Intersegmental coordination patterns are differently affected in Parkinson’s disease and cerebellar ataxia
Bibliographic record
Abstract
The law of intersegmental coordination (Borghese et al. 1996) may be altered in pathological conditions. Here we investigated the contribution of the basal ganglia (BG) and the cerebellum to lower limb intersegmental coordination by inspecting the plane's orientation and other parameters pertinent to this law in patients with idiopathic Parkinson's disease (PD) or cerebellar ataxia (CA). We also applied a mathematical model that successfully accounts for the intersegmental law of coordination observed in control subjects (Barliya et al. 2009). In the present study, we compared the planarity index (PI), covariation plane (CVP) orientation, and CVP orientation predicted by the model in 11 PD patients, 8 CA patients, and two groups of healthy subjects matched for age, height, weight, and gender to each patient group (Ctrl_PD and Ctrl_CA). Controls were instructed to alter their gait speed to match those of their respective patient group. PD patients were examined after overnight withdrawal of anti-parkinsonian medications (PD-off-med) and then on medication (PD-on-med). PI was above 96% in all gait conditions in all groups suggesting that the law of intersegmental coordination is preserved in both BG and cerebellar pathology. However, the measured and predicted CVP orientations rotated in PD-on-med and PD-off-med compared with Ctrl_PD and in CA vs. Ctrl_CA. These rotations caused by PD and CA were in opposite directions suggesting differences in the roles of the BG and cerebellum in intersegmental coordination during human locomotion. NEW & NOTEWORTHY Kinematic and muscular synergies may have a role in overcoming motor redundancies, which may be reflected in intersegmental covariation. Basal ganglia and cerebellar networks were suggested to be involved in crafting and modulating synergies. We thus compared intersegmental coordination in Parkinson's disease and cerebellar disease patients and found opposite effects in some aspects. Further research integrating muscle activities as well as biomechanical and neural control modeling are needed to account for these findings.
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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".