Diagnostic and therapeutic utility of neuroimaging in depression: an overview
Bibliographic record
Abstract
A growing number of studies have used neuroimaging to further our understanding of how brain structure and function are altered in major depression. More recently, these techniques have begun to show promise for the diagnosis and treatment of depression, both as aids to conventional methods and as methods in their own right. In this review, we describe recent neuroimaging findings in the field that might aid diagnosis and improve treatment accuracy. Overall, major depression is associated with numerous structural and functional differences in neural systems involved in emotion processing and mood regulation. Furthermore, several studies have shown that the structure and function of these systems is changed by pharmacological and psychological treatments of the condition and that these changes in candidate brain regions might predict clinical response. More recently, "machine learning" methods have used neuroimaging data to categorize individual patients according to their diagnostic status and predict treatment response. Despite being mostly limited to group-level comparisons at present, with the introduction of new methods and more naturalistic studies, neuroimaging has the potential to become part of the clinical armamentarium and may improve diagnostic accuracy and inform treatment choice at the patient level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".