Training-related brain plasticity in subjects at risk of developing Alzheimer’s disease
Bibliographic record
Abstract
Subjects with mild cognitive impairment are at risk of developing Alzheimer's disease. Cognitive stimulation is an emerging intervention in the field of neurology and allied sciences, having already been shown to improve cognition in subjects with mild cognitive impairment. Yet no studies have attempted to unravel the brain mechanisms that support such improvement. This study uses functional magnetic resonance imaging to measure the effect of memory training on brain activation in older adults with mild cognitive impairment and to assess whether it can reverse the brain changes associated with mild cognitive impairment. Brain activation associated with verbal encoding and retrieval was recorded twice prior to training and once after training. In subjects with mild cognitive impairment, increased activation was found after training within a large network that included the frontal, temporal and parietal areas. Healthy controls showed mostly areas of decreased activation following training. Comparison with pre-training indicated that subjects with mild cognitive impairment used a combination of specialized areas; that is, areas activated prior to training and new alternative areas activated following training. However, only activation of the right inferior parietal lobule, a new area of activation, correlated with performance. Furthermore, the differences between the brain activation patterns of subjects with mild cognitive impairment and those of healthy controls were attenuated by training in a number of brain regions. These results indicate that memory training can result in significant neural changes that are measurable with brain imaging. They also show that the brains of people with mild cognitive impairment remain highly plastic.
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 | 0.000 | 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) | 0.001 | 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".