Preliminary evidence for high-definition transcranial direct current stimulation effects on white matter microstructure and executive function in mild cognitive impairment
Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment (MCI) represents a critical window for intervention before Alzheimer's disease progression. This study investigated whether high-definition transcranial direct current stimulation (HD-tDCS) targeting the left dorsolateral prefrontal cortex (L-DLPFC) could modulate white matter microstructure and thereby influence cognitive function. METHODS: Twenty-four patients with MCI received 10 sessions of active HD-tDCS over the L-DLPFC. White matter integrity was assessed using diffusion tensor imaging (DTI) to quantify fractional anisotropy in corticospinal tracts (CSTs) and anterior thalamic radiations (ATR). Cognitive function was evaluated with the trail making test B (TMT-B), mini-mental state examination (MMSE), and Montreal cognitive assessment (MoCA) at baseline and postintervention. Forty healthy controls provided baseline DTI data. RESULTS: At baseline, patients with MCI showed significantly reduced fractional anisotropy in the bilateral CST and ATR compared with healthy controls. Following HD-tDCS, increases in fractional anisotropy were observed in these tracts. While MMSE and MoCA scores showed no significant change, TMT-B performance appeared to improve. Notably, increased fractional anisotropy in the left ATR showed an association with improved TMT-B performance (r = 0.467, P < 0.05). CONCLUSION: The findings suggest that HD-tDCS targeting the L-DLPFC may promote microstructural remodeling in white matter tracts, evidenced by elevated fractional anisotropy within the corticospinal and anterior thalamic pathways. While global cognitive measures remained stable, a trend toward improved executive function (TMT-B) was observed, potentially associated with left ATR fractional anisotropy enhancement. This positions HD-tDCS as a candidate neuromodulatory intervention for MCI, warranting further investigation to confirm functional 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 | 0.000 | 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) | 0.001 | 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".