The Incidence and Nature of Cerebellar Findings in Schizophrenia: A Quantitative Review of fMRI Literature
Bibliographic record
Abstract
Clinical evidence and structural neuroimaging studies linked cerebellar deficits to cognitive-related symptoms in schizophrenia. Yet, in functional neuroimaging literature to date, the role of the cerebellum in schizophrenia was not explored in a systematic fashion. Here, we reviewed 234 functional magnetic resonance imaging studies indexed by PubMed and published in 1997-2010 that had at least one group of schizophrenia patients, used blood oxygenation level dependent contrast and the general linear model to assess neuronal activity. We quantified presence/absence of cerebellar findings and the frequency of hypo- and hyperactivations (ie, less or more activity in patients relative to healthy controls). We used peaks of activations reported in these studies to build a topographical representation of group differences on a cerebellar map. Cerebellar activity was reported in patients in 41.02% of the articles, with more than 80% of these dedicated to cognitive, emotional, and executive processes in schizophrenia. Almost two-thirds of group comparisons resulted in cerebellar hypoactivation, with a frequency that presented an inverted U shape across different age categories. The majority of the hypoactivation foci were located in the medial portion of the anterior lobe and the lateral hemispheres (lobules IV-V) of the cerebellum. Even though most experimental manipulations did not target explicitly the cerebellum's functions in schizophrenia, the cerebellar findings are frequent and cerebellar hypoactivations predominant. Therefore, although the cerebellum seems to play an important functional role in schizophrenia, the lack of reporting and interpretation of these data may hamper the full understanding of the 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.034 | 0.025 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".