Language Network Dysfunction and Formal Thought Disorder in schizophrenia
Bibliographic record
Abstract
BackgroundPathophysiological inquiries into schizophrenia require a consideration of one of its defining features: disorganisation and impoverishment in verbal behaviour. This feature, often captured using the term Formal Thought Disorder, still remains to be one of the most poorly understood and often understudied dimensions of schizophrenia. AimIn this review, we consider the various challenges that need to be addressed for us to move towards mapping FTD (construct) to a brain network level account (circuit). This construct-to-circuit mapping goal is now becoming more plausible than it ever was, given the advent of tools providing objective readouts of human speech. ConclusionsWe suggest that any comprehensive pathophysiological model of schizophrenia must satisfactorily account for the onset and persistence of FTD and address the nature of Language Network dysfunction in this illness. To this end, we consider the need for phenotype refinement, robust experimental designs, informed analytical choices and suggest specific steps that can be taken towards the deciphering the neural mechanisms underwriting FTD, and the translational promise of improved interpersonal communication and reduced social disability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".