Brain activity associated with Dual‐task performance of Ankle motor control during cognitive challenge
Bibliographic record
Abstract
Abstract Introduction Skilled Ankle motor control is frequently required while performing secondary cognitively demanding tasks such as socializing and avoiding obstacles while walking, termed “Dual tasking.” It is likely that Dual‐task performance increases demand on the brain, as both motor and cognitive systems require neural resources. The purpose of this study was to use functional MRI to understand which brain regions are involved in resolving Dual‐task interference created by requiring high levels of Ankle motor control during a cognitive task. Methods Using functional MRI, brain activity was measured in sixteen young adults during performance of visually cued Ankle plantar flexion to a target (Ankle task), a cognitive task (Flanker task), and both tasks simultaneously (Dual task). Results Dual‐task performance did not impact the Ankle task (p = 0.78), but did affect behavior on the Flanker task. Response times for both the congruent and incongruent conditions during the Flanker task were significantly longer (p < 0.001, p = 0.050, respectively), and accuracy for the congruent condition decreased during Dual tasking (p < 0.001). Activity in 3 brain regions was associated with Dual‐task Flanker performance. Percent signal change from baseline in Brodmann area (BA) 5, BA6, and the left caudate correlated with performance on the Flanker task during the Dual‐task condition (R2 = 0.261, p = 0.04; R2 = −0.258, p = 0.04; R2 = 0.303, p = 0.03, respectively). Conclusions Performance of Ankle motor control may be prioritized over a cognitive task during Dual‐task performance. Our work advances Dual‐task research by elucidating patterns of whole brain activity for Dual tasks that require Ankle motor control during a cognitive task.
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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".