Volumetric neuroimaging investigations in mood disorders: bipolar disorder versus major depressive disorder
Bibliographic record
Abstract
BACKGROUND: As patients with mood disorders manifest heterogeneity in phenomenology, pathophysiology, etiology, and treatment response, a biological classification of mental disease is urgently needed to advance research. Patient and methodological variability complicates the comparison of neuroimaging study results and limits heuristic model development and a biologically-based diagnostic schema. OBJECTIVE: We have critically reviewed and compared the magnetic resonance neuroimaging literature to determine the degree and directionality of volumetric changes in brain regions putatively implicated in the pathophysiology of major depressive disorder (MDD) versus bipolar disorder (BD). METHODS: A total of 140 published magnetic resonance imaging investigations evaluating subjects with BD or MDD were selected to provide a summary and interpretation of volumetric neuroimaging results in MDD and BD. Further commentary on the pathophysiological implications, and putative cellular and pharmacological mechanisms, is also provided. RESULTS: While whole brain volumes of patients with mood disorders do not differ from those of healthy controls, regional deficits in the frontal lobe, particularly in the anterior cingulate and the orbitofrontal cortex, appear to consistently differentiate subjects with mood disorders from the general population. Preliminary findings also suggest that subcortical structures, particularly the striatum, amygdala, and hippocampus, may be differentially affected in MDD and BD. CONCLUSIONS: Structural neuroimaging studies have consistently identified regional abnormalities in subjects with mood disorders. Future studies should strive to definitively establish the influence of age and medication.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".