High-definition transcranial direct current stimulation modulation of brain network connectivity in patients with mild cognitive impairment
Bibliographic record
Abstract
BACKGROUND: High-definition transcranial direct current stimulation (HD-tDCS) enhances cognitive function, but its mechanisms and neural basis in mild cognitive impairment (MCI) are unclear. This study investigated whether HD-tDCS modulates cognition in MCI patients and correlates with resting-state functional MRI (rs-fMRI) measured changes in spontaneous brain activity. METHODS: Forty-three patients with MCI were randomized to receive 10 sessions of active HD-tDCS targeting the left dorsolateral prefrontal cortex or sham stimulation. rs-fMRI assessed degree centrality (DC) changes before and after the intervention. Cognitive function was evaluated using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Paired t-tests, independent t-tests, and analysis of variance were used to analyze DC differences and group-by-time interactions, with age, gender, education, and head motion as covariates. RESULTS: The HD-tDCS group exhibited significant DC increases in the cerebellum, right inferior temporal gyrus, left middle temporal gyrus, right precentral gyrus, and left dorsolateral superior frontal gyrus, with decreases in the left operculum inferior frontal gyrus, left angular gyrus, left superior parietal gyrus, and right superior occipital gyrus (P < 0.05, AlphaSim corrected). Sham stimulation induced minimal DC changes. No significant MMSE/MoCA improvements occurred in either group (P > 0.05). CONCLUSION: HD-tDCS selectively modulates key nodes of cognitive and motor networks in MCI, as demonstrated by targeted DC alterations. Despite the absence of MMSE/MoCA improvements, this network-specific neuromodulation indicates HD-tDCS engages disease-relevant functional circuits. Longer interventions and sensitive cognitive metrics may clarify clinical relevance.
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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 | 0.000 |
| 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) | 0.001 | 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".