Attention and grasping in Parkinson's disease: Effects of treatment and disease stages
Bibliographic record
Abstract
Objective: To compare the attentional resources devoted to reach-to-grasp movements at different stages of Parkinson’s disease (PD), on and off treatment, relative to intact controls. Background: Reach-to-grasp movements are a critical component of activities of daily living. The grasping movements of patients with PD are characterized by a reliance on cues and visual feedback, not seen in the very automatic movements of intact controls (Azulay et al, 2006). PD patients appear to devote significant attentional resources to their movements; dual-task performance is a well-known area of difficulty for PD patients. To date, the role of attention in everyday reach-to-grasp movements has not been examined, though the implications for performance of activities of daily living are clearly considerable. Methods: The performance of three patient groups [de novo (n=11); moderate (n=24); and surgical (STN DBS; n=14)] was compared with age matched controls (n=24) on a dual task paradigm involving grasping and a concurrent cognitive task designed to consume attention. Attentional resources were measured with standardized tests. Participants reached to grasp functional objects with handles (e.g. a comb). Handles were turned away from participants; an appropriate grasp was scored if they successfully picked up the object by the handle. Between and within group comparisons were made using ANOVA and t-tests. Results: The control group and the de novo group not yet on medication displayed strong evidence of automaticity in their movements; when performing a challenging spatial imagery task, they made the same number of appropriate grasps as when they picked up the objects without a concurrent task. In contrast, for the moderate and the surgical groups, performing the concurrent task resulted in fewer appropriate grasps, and there was no improvement while on treatment (medication and stimulation, respectively). Only the surgical group showed decreased divided attention on standardized measures (>-0.7 SD below their normative group). Conclusions: Grasping appears to shift from an automatic to an attention demanding process by the moderate stages of PD. This may be a coping mechanism designed to safely adapt to reduced motor function, or it may reflect pathology in the mechanisms underlying grasping movements. References: Azulay JP, Mesure S, and Blin O. 2006. J Neurol Sci, 248(1-2): 192-195.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".