Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling
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 p...
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Elsevier
2021-01-01
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Series: | NeuroImage: Clinical |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158221001108 |
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doaj-fdd5398838da40b6b076cb552ee4c979 |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
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 |
spellingShingle |
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 Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling NeuroImage: Clinical Formal thought disorder Neuroimaging Machine learning |
author_facet |
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 |
author_sort |
Ji Chen |
title |
Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling |
title_short |
Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling |
title_full |
Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling |
title_fullStr |
Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling |
title_full_unstemmed |
Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling |
title_sort |
neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling |
publisher |
Elsevier |
series |
NeuroImage: Clinical |
issn |
2213-1582 |
publishDate |
2021-01-01 |
description |
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. |
topic |
Formal thought disorder Neuroimaging Machine learning |
url |
http://www.sciencedirect.com/science/article/pii/S2213158221001108 |
work_keys_str_mv |
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doaj-fdd5398838da40b6b076cb552ee4c9792021-06-13T04:38:07ZengElsevierNeuroImage: Clinical2213-15822021-01-0130102666Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modelingJi Chen0Tobias Wensing1Felix Hoffstaedter2Edna C. Cieslik3Veronika I. Müller4Kaustubh R. Patil5André Aleman6Birgit Derntl7Oliver Gruber8Renaud Jardri9Lydia Kogler10Iris E. Sommer11Simon B. Eickhoff12Thomas Nickl-Jockschat13Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, GermanyDepartment of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH, Aachen, Germany; JARA Translational Brain Medicine, Aachen, GermanyInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, GermanyInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, GermanyInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, GermanyInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, GermanyDepartment of Neuroscience, University of Groningen, University Medical Center Groningen, the NetherlandsDepartment of Psychiatry and Psychotherapy, Medical School, University of Tübingen, GermanySection for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, GermanyUniv Lille, INSERM U1172, Lille Neuroscience & Cognition Centre, Plasticity &SubjectivitY Team & CHU Lille, Fontan Hospital, CURE Platform, Lille, FranceDepartment of Psychiatry and Psychotherapy, Medical School, University of Tübingen, GermanyDepartment of Biomedical Science of Cells and Systems, University of Groningen, University Medical Center Groningen, the NetherlandsInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, GermanyIowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, United States; Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA, United States; Corresponding author at: Department of Psychiatry, Iowa Neuroscience Institute, University of Iowa, 2312 Pappajohn Biomedical Discovery Building, Iowa City 52242, IA, United States.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.http://www.sciencedirect.com/science/article/pii/S2213158221001108Formal thought disorderNeuroimagingMachine learning |