An Overview of Psychological and Neurobiological Mechanisms by which Early Negative Experiences Increase Risk of Mood Disorders.
Bibliographic record
Abstract
OBJECTIVE: Early life experiences are associated with severe and long-lasting effects on behavioural and emotional functioning, which in turn are thought to increase the risk for unipolar depression and other disorders of affect regulation. The neurobiological and psychological mechanisms through which adverse early life experiences confer risk are poorly understood. METHOD: Alterations in brain structure and function in limbic and prefrontal cortical regions have been linked to early negative experiences and to mood disorders. RESULTS: There are a number of psychological domains that may be dysfunctional in people with mood disorders, and which, if the dysfunction occurs prior to onset of mood symptoms, may signify a risk factor for depression. Cognitive dysfunction has been examined in patients with mood disorders, with some suggestion that changes in cognitive function may antedate the onset of mood symptoms, and may be exacerbated in those who experienced early negative trauma. Social cognition, including emotion comprehension, theory of mind and empathy, represent under-studied domains of psychological function that may be negatively influenced by early adverse experience. Temperament and personality factors may also leave people vulnerable to mood instability. CONCLUSION: This review summarizes the evidence for dysfunction in each of these domains for people with mood disorders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".