Differential pattern of fMRI signal in patients with Mild Cognitive Impairment compared to healthy controls during working memory tasks involving a task‐irrelevant fearful face distracter
Bibliographic record
Abstract
Mild cognitive impairment (MCI) represents a transitional stage between normal age‐related cognitive changes and mild dementia. Patients with MCI show neurodegenerative changes that lead to modification of many fundamental neural pathways, including those involved in processing emotion and memory. To date, the literature on these networks in this patient population is very limited. A block‐design, delayed match to sample paradigm with task‐irrelevant emotional distracter was used and presented in a 3‐Tesla MRI scanner. Women, ages 55–85, MCI or healthy controls (HC) were presented with “what” and “where” information at low or high‐item loading with task‐irrelevant fearful or neutral faces. The change in blood oxygen level dependent (BOLD) signal was analyzed using SPM8 and two‐tailed t‐tests were used to compare different combinations of loading and emotional valence factors between groups. Despite comparable behavioral data between groups, MCI patients showed significant differential patterns of activation during working memory (WM) tasks when compared to HC. This pattern was different across information type, loading and emotional valence. Emotional distracters modified this pattern depending on the type of information presented. This work highlights changes in WM and emotional networks in MCI patients and is promising as an early detection tool for Alzheimer disease. Lawson Health Research Institute, London, ON Grant Funding Source : Internal Funding
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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 | 0.001 | 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) | 0.003 | 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".