The influence of emotion on working memory: Exploratory fMRI findings among men with histories of childhood sexual abuse
Bibliographic record
Abstract
BACKGROUND: Childhood maltreatment can have detrimental consequences on individual well-being and cognitive functioning. One type of childhood maltreatment that remains stigmatized and under-researched among men is child sexual abuse (CSA). Research examining the neurophysiological consequences of CSA in males is limited even further. OBJECTIVE: To provide preliminary insight into the neural basis of the impact of CSA during two working memory tasks. PARTICIPANTS AND SETTING: Men with CSA histories, with and without post-traumatic stress disorder (PTSD; CSA + PTSD n = 7, mean age = 45; CSA-PTSD; n = 9, mean age = 41), and men without a CSA history nor PTSD (n = 13, mean age = 36) participated in the study at a local hospital. METHODS: Participants completed a letter n-back task and an emotional picture n-back task during fMRI to measure working memory and the influence of emotion on working memory. They also completed self-report measures to assess mental health and childhood abuse histories. RESULTS: In the letter n-back task, men with CSA + PTSD had less activation in the cerebellum and left fusiform gyrus compared to CSA-PTSD men. During the working memory task with negative emotional pictures the control group had greater frontal activation, while the CSA-PTSD group had greater limbic activation. Analyses were performed with independent-samples t-tests. CONCLUSIONS: This study provides preliminary empirical evidence of the impact CSA can have on men regarding working memory when negative stimuli are involved. It highlights that CSA, even without a diagnosis of PTSD, can have a significant neurophysiological impact. It also provides clinicians with information to support well-being and help with potential day to day challenges.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".