Increasing Lateralized Motor Activity in Younger and Older Adults using Real-time fMRI during Executed Movements
Bibliographic record
Abstract
Neurofeedback training involves presenting an individual with a representation of their brain activity and instructing them to alter the activity using the feedback. One potential application of neurofeedback is for patients to alter neural activity to improve function. For example, there is evidence that greater laterality of movement-related activity is associated with better motor outcomes after stroke; so using neurofeedback to increase laterality may provide a novel route for improving outcomes. However, we must demonstrate that individuals can control relevant neurofeedback signals. Here, we performed two proof-of-concept studies, one in younger (median age: 26years) and one in older healthy volunteers (median age: 67.5years). The purpose was to determine if participants could manipulate laterality of activity between the motor cortices using real-time fMRI neurofeedback while performing simple hand movements. The younger cohort trained using their left and right hand, the older group trained using their left hand only. In both studies participants in a neurofeedback group were able to achieve more lateralized activity than those in a sham group (younger adults: F(1,23)=4.37, p<0.05; older adults: F(1,15)=9.08, p<0.01). Moreover, the younger cohort was able to maintain the lateralized activity for right hand movements once neurofeedback was removed. The older cohort did not maintain lateralized activity upon feedback removal, with the limitation being that they did not train with their right hand. The results provide evidence that neurofeedback can be used with executed movements to promote lateralized brain activity and thus is amenable for testing as a therapeutic intervention for patients following stroke.
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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".