The Neural Correlates of Mental Rotation Abilities in Cannabis-Abusing Patients with Schizophrenia: An fMRI Study
Bibliographic record
Abstract
Growing evidence suggests that cannabis abuse/dependence is paradoxically associated with better cognition in schizophrenia. Accordingly, we performed a functional magnetic resonance imaging (fMRI) study of visuospatial abilities in 14 schizophrenia patients with cannabis abuse (DD), 14 nonabusing schizophrenia patients (SCZ), and 21 healthy controls (HCs). Participants performed a mental rotation task while being scanned. There were no significant differences in the number of mistakes between schizophrenia groups, and both made more mistakes on the mental rotation task than HC. Relative to HC, SCZ had increased activations in the left thalamus, while DD patients had increased activations in the right supramarginal gyrus. In both cases, hyper-activations are likely to reflect compensatory efforts. In addition, SCZ patients had decreased activations in the left superior parietal gyrus compared to both HC and DD patients. This latter result tentatively suggests that the neurophysiologic processes underlying visuospatial abilities are partially preserved in DD, relative to SCZ patients, consistently with the findings showing that cannabis abuse in schizophrenia is associated with better cognitive functioning. Further fMRI studies are required to examine the neural correlates of other cognitive dysfunctions in schizophrenia patients with and without comorbid cannabis use disorder.
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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 | 0.000 |
| 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".