Assessment of anhedonia in psychological trauma: psychometric and neuroimaging perspectives
Bibliographic record
Abstract
Symptoms of anhedonia, or deficits in the ability to experience positive affect, are increasingly recognized as an outcome of traumatic stress including in individuals with PTSD. However, little research has investigated negative affective responses to what would normally be considered pleasant events (e.g., receiving a compliment or gift, physical affection) in traumatized persons. We demonstrate not only self-reported decreased positive affect but also increased negative affect in response to positive events in 55 women with PTSD, in comparison with 35 women without PTSD, via their response to a Hedonic Deficit & Interference Scale (HDIS). The HDIS demonstrated strong internal validity, convergent and incremental validity relative to other measures of anhedonia, and discriminant validity in relation to depression versus anxiety symptoms in this sample. In addition, in response to imagery of social versus non-social positive events, HDIS scores predicted self-report positive and negative affective responses. In a sub-sample of participants completing the imagery task while undergoing fMRI (n=12), HDIS scores also predicted BOLD response within the left orbitofrontal cortex, ventromedial prefrontal cortex, amygdala, and cerebellum. Future research and clinical directions are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".