Predictive and mechanistic biomarkers of treatment response to Transcranial Magnetic Stimulation (TMS) in Psychiatric and Neurocognitive Disorders, identified via TMS-Electroencephalography (EEG) and Resting-State EEG: A systematic review
Bibliographic record
Abstract
Electroencephalography (EEG) is a comparatively inexpensive and non-invasive recording technique of neural activity, making it a valuable tool for biomarker discovery in transcranial magnetic stimulation (TMS). This systematic review aimed to examine mechanistic and predictive biomarkers, identified through TMS-EEG or resting-state EEG, of treatment response to TMS in psychiatric and neurocognitive disorders. Nineteen articles were obtained via Embase, APA PsycInfo, MEDLINE, and manual search; conditions included, unipolar depression (k = 13), Alzheimer's disease (k = 3), bipolar depression (k = 2), and schizophrenia (k = 2). Two mechanistic biomarkers were identified: one TMS-EEG marker, reductions in N100 post-dorsolateral prefrontal cortex (DLPFC) repetitive TMS or intermittent theta burst stimulation (iTBS) in unipolar depression (n = 120; k = 2), and one resting-state marker, reductions in theta connectivity post-DLPFC repetitive TMS in unipolar and bipolar depression (n = 89; k = 2). Whereas one predictive TMS-EEG biomarker was isolated: greater baseline N100 was predictive of unipolar depression improvement in DLPFC repetitive TMS and iTBS (n = 113; k = 2). Promising markers were briefly discussed for future research in Alzheimer's disease and schizophrenia. In conclusion, across the psychiatric and neurocognitive disorders considered in this study, TMS-EEG and resting-state mechanistic and predictive biomarkers of depression appear to hold the most promise. Further research is needed to validate the biomarkers identified in depression, to help guide treatment plans and advance precision medicine in psychiatry.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".