Dataset Information

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors.

ABSTRACT: In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1-year follow-up was assessed in 30 individuals with a schizophrenia-spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting-state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1-year follow-up varied markedly among individuals (interquartile range: 55%). Dynamic resting-state connectivity measured within the default-mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow-up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1-year follow-up were predicted by hyper-connectivity and hypo-dynamism within the default-mode network at baseline assessment, while hypo-connectivity and hyper-dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates.

SUBMITTER: Kottaram A

PROVIDER: S-EPMC7375115 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors.

Kottaram Akhil A Johnston Leigh A LA Tian Ye Y Ganella Eleni P EP Laskaris Liliana L Cocchi Luca L McGorry Patrick P Pantelis Christos C Kotagiri Ramamohanarao R Cropley Vanessa V Zalesky Andrew A

Human brain mapping 20200529 12

In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1-year follow-up was assessed in 30 individuals with a schizophrenia-spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifier ...[more]

PMID: 32469448

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Project description:PurposeStudies show that deficit syndrome schizophrenia patients, characterized by primary negative symptoms and poor functional outcome, have impairment in specific neural circuits. We assessed whether these same neural circuits are directly linked to functional outcomes across schizophrenia patients.MethodsT1- and diffusion-weighted MR images were obtained for schizophrenia (n=30) and matched healthy control participants (n=30). Negative symptoms and functional outcome were assessed at baseline and 6-month follow-up. Cortical thickness and tract-wise fractional anisotropy (FA) were compared between groups. To assess relationships of neuroimaging measures with functional outcome, principal component analysis (PCA) was performed on tract-wise FA values and components were entered into a multiple regression model for schizophrenia participants.ResultsConsistent with the literature, schizophrenia participants showed frontotemporal reductions in cortical thickness and tract-wise FA compared to controls. The top two components from PCA explained 71% of the variance in tract-wise FA values. The second component (associated with inferior longitudinal and arcuate fasciculus FA) was significantly correlated with functional outcome (baseline: β=0.54, p=0.03; follow-up: β=0.74, p=0.047); further analysis revealed this effect was mediated by negative symptoms. Post-hoc network analysis revealed increased cortical coupling between right inferior frontal and supramarginal gyri (connected by the arcuate fasciculus) in schizophrenia participants with poorer functional outcome.ConclusionsOur findings indicate that impairment in the same neural circuitry susceptible in deficit syndrome schizophrenia predicts functional outcome in a continuous manner in schizophrenia participants. This relationship was mediated by negative symptom burden. Our findings provide novel evidence for brain-based biomarkers of longitudinal functional outcome in people with schizophrenia.

Project description:BackgroundThe gut microbiome and microbiome-gut-brain (MGB) axis have been receiving increasing attention for their role in the regulation of mental behavior and possible biological basis of psychiatric disorders. With the advance of next-generation sequencing technology, characterization of the gut microbiota in schizophrenia (SZ) patients can provide rich clues for the diagnosis and prevention of SZ.MethodsIn this study, we compared the differences in the fecal microbiota between 82 SZ patients and 80 demographically matched normal controls (NCs) by 16S rRNA sequencing and analyzed the correlations between altered gut microbiota and symptom severity.ResultsThe alpha diversity showed no significant differences between the NC and SZ groups, but the beta diversity revealed significant community-level separation in microbiome composition between the two groups (pseudo-F =3.337, p < 0.001, uncorrected). At the phylum level, relatively more Actinobacteria and less Firmicutes (p < 0.05, FDR corrected) were found in the SZ group. At the genus level, the relative abundances of Collinsella, Lactobacillus, Succinivibrio, Mogibacterium, Corynebacterium, undefined Ruminococcus and undefined Eubacterium were significantly increased, whereas the abundances of Adlercreutzia, Anaerostipes, Ruminococcus and Faecalibacterium were decreased in the SZ group compared to the NC group (p < 0.05, FDR corrected). We performed PICRUSt analysis and found that several metabolic pathways differed significantly between the two groups, including the Polyketide sugar unit biosynthesis, Valine, Leucine and Isoleucine biosynthesis, Pantothenate and CoA biosynthesis, C5-Branched dibasic acid metabolism, Phenylpropanoid biosynthesis, Ascorbate and aldarate metabolism, Nucleotide metabolism and Propanoate metabolism pathways (p < 0.05, FDR corrected). Among the SZ group, the abundance of Succinivibrio was positively correlated with the total Positive and Negative Syndrome Scale (PANSS) scores (r = 0.24, p < 0.05, uncorrected) as well as the general PANSS scores (r = 0.22, p < 0.05, uncorrected); Corynebacterium was negatively related to the negative scores of PANSS (r = 0.22, p < 0.05, uncorrected).ConclusionsOur findings provided evidence of altered gut microbial composition in SZ group. In addition, we found that Succinvibrio and Corynebacterium were associated with the severity of symptoms for the first time, which may provide some new biomarkers for the diagnosis of SZ.

Dataset Information

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors.

Publications

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors.

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