Autobiographical Memory Disturbances in Depression: A Novel Therapeutic Target?
Bibliographic record
Abstract
Major depressive disorder (MDD) is characterized by a dysfunctional processing of autobiographical memories. We review the following core domains of deficit: systematic biases favoring materials of negative emotional valence; diminished access and response to positive memories; a recollection of overgeneral memories in detriment of specific autobiographical memories; and the role of ruminative processes and avoidance when dealing with autobiographical memories. Furthermore, we review evidence from functional neuroimaging studies of neural circuits activated by the recollection of autobiographical memories in both healthy and depressive individuals. Disruptions in autobiographical memories predispose and portend onset and maintenance of depression. Thus, we discuss emerging therapeutics that target memory difficulties in those with depression. We review strategies for this clinical domain, including memory specificity training, method-of-loci, memory rescripting, and real-time fMRI neurofeedback training of amygdala activity in depression. We propose that the manipulation of the reconsolidation of autobiographical memories in depression might represent a novel yet largely unexplored, domain-specific, therapeutic opportunity for depression treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".