A computerized tablet with visual feedback of hand position for functional magnetic resonance imaging
Bibliographic record
Abstract
Neuropsychological tests behavioral tasks that very commonly involve handwriting and drawing are widely used in the clinic to detect abnormal brain function. Functional magnetic resonance imaging (fMRI) may be useful in increasing the specificity of such tests. However, performing complex pen-and-paper tests during fMRI involves engineering challenges. Previously, we developed an fMRI-compatible, computerized tablet system to address this issue. However, the tablet did not include visual feedback of hand position (VFHP), a human factors component that may be important for fMRI of certain patient populations. A real-time system was thus developed to provide VFHP and integrated with the tablet in an augmented reality display. The effectiveness of the system was initially tested in young healthy adults who performed various handwriting tasks in front of a computer display with and without VFHP. Pilot fMRI of writing tasks were performed by two representative individuals with and without VFHP. Quantitative analysis of the behavioral results indicated improved writing performance with VFHP. The pilot fMRI results suggest that writing with VFHP requires less neural resources compared to the without VFHP condition, to maintain similar behavior. Thus, the tablet system with VFHP is recommended for future fMRI studies involving patients with impaired brain function and where ecologically valid behavior is important.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".