Dynamic Meta-Networking Identifies Distinct Network Correlates of Positive and Negative Formal Thought Disorder in Schizophrenia
Bibliographic record
Abstract
Abstract Formal thought disorder (FTD) is a core symptom of schizophrenia, yet the neural network mechanisms underlying this phenotype remain poorly understood. In this study, we applied a dynamic meta-networking framework, which captures temporally recurring functional network states, to investigate alterations in the language and executive control networks and their associations with positive and negative FTD. Resting-state fMRI data were collected from three independent cohorts: a discovery cohort comprising 150 first-episode, drug-naïve patients with schizophrenia and 175 healthy controls (HCs); a replication cohort including 183 first-episode, drug-naïve patients and 109 HCs; and a third cohort consisting of 71 patients who had received two weeks of antipsychotic treatment and 71 HCs. Meta-networking analysis identified four distinct resting-state meta-states within both the language and executive control networks. Connectivity-behavior correlation analyses and machine-learning regression models revealed that positive FTD was associated with aberrant connectivity across specific meta-states in both networks. In contrast, negative FTD was linked exclusively to dysfunction within two meta-states of the executive control network. Notably, these polarity-specific, multi-state connectivity disruptions normalized following short-term antipsychotic treatment, highlighting their potential as clinically relevant neuroimaging biomarkers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".