A Method for Estimating Longitudinal Change in Motor Skill from Individualized Functional-Connectivity Measures
Bibliographic record
Abstract
Pragmatic, objective, and accurate motor assessment tools could facilitate more frequent appraisal of longitudinal change in motor function and subsequent development of personalized therapeutic strategies. Brain functional connectivity (FC) has shown promise as an objective neurophysiological measure for this purpose. The involvement of different brain networks, along with differences across subjects due to age or existing capabilities, motivates an individualized approach towards the evaluation of FC. We advocate the use of EEG-based resting-state FC (rsFC) measures to address the pragmatic requirements. Pertaining to appraisal of accuracy, we suggest using the acquisition of motor skill by healthy individuals that could be quantified at small incremental change. Computer-based tracing tasks are a good candidate in this regard when using spatial error in tracing as an objective measure of skill. This work investigates the application of an individualized method that utilizes Partial Least Squares analysis to estimate the longitudinal change in tracing error from changes in rsFC. Longitudinal data from participants yielded an average accuracy of 98% (standard deviation of 1.2%) in estimating tracing error. The results show potential for an accurate individualized motor assessment tool that reduces the dependence on the expertise and availability of trained examiners, thereby facilitating more frequent appraisal of function and development of personalized training programs.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".