Different effects of dopaminergic medications on subthalamic beta bursts and non-oscillatory fractal components in parkinson’s disease: a longitudinal study
Bibliographic record
Abstract
Despite a variety of different treatment options for major depressive disorder (MDD), many patients do not experience adequate symptom relief.Moving from the standard one-size-fits-all treatment prescription towards stratifying patients to different interventions by means of biomarkers, could aid in increasing clinical remission.We recently developed a clinically implementable and easily interpretable biomarker (Brainmarker-I) based on the individual alpha peak frequency (iAPF) measured during resting-state electroencephalography (EEG) in a large heterogeneous dataset (N¼4249), and conducted blinded out-ofsample validations in two independent samples, successfully predicting remission to different pharmaceutical and non-pharmaceutical interventions of attention-deficit/hyperactivity-disorder.Next, we applied Brainmarker-I to several datasets to predict remission to different MDD treatments including rTMS (10Hz left DLPFC and 1Hz right DLPFC) and pharmaceutical interventions (sertraline, escitalopram, venlafaxine).Positive predictive values (PPVs) were employed to indicate the direction of treatment stratification.Normalized PPVs were calculated to improve comparability of predicted increase in remission rates across datasets.As demonstrated in earlier work, an iAPF close to the stimulation frequency of 10Hz at the site of stimulation best predicted remission to 10Hz rTMS, with an increase in predicted normalized remission rate (normalized PPV) of 24%.A relatively lower iAPF suggested an increased likelihood of remission to sertraline, while individuals with a relatively higher iAPF were more likely to remit to 1Hz rTMS.Escitalopram and venlafaxine were exploratively examined in the same way, and results are discussed.Here we present a transdiagnostic treatment stratification biomarker that is capable of predicting differential treatment outcome in patient subgroups, and that is ready for implementation in clinical practice.Brainmarker-I represents a first step from a one-size-fits-all treatment approach towards personalized psychiatry in depression treatment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".