Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling

Ji Chen; Tobias Wensing; Felix Hoffstaedter; Edna C Cieslik; Veronika I Müller; Kaustubh R Patil; André Aleman; Birgit Derntl; Oliver Gruber; Renaud Jardri; Lydia Kogler; Iris E Sommer; Simon B Eickhoff; Thomas Nickl-Jockschat

doi:10.1016/j.nicl.2021.102666

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Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling

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Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling

Ji Chen, Tobias Wensing, Felix Hoffstaedter, Edna C Cieslik, Veronika I Müller, Kaustubh R Patil, André Aleman, Birgit Derntl, Oliver Gruber, Renaud Jardri, …

NeuroImage clinical, Vol.30, pp.102666-102666

2021

DOI: 10.1016/j.nicl.2021.102666

PMCID: PMC8105296

PMID: 34215141

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Abstract

•Formal thought disorder (FTD) is a core symptom of schizophrenia, but its neurobiological substrates remain elusive.•Resting-state functional connectivity (rsFC) of three meta-analytically defined seeds were correlated to positive and negative symptom dimensions of FTD.•RsFC patterns allowed individual prediction of positive FTD symptom severity.•These findings generalized to an independent data set.•Our study has identified robust neurobiological correlates of positive FTD in schizophrenia. Formal thought disorder (FTD) is a core symptom cluster of schizophrenia, but its neurobiological substrates remain poorly understood. Here we collected resting-state fMRI data from 276 subjects at seven sites and employed machine-learning to investigate the neurobiological correlates of FTD along positive and negative symptom dimensions in schizophrenia. Three a priori, meta-analytically defined FTD-related brain regions were used as seeds to generate whole-brain resting-state functional connectivity (rsFC) maps, which were then compared between schizophrenia patients and controls. A repeated cross-validation procedure was realized within the patient group to identify clusters whose rsFC patterns to the seeds were repeatedly observed as significantly associated with specific FTD dimensions. These repeatedly identified clusters (i.e., robust clusters) were functionally characterized and the rsFC patterns were used for predictive modeling to investigate predictive capacities for individual FTD dimensional-scores. Compared with controls, differential rsFC was found in patients in fronto-temporo-thalamic regions. Our cross-validation procedure revealed significant clusters only when assessing the seed-to-whole-brain rsFC patterns associated with positive-FTD. RsFC patterns of three fronto-temporal clusters, associated with higher-order cognitive processes (e.g., executive functions), specifically predicted individual positive-FTD scores (p = 0.005), but not other positive symptoms, and the PANSS general psychopathology subscale (p > 0.05). The prediction of positive-FTD was moreover generalized to an independent dataset (p = 0.013). Our study has identified neurobiological correlates of positive FTD in schizophrenia in a network associated with higher-order cognitive functions, suggesting a dysexecutive contribution to FTD in schizophrenia. We regard our findings as robust, as they allow a prediction of individual-level symptom severity.

Machine Learning

Neuroimaging

Formal thought disorder

Details

Title: Subtitle: Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling
Creators: Ji Chen - Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China
Tobias Wensing - Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH, Aachen, Germany
Felix Hoffstaedter - Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
Edna C Cieslik - Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
Veronika I Müller - Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
Kaustubh R Patil - Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
André Aleman - Department of Neuroscience, University of Groningen, University Medical Center Groningen, the Netherlands
Birgit Derntl - Department of Psychiatry and Psychotherapy, Medical School, University of Tübingen, Germany
Oliver Gruber - Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Germany
Renaud Jardri - Univ Lille, INSERM U1172, Lille Neuroscience & Cognition Centre, Plasticity &SubjectivitY Team & CHU Lille, Fontan Hospital, CURE Platform, Lille, France
Lydia Kogler - Department of Psychiatry and Psychotherapy, Medical School, University of Tübingen, Germany
Iris E Sommer - Department of Biomedical Science of Cells and Systems, University of Groningen, University Medical Center Groningen, the Netherlands
Simon B Eickhoff - Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
Thomas Nickl-Jockschat - University of Iowa, Psychiatry
Resource Type: Journal article
Publication Details: NeuroImage clinical, Vol.30, pp.102666-102666
DOI: 10.1016/j.nicl.2021.102666
PMID: 34215141
PMCID: PMC8105296
NLM abbreviation: Neuroimage Clin
ISSN: 2213-1582
eISSN: 2213-1582
Publisher: Elsevier Inc
Language: English
Date published: 2021
Academic Unit: Psychiatry; Iowa Neuroscience Institute; Neuroscience and Pharmacology
Record Identifier: 9984216456102771

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