Levodopa alters resting‐state functional connectivity more selectively in Parkinson's disease with freezing of gait
Bibliographic record
Abstract
Freezing of gait (FOG) is a debilitating motor symptom of Parkinson's disease (PD). Although PD dopaminergic medication (L-DOPA) seems to generally reduce FOG severity, its effect on neural mechanisms of FOG remains to be determined. The purpose of this study was to quantify the effect of L-DOPA on brain resting-state functional connectivity in individuals with FOG. Functional magnetic resonance imaging was acquired at rest in 30 individuals living with PD (15 freezers) in the ON- and OFF- medication state. A seed-to-voxel analysis was performed with seeds in the bilateral basal ganglia nuclei, the thalamus and the mesencephalic locomotor region. In freezers, medication-state contrasts revealed numerous changes in resting-state functional connectivity, not modulated by L-DOPA in non-freezers. In freezers, L-DOPA increased the functional connectivity between the seeds and regions including the posterior parietal, the posterior cingulate, the motor and the medial prefrontal cortices. Comparisons with non-freezers revealed that L-DOPA generally normalizes brain functional connectivity to non-freezers levels but can also increase functional connectivity, possibly compensating for dysfunctional networks in freezers. Our findings suggest that L-DOPA could contribute to a better sensorimotor, attentional, response inhibition and limbic processing to prevent FOG when triggers are encountered but could also contribute to FOG by interfering with the processing capacity of the striatum. This study shows that levodopa taken to control PD symptoms induces changes in functional connectivity at rest, in freezers only. Increases (green) in functional connectivity of GPe, GPi, putamen and thalamus with cognitive, sensorimotor and limbic cortical regions of the Interference model (blue) was observed. Our results suggest that levodopa can normalize connections similar to non-freezers or increases connectivity to compensate for dysfunctional networks.
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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 | 0.000 |
| 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) | 0.001 | 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".