Motor reserve and novel area recruitment: amplitude and spatial characteristics of compensation in Parkinson’s disease
Bibliographic record
Abstract
Motor symptoms of Parkinson's disease (PD) do not appear until the majority of dopaminergic cells in the substantia nigra pars compacta are lost, suggesting significant redundancy or compensation in the motor systems affected by PD. Using functional magnetic resonance imaging, we examined whether compensation in PD is manifested by changes in amplitude and/or spatial extent of activity within normal networks (active motor reserve) and/or newly recruited regions [novel area recruitment (NAR)]. Ten PD subjects off and on medication and 10 age-matched controls performed a visually guided sinusoidal force task at 0.25, 0.5 and 0.75 Hz. Regression was used to determine the combination of regions where activation amplitude scaled linearly with movement speed in controls. We then determined the activation of PD subjects in this network, as well as the corresponding PD network. To measure the spatial variance of activation, we used an invariant spatial feature approach. Control subjects monotonically increased activity within striato-thalamo-cortical and cerebello-thalamo-cortical regions with increasing movement speed. In PD subjects, the activity of this network at low speeds was similar to that in controls at higher speeds. Additionally, PD subjects off medication demonstrated NARs of the bilateral cerebellum and primary motor cortex, which were incompletely normalized by levodopa. Our results suggest that PD subjects tap into motor reserve, increase the spatial extent of activation and demonstrate NAR to maintain near-normal motor output.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".