Genetics of Response to <scp>ECT</scp>, <scp>TMS</scp>, Ketamine and Esketamine
Bibliographic record
Abstract
Treatment-resistant mood disorders are often managed with intensive interventions that include electroconvulsive therapy (ECT), transcranial magnetic stimulation (TMS), ketamine, and esketamine, but the role of genetics in clinical response to those interventions is yet to be clearly determined. Here, we review the current literature on the genetics of response to these treatment modalities. To date, the limited number of studies done to investigate genetic predictors of treatment response have primarily focused on single variants in candidate genes, and none of these have been consistently reproducible. The majority of candidate gene studies examine the effect of variants in the COMT and BDNF genes on treatment response. There are a limited number of genome-wide association studies (GWAS) looking at treatment response, though they are almost all underpowered, with only one study including a sample size > 1000. As a result, there have been few single nucleotide polymorphisms (SNPs) found to be associated with treatment response at a statistically significant level, all in genes other than COMT and BDNF. The challenge is now to generate data from a large group of patients undergoing these therapies in order to more robustly assess the genetic factors affecting treatment response. This will not only help establish genetic predictors of response, but also potentially develop differential predictors of response to available treatments, which could provide clinicians with critical information to aid in deciding which treatment modality to recommend for treatment-resistant depression. We are currently pursuing such a strategy in our 50-site worldwide Gen-ECT-ic consortium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".