Effect of Disease Severity on Neural Compensation of Item and Associative Recognition in Mild Cognitive Impairment
Bibliographic record
Abstract
It is proposed that the prodromal phase of Alzheimer's disease is associated with additional brain activation in key regions involved in memory, reflecting compensatory brain plasticity. Very little is known, however, about the evolution of these compensatory mechanisms as the brain acquires more damages. We conducted an fMRI memory study measuring brain activation related to old/new (item recognition) and intact/rearranged (associative recognition) word-pair recognition paradigms in 26 persons with mild cognitive impairment (MCI) and 14 healthy older adults. The Mattis Dementia Rating Scale was used to divide persons with MCI into those with higher and lower cognitive performances. Results indicated more brain activation in MCIs than in controls but disease severity determined which cognitive process was associated with larger activation: Persons with less severe MCI showed hyperactivation during associative recognition only, whereas persons with more severe MCI showed hyperactivation during item recognition only. These hyperactivations were found mainly in brain areas that are typically associated with retrieval mode (e.g., bilateral prefrontal cortex). These findings indicate that neural plasticity occurs during the entire MCI phase but that it is associated with different cognitive components. As they progress in the disease, individuals with MCI will experience a breakdown in the compensatory mechanisms for associative recognition accompanied by emergence of compensatory mechanisms for item recognition.
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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 | 0.002 |
| 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) | 0.001 | 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".