Short-Latency Afferent Inhibition Correlates with Stage of Disease in Parkinson’s Patients
Bibliographic record
Abstract
BACKGROUND: Sensory-motor decoupling at the cortical level involving cholinergic circuitry has also been reported in Parkinson's Disease (PD). Short-latency afferent inhibition (SAI) is a transcranial magnetic stimulation (TMS) paradigm that has been used previously to probe cortical cholinergic circuits in well-characterised subgroups of patients with PD. In the current study, we compared SAI in a cohort of PD patients at various stages of disease and explored correlations between SAI and various clinical measures of disease severity. METHODS: The modified Hoehn and Yahr (H&Y) scale was used to stage disease in 22 patients with PD. Motor and cognitive function were assessed using the MDS-UPDRS (Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale) part III and MoCA (Montreal Cognitive Assessment) score, respectively. Objective gait assessment was performed using an electronic walkway (GAITRite®). SAI was measured as the average percentage inhibition of test motor-evoked potentials (MEPs) conditioned by electrical stimulation of the contralateral median nerve at the wrist. RESULTS: SAI was significantly reduced in patients with advanced PD (H&Y stage 3) compared to early PD patients (H&Y stage 1) on pairwise comparison. The visuospatial executive function and orientation domains of cognition demonstrated significant negative associations with SAI. CONCLUSION: Cortical sensory-motor integration is progressively diminished as disease progresses. The observation that a reduction in SAI is associated with a reduction in cognitive function possibly reflects the progressive involvement of cortical cholinergic circuits in PD with increasing motor stage. Future longitudinal studies are necessary to confirm this preliminary result.
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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) | 0.001 | 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".