Tablet-Based Functional MRI of the Trail Making Test: Effect of Tablet Interaction Mode
Bibliographic record
Abstract
The Trail Making Test (TMT) is widely used for assessing executive function, frontal lobe abilities, and visual motor skills. Part A of this pen-and-paper test (TMT-A) involves linking numbers randomly distributed in space, in ascending order. Part B (TMT-B) alternates between linking numbers and letters. TMT-B is more demanding than TMT-A, but the mental processing that supports the performance of this test remains incompletely understood. Functional MRI (fMRI) may help to clarify the relationship between TMT performance and brain activity, but providing an environment that supports real-world pen-and-paper interactions during fMRI is challenging. Previously, an fMRI-compatible tablet system was developed for writing and drawing with two modes of interaction: the original cursor-based, proprioceptive approach, and a new mode involving augmented reality to provide visual feedback of hand position (VFHP) for enhanced user interaction. This study characterizes the use of the tablet during fMRI of young healthy adults (n=22), with half of the subjects performing TMT with VFHP and the other half performing TMT without VFHP. Activation maps for both TMT-A and TMT-B showed considerable overlap between the two tablet modes, and no statistically differences in brain activity were detected when contrasting TMT-B versus TMT-A for the two tablet modes. Behavioral results also showed no statistically different interaction effects for TMT-B versus TMT-A performance between the two tablet modes. Tablet-based TMT scores showed reasonable convergent validity with those obtained by administering the standard pen-and-paper TMT to the same subjects. Overall, the results suggest that despite the slightly different mechanisms involved for the two modes of tablet interaction, both are suitable for use in fMRI studies involving TMT performance. This study provides information for using tablet-based TMT methods appropriately in future fMRI studies involving patients and healthy individuals.
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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 | 0.000 | 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".