Functional MRI of Handwriting Tasks: A Study of Healthy Young Adults Interacting with a Novel Touch-Sensitive Tablet
Bibliographic record
Abstract
Handwriting is a complex human activity that engages a blend of cognitive and visual motor skills. Current understanding of the neural correlates of handwriting has largely come from lesion studies of patients with impaired handwriting. Task-based fMRI studies would be useful to supplement this work. To address concerns over ecological validity, previously we developed a fMRI-compatible, computerized tablet system for writing and drawing including visual feedback of hand position and an augmented reality display. The purpose of the present work is to use the tablet system in proof-of-concept to characterize brain activity associated with clinically relevant handwriting tasks, originally developed to characterize handwriting impairments in Alzheimer's disease patients. As a prelude to undertaking fMRI studies of patients, imaging was performed of twelve young healthy subjects who copied sentences, phone numbers, and grocery lists using the fMRI-compatible tablet. Activation maps for all handwriting tasks consisted of a distributed network of regions in reasonable agreement with previous studies of handwriting performance. In addition, differences in brain activity were observed between the test subcomponents consistent with different demands of neural processing for successful task performance, as identified by investigating three quantitative behavioral metrics (writing speed, stylus contact force and stylus in air time). This study provides baseline behavioral and brain activity results for fMRI studies that adopt this handwriting test to characterize patients with brain impairments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".