Predictive Neuroimaging Markers of Psychotherapy Response: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Predictive neuroimaging markers of treatment response are increasingly sought in order to inform the treatment of major depressive and anxiety disorders. We review the existing literature regarding candidate predictive neuroimaging markers of psychotherapy response and assess their potential clinical utility. METHODS: We searched Embase, PsycINFO, and PubMed up to October 2014 for studies correlating pretreatment neuroimaging parameters with psychotherapy response in major depressive and anxiety disorders. Our search yielded 40 eligible studies. RESULTS: The anterior cingulate cortex, amygdala, and anterior insula emerged as potential markers in major depressive disorder and some anxiety disorders. Results across studies displayed a large degree of variability, however, and to date the findings have not been systematically validated in independent clinical cohorts and have not been shown capable of distinguishing between medication and psychotherapy responders. Also limited is the examination of how neuroimaging compares or might add to other prognostic clinical variables. CONCLUSION: While the extant data suggest avenues of further investigation, we are still far from being able to use these markers clinically. Future studies need to focus on longitudinal testing of potential markers, determining their prescriptive value and examining how they might be integrated with clinical factors.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".