Semantic Network Disconnection in Formal Thought Disorder
Bibliographic record
Abstract
BACKGROUND: Structural and functional findings in schizophrenic patients with formal thought disorder (FTD) show abnormalities within left-side semantic areas. The present study investigate the network function of the involved brain regions as a function of FTD severity. METHODS: We examined a group of 16 schizophrenia patients differing in FTD, but not in overall symptom severity, and 18 matched healthy controls. A passive word reading paradigm was applied during functional MRI (fMRI). A concatenated independent component analysis approach separated the fMRI signal into independent components, and spatial similarity was used to estimate the individual differences in spatial configuration of networks. RESULTS: The semantic network was identified for both groups encompassing structures of the left inferior frontal gyrus, the left angular gyrus and the left middle temporal gyrus. The differences between the semantic networks of patients and controls increased with increasing severity of FTD. This difference was due to a decreasing contribution of the left inferior frontal gyrus (Brodmann area 45 and 47). CONCLUSION: Severity of FTD was correlated with a disruption of the left semantic network in schizophrenic patients. We suggest that FTD is a consequence of a frontal-parietal/temporal disconnection due to a complex interaction between structural and functional abnormalities within the left semantic network.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".