Functional Brain Changes Following Cognitive and Motor Skills Training
Bibliographic record
Abstract
BACKGROUND: Functional neuroimaging is increasingly used in rehabilitation research to map the neural mechanisms subserving training targets. These data can inform intervention design and improve evaluation of treatment outcomes. Reliable neural markers may provide standard metrics of treatment impact and allow consideration of behavioral outcomes in the context of functional brain changes. OBJECTIVE: To identify common patterns of functional brain changes associated with training across a diverse range of intervention protocols. Reliable brain changes could inform development of candidate neural markers to guide intervention research. METHODS: Taking a quantitative meta-analytic approach, we review the functional neuroimaging studies of cognitive and motor skills training interventions in healthy young adults (N = 38). RESULTS: Reliable decreases in functional brain activity from pretraining to posttraining were observed in brain regions commonly associated with cognitive control processes, including lateral prefrontal, left anterior inferior parietal lobule, and dorsal anterior cingulate cortex. Training-related increases were observed in the medial prefrontal cortex and posterior cingulate and angular gyrus, core regions of the default network. Activity within the subcortical striatum also showed reliable increases pretraining to posttraining. CONCLUSIONS: These data suggest that altered engagement of large-scale, spatially distributed cortical brain networks and subcortical striatal brain regions may serve as candidate neural markers of training interventions. The development of reliable metrics based on activity and functional connectivity among large-scale brain networks may prove fruitful in identifying interactions between domain-general and -specific changes in brain activity that affect behavioral outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".