S2‐01‐03: Functional Neuroimaging in Trials of Cognition‐Focused Interventions
Bibliographic record
Abstract
Functional neuroimaging techniques are increasingly used to assess the neuroplastic compensatory processes induced by cognition focused interventions. This conference will present studies investigating the effect of attention and/or memory training on task-related brain activation measured with functional magnetic resonance imaging (fMRI) in older adults with or without mild cognitive impairment. The studies will also assess whether training format and individual characteristics of the participants (here education) modify the pattern of activation changes. Cognitive training improves objective and subjective measures of cognition. In parallel, fMRI shows an increase in task-related activation following training, particularly when the intervention involves the explicit learning of new strategies and the implementation of metacognitive processes. Many of the training-induced neural changes are found in alternative, functionnally intact brain regions. However, we also found evidence for increased activation in regions that are typically impaired in this population. This suggests that training can also induce restoration. Both training format and education substantially modify the pattern of brain changes observed following training. These results show that the brain remains highly plastic in aging and during the prodrome of Alzheimer’s disease and that functional brain imaging can be used to reveal the neural mechanisms of cognitive training. The data will be related to the INTERACTIVE model which suggests that training-induced brain activation varies as a function of a range of subject-related and training-related dimensions.
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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 | 0.040 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".