Generalized learning induced by training and tDCS is predicted by prefrontal cortical morphology
Bibliographic record
Abstract
Brain stimulation shows promise as an intervention to enhance executive function, particularly when paired with cognitive training. To optimize such approaches, we must understand the potential role of individual differences in intervention outcomes. We investigated the combined effects of multi-session multitasking training and prefrontal transcranial direct current stimulation (tDCS) on generalization of performance benefits, focusing on how cortical morphology predicts performance improvements. One hundred seventy-eight individuals underwent 7 Tesla MRI before completing multisession training with online stimulation. A cognitive task battery assessed improvements in trained and untrained tasks pre- and post-training. Stimulating the left or right prefrontal cortex at 1 mA during multitasking training enhanced transfer to a visual search task. Critically, cortical morphology predicted stimulation efficacy for inducing transfer. Cortical thickness in regions beneath the stimulating anode was related to reaction time changes in the most difficult visual search condition but only for the left and right 1 mA multitasking training groups. Performance was not related to cortical thickness for the groups receiving sham stimulation, 2 mA stimulation, or 1 mA stimulation with a control training task. These results highlight the importance of individual anatomical differences in modulating tDCS efficacy and identifying specific neuroanatomical features that predict generalized performance gains from combining tDCS with cognitive training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".