter integrity (r?=?0.12). Conclusions and Relevance:Multivariate analyses provide a more accurate characterization of the association between brain alterations and psychosis because they enable the modeling of other key factors that influence neuroimaging phenotypes.

Dataset Information

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

Publications

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

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