Home-based transcranial direct current stimulation (tDCS) in major depressive disorder: Enhanced network synchronization with active relative to sham and deep learning-based predictors of remission
Bibliographic record
Abstract
Aim To investigate neural oscillatory networks in major depressive disorder (MDD), effects of home-based transcranial direct current stimulation (tDCS) treatment, and predictors of treatment remission. Methods In a randomized controlled trial, EEG data were acquired from 21 MDD participants (16 women, mean age 36.63 ± 9.71 years) with moderate to severe depressive episodes (mean HAMD score 18.42 ± 1.80). Participants were randomized to active (n = 11) or sham tDCS (n = 8). Home-based tDCS treatment was administered for 10 weeks, with 5 sessions per week for 3 weeks, then 3 sessions per week for 7 weeks. Active tDCS was 2 mA, and sham tDCS was 0 mA with brief ramp-up/down periods. Clinical remission was defined as HAMD score ≤ 7. Resting-state EEG data were collected at baseline and at the 10-week end of treatment using a portable 4-channel EEG device. EEG band power and functional connectivity (phase locking value, PLV) were analyzed. Deep learning identified predictors of treatment remission from baseline PLV features. Results The active tDCS group showed higher gamma PLV in frontal and temporal regions compared to the sham group. Positive correlations between changes in delta, theta, alpha, and beta PLV and depression improvement were observed in the active group. Combining PLV features from theta, alpha, and beta achieved the highest treatment remission prediction accuracy: 71.94 % (sensitivity 52.88 %, specificity 83.06 %). Conclusions Synchronized brain activity in gamma PLV may be a mechanism of active tDCS. Baseline resting-state EEG could predict treatment remission. Home-based EEG measures are feasible and useful predictors of clinical outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".