Defining Neurocircuits in Depression
Bibliographic record
Abstract
While many effective treatments are available to treat a major depressive episode, no clinical or biological markers identify which patients are likely to respond to a given treatment or explain why one treatment modality or class of medication is effective when another is not. With these clinical issues in mind, this article presents a synthesis of brain changes associated with clinical response to pharmacotherapy, cognitive-behavior therapy (CBT), and deep brain stimulation (DBS), identified using functional neuroimaging, with findings interpreted in the context of a data-driven depression model. ABOUT THE AUTHOR Dr. Mayberg is professor, Departments of Psychiatry and Behavioral Sciences and Neurology, Emory University School of Medicine, Atlanta, GA. Address reprint requests to: Helen Mayberg, MD, Department of Psychiatry, Emory University School of Medicine, 101 Woodruff Circle, WMB 4313, Atlanta GA 30327; or e-mail hmayber@emory.edu. The research described in this article was supports in part by grants from the Canadian Institutes for Health Research, the National Institutes of Health, and the National Alliance for Research in Schizophrenia and Depression. Dr. Mayberg serves as a consultant for Advanced Neuromodulation Systems on deep brain stimulation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".