A meta‐analytic study of changes in brain activation in depression
Bibliographic record
Abstract
OBJECTIVE: A large number of studies with considerably variable methods have been performed to investigate brain regions involved in the pathophysiology of major depressive disorder. The aim of this study was to use a quantitative meta-analytic technique to synthesise the results of much of this research. METHODS: Three separate quantitative meta-analytical studies were conducted using the Activation Likelihood Estimation technique. Analysis was performed on three types of studies: (1) those conducted at rest comparing brain activation in patients with depression and controls; (2) those involving brain changes following antidepressant treatment; and (3) those comparing brain activation patterns induced by the induction of positive or negative emotion in patients with depression compared with controls. RESULTS: There appears to be a complex series of areas of the brain implicated in the pathophysiology of depression although limited overlap was found across imaging paradigms. This included a network of regions including frontal and temporal cortex as well as the insula and cerebellum that are hypoactive in depressed subjects and in which there is increase in activity with treatment. There was a corresponding set of subcortical and limbic regions in which opposite changes were found. CONCLUSIONS: There is limited overlap between the brain regions identified using differing imaging methods. The most consistently identified regions include areas of the anterior cingulate, dorsolateral, medial and inferior prefrontal cortex, insula, superior temporal gyrus, basal ganglia and cerebellum. Further research is required to identify if different imaging methods are identifying complementary networks that are equally involved in the disorder.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".