ArticleNeuroImage. Clinical2021
Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling.
Article in NeuroImage. Clinical, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Aberrant brain language network in schizophrenia spectrum disorder: a systematic review of its relation to language signs beyond symptoms.Frontiers in psychiatry · 2024Pooled it
- Functional and structural connectome-based predictive modelling of balance in elderly adults.Scientific reports · 2026Article
- Transdiagnostic types of formal thought disorder and their association with gray matter brain structure: a model-based cluster analytic approach.Molecular psychiatry · 2025Article
- Can artificial intelligence be the future solution to the enormous challenges and suffering caused by Schizophrenia?Schizophrenia (Heidelberg, Germany) · 2025Review
- Differences in the neural correlates of schizophrenia with positive and negative formal thought disorder in patients with schizophrenia in the ENIGMA dataset.Molecular psychiatry · 2024Article
- The Neurobiology of Formal Thought Disorder.Current topics in medicinal chemistry · 2024Review
- Neural Correlates of Positive and Negative Formal Thought Disorder in Individuals with Schizophrenia: An ENIGMA Schizophrenia Working Group Study.Research square · 2023Article
- Language Network Dysfunction and Formal Thought Disorder in Schizophrenia.Schizophrenia bulletin · 2023Review
- Language abnormalities in schizophrenia: binding core symptoms through contemporary empirical evidence.Schizophrenia (Heidelberg, Germany) · 2022Review
- Dimensions of Formal Thought Disorder and Their Relation to Gray- and White Matter Brain Structure in Affective and Psychotic Disorders.Schizophrenia bulletin · 2022Article
- Linked brain connectivity patterns with psychopathological and cognitive phenotypes in drug-naïve first-episode schizophrenia.Psychoradiology · 2022Article
- Machine learning approaches for diagnosing depression using EEG: A review.Translational neuroscience · 2022Review
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Authors and funding
14 authors at 7 institutions in 5 countries.
Funding
Abstract
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.
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