Cannabis-induced alterations in brain activation during a test of information processing speed in patients with MS
Bibliographic record
Abstract
Objective The objective of this article is to determine the functional brain correlates of information processing speed in multiple sclerosis (MS) patients who smoke cannabis and those who are drug naïve. Methods Two neurologically and demographically matched samples of MS patients were enrolled, those who smoked cannabis daily ( n = 20) and those who were cannabis naïve ( n = 19). All participants completed the Brief Repeatable Battery of Neuropsychological Tests and underwent fMRI testing during which they were administered a modified version of the Symbol Digit Modalities Test (mSDMT). Results The cannabis group responded slower in nine of 11 blocks of the mSDMT ( p < 0.001), showing a trend toward a slower response time ( p < 0.08), but did not differ in the accuracy of response ( p < 0.18). Both groups displayed activation in a prefrontal cortex-parietal network associated with information processing speed. When compared to the cannabis-naïve group, cannabis users showed less activation in the right ( p = 0.009) and left ( p = 0.001) thalami and increased activation in the anterior cingulate ( p = 0.006). Conclusion Regular cannabis use in MS patients is associated with slower information processing speed and a pattern of cerebral activity that differs from cannabis-naïve individuals, most notably in a bilateral reduction of thalamic activity.
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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 | 0.000 | 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) | 0.002 | 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".