Neurobiological Factors Linking Personality Traits and Major Depression
Bibliographic record
Abstract
OBJECTIVE: To examine the neurobiological basis of personality and depression. METHOD: We examined preclinical and clinical studies related to neuroanatomy, neuroendocrine, molecular, and genetic alterations in depressed patients. We considered whether common neurobiological factors might be shared between personality and depression. RESULTS: Preclinical studies provide insights into the neurobiological mechanisms underlying the pathophysiology of depression including neuroendocrine alterations in hypothalamic-pituitary-adrenal (HPA) function, neuroanatomical alterations in key brain regions, and alterations in neurotrophin and serotonergic signalling systems. Clinical studies show similar alterations in depressed patients. Evidence suggests that neuroendocrine alterations in HPA function may contribute to personality traits. Brain regions implicated in depression, including the hippocampus and the anterior cingulate cortex, might play a role in personality. Key molecules implicated in depression have been extensively studied with reference to personality traits, particularly neuroticism. To date, physiological measures (serum and positron emission tomography) provide the strongest evidence implicating brain-derived neurotrophic factor and serotonin in personality, while genetic evidence is less convincing. CONCLUSIONS: A neurobiological link exists between personality and depression; however, more work is needed to provide an understanding of the nature of this relation and to link this work with clinical studies examining the influence of personality factors on depression.
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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) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".