Functional Connectivity of the Cerebellar Vermis in Bipolar Disorder and Associations with Mood
Bibliographic record
Abstract
Purpose: Studies of the neural underpinnings of bipolar type I disorder have focused on the emotional control network. However, there is also growing evidence for cerebellar involvement, including abnormal structure, function, and metabolism. Here, we sought to assess functional connectivity of the cerebellum with the cerebrum in bipolar disorder and to assess whether any effects might depend on mood. Methods: This cross-sectional study enrolled 128 participants with bipolar type I disorder and 83 control comparison participants who completed a 3T MRI scan, which included anatomical imaging as well as resting state BOLD imaging. Functional connectivity of the cerebellar vermis to all other brain regions was assessed. Based on quality control metrics of the fMRI data, 109 participants with bipolar disorder and 79 controls were used to in the statistical analysis comparing connectivity of the vermis as well as associations with mood. Potential impacts of medications were also explored. Results: Functional connectivity of the cerebellar vermis in bipolar disorder was found to differ significantly between brain regions known to be involved in the control of emotion, motor function, and language. While connections with emotion and motor control areas were significantly stronger in bipolar disorder, connection to a region associated language production was significantly weaker. In the participants with bipolar disorder, ratings of depression and mania were inversely associated with vermis functional connectivity. No effect of medications on these connections were observed. Conclusion: Together the findings suggest cerebellum may play a compensatory role in bipolar disorder and when it can no longer fulfill this role, depression and mania develop. The proximity of the cerebellar vermis to the skull may make this region a potential target for treatment with transcranial magnetic stimulation.
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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) | 0.002 | 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".