Abnormal cerebellar activity and connectivity alterations of the cerebellar-limbic system in post-stroke cognitive impairment: a study based on resting state functional magnetic resonance imaging
Bibliographic record
Abstract
Background: Stroke is an important cause of cognitive impairment. Post-stroke cognitive impairment (PSCI) is a prevalent psychiatric disorder following stroke. However, the effects of PSCI on the cerebellum remain mostly unknown. Methods: A total of 31 PSCI patients and 31 patients without cognitive impairment after stroke were included in this study. The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) were administered to all participants. Analyses of ALFF, fALFF, and ReHo were employed to investigate alterations in brain neuronal activity, while limbic connectivity analysis was utilized to reflect changes within the abnormal connections within brain regions. Results: We found that ALFF values were increased in Cerebelum_7b_R, Cerebelum_Crus1_L. fALFF values were increased in Vermis_3. The ReHo values were increased in Cerebelum_8_R, Cerebelum_Crus2_R, Cerebelum_Crus1_L. The functional connection between Frontal_Mid_Orb_L and Cerebelum_Crus2_R brain regions was decreased. The functional connection between Hippocampus_L and Cerebelum_Crus2_R brain regions was decreased. The functional connection between Vermis_3 and Frontal_Med_Orb_L brain regions was decreased. Conclusion: The severity of cognitive impairment may influence the extent of functional connectivity disruption between the cerebellum and the limbic system. Furthermore, atypical alterations in neuronal activity within cerebellar regions are associated with cognitive decline.
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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 | 0.001 |
| 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".