Standardized EEG for multi-site biomarker-informed trials: Implementation in the Canadian Biomarker Integration Network in Depression
Bibliographic record
Abstract
OBJECTIVES: To standardize EEG practices in research and facilitate the transition to multi-site biomarker-informed clinical trials, with the aim of enhancing the treatment of depression and other neuropsychiatric disorders. METHODS: This study details the approaches employed by the Canadian Biomarker Integration Network in Depression (CAN-BIND), a collaborative, multi-site network supported by the Ontario Brain Institute and Brain Canada, focused on enhancing depression care. To achieve our objective, we implemented strategies to reduce variability across CAN-BIND sites participating in EEG data collection. RESULTS: We implemented standardization solutions in three key areas: infrastructure, procedures, and toolboxes. As part of this initiative, we developed two novel toolboxes designed to automatically clean EEG data and ensure that high-quality standards are met prior to biomarker extraction, enhancing the homogeneity of collected EEG data. CONCLUSIONS: Achieving standardization of EEG procedures in multi-site studies is essential for the successful implementation of biomarker-informed clinical trials. SIGNIFICANCE: Our study offers comprehensive solutions for EEG practices across multiple sites, providing valuable insights and inspiration for establishing standardization approaches in collaborative neuroimaging efforts beyond depression research.
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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 | 0.109 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".