Cognitive cerebellum dominates motor cerebellum in functional decline of older adults with mild cognitive impairment
Bibliographic record
Abstract
OBJECTIVES: The present study aims to investigate the role that cognitive cerebellar lobules, compared to the motor ones, could have on performance abilities control in older individuals with Mild Cognitive Impairment (MCI). METHODS: Thirty-six participants with MCI were retrospectively recruited from the outpatient clinic for Cognitive Decline and Dementia at Geriatric Clinic and Regional Center for Brain Aging. Cognition was assessed through a reaction time (RT) task in which a mere cognitive (COG) component (RT/S1 COG, RT/S3 COG) has been isolated from a motor (MOT) component (RT/S1 MOT, RT/S3 MOT). Performance abilities were evaluated using Short Physical Performance Battery (SPPB), Tinetti Scale, and Activities of Daily Living (ADL). Finally, structural neuroimaging was conducted using magnetic resonance imaging at 3T. RESULTS: Left_Crus_I showed a correlation with SPPB, ADL%, and RT/S3 COG. Vermis_VI and Right_VI were correlated to ADL%, and RT/S3 COG with the entire lobule VI. ADL% showed negative correlations with RT/S1 COG, RT/S3 COG, and RT/S3 MOT. In the regression analysis, the strongest associations were found between RT/S3 COG and SPPB gait speed (R2 = 0.44, p = 0.03), Tinetti gait speed (R2 = 0.62, p < 0.001), and ADL% (R2 = 0.78, p < 0.001). Regarding cerebellar volumes, Right_Crus_I was associated with all SPPB tests, while Left_VI was associated with functional autonomy (ADL%: R2 = 0.78, p = 0.04). No associations were found between performance variables and total intracranial volume. CONCLUSIONS: This study highlights that the cognitive cerebellar component dominates over the motor one even in the control of physical and functional capabilities of older adults with MCI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".