Cerebellum as a neural substrate for impoverishment in early psychosis
Bibliographic record
Abstract
BACKGROUND: Formal Thought Disorder and includes both positive (i.e., disorganized speech) and negative (i.e., impoverished speech) symptoms. Emerging evidence suggests that the cerebellum plays a critical role in cognitive functions, including language processing. This study leverages Natural Language Processing to objectively measure language disturbances in patients with first-episode psychosis and investigates the relationship between these disturbances and cerebellar structure. METHODS: Fifty-four patients with schizophrenia, either drug-naïve or minimally medicated, were recruited from an early psychosis program. Impoverished thought was assessed using the Thought Language Index while lexico-semantic features (affect, cognitive, linguistic, perception, time) were identified from speech samples analyzed using the Linguistic Inquiry Word Count-22 software. Structural cerebellar analysis was completed on 7.0 Tesla MRI scans using voxel-based morphometry (VBM) to measure global and regional grey matter volume changes. RESULTS: Linear regression analysis revealed that reduced perceptual word usage was the strongest predictor of impoverished thinking. Correlational analysis identified reduced cerebellar volumes in patients with lower LIWC-based perception scores. VBM localized this relationship to a cluster in the right posterolateral cerebellar hemisphere, an area related to executive demand and verb generation function. CONCLUSION: The cerebellum contributes to impoverished thinking in early psychosis, likely by influencing the lexical expression of perceptual experiences. This underscores the cerebellum's role in higher-order cognitive processes relevant to psychotic disorders and its potential as a therapeutic target for language and cognitive deficits in schizophrenia.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".