The Effect of Task-Irrelevant Fearful-Face Distractor on Working Memory Processing in Mild Cognitive Impairment versus Healthy Controls: An Exploratory fMRI Study in Female Participants
Bibliographic record
Abstract
In mild cognitive impairment (MCI), a risk state for Alzheimer's disease, patients have objective cognitive deficits with relatively preserved functioning. fMRI studies have identified anomalies during working memory (WM) processing in individuals with MCI. The effect of task-irrelevant emotional face distractor on WM processing in MCI remains unclear. We aim to explore the impact of fearful-face task-irrelevant distractor on WM processing in MCI using fMRI. Hypothesis. Compared to healthy controls (HC), MCI patients will show significantly higher BOLD signal in a priori identified regions of interest (ROIs) during a WM task with a task-irrelevant emotional face distractor. Methods. 9 right-handed female participants with MCI and 12 matched HC performed a WM task with standardized task-irrelevant fearful versus neutral face distractors randomized and counterbalanced across WM trials. MRI images were acquired during the WM task and BOLD signal was analyzed using statistical parametric mapping (SPM) to identify signal patterns during the task response phase. Results. Task-irrelevant fearful-face distractor resulted in higher activation in the amygdala, anterior cingulate, and frontal areas, in MCI participants compared to HC. Conclusions. This exploratory study suggests altered WM processing as a result of fearful-face distractor in MCI.
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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 | 0.000 | 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".