Neuroimaging Markers of Cellular Function in Major Depressive Disorder: Implications for Therapeutics, Personalized Medicine, and Prevention
Bibliographic record
Abstract
It is estimated that 15% of all individuals will experience a major depressive episode (MDE) during their lifetime and that treatment response is inadequate in 40% of these cases. To address this, neuroimaging is being used to identify MDE subtypes and mechanisms of onset as well as to optimize target occupancy of novel treatments. Neuroimaging of monoamine oxidase-A (MAO-A) binding; glutamate levels; indexes of 5-HT(2A), 5-HTT, 5-HT(1A), and 5-HT(1B) receptors; levels of dopamine transporters D(1) and D(2); and hippocampal volume are described here. Three themes emerge. First, symptoms such as pessimism, motor retardation, anxiety disorder, and verbal memory deficits best indicate the subtype of depression. Second, measures related to mechanisms of monoamine loss, particularly elevated MAO-A binding in prefrontal and anterior cingulate cortex, are present in MDE and in high-risk states for MDE. Third, clinical trials show a consistent 80% 5-HTT occupancy of selective serotonin reuptake inhibitors at doses sufficient to distinguish from placebo in clinical trials (although in vitro affinities vary 100-fold), thereby supporting the need for further occupancy studies to accelerate therapeutic development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".