Gray Matter Volume as Evidence for Cognitive Reserve in Bilinguals With Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Compared with monolinguals, bilinguals have a later onset of mild cognitive impairment (MCI) and Alzheimer disease symptoms and greater neuropathology at similar cognitive and clinical levels. The present study follows a previous report showing the faster conversion from MCI to Alzheimer disease for bilingual patients than comparable monolinguals, as predicted by a cognitive reserve (CR). PURPOSE: Identify whether the increased CR found for bilinguals in the previous study was accompanied by greater gray matter (GM) atrophy than was present for the monolinguals. METHODS: A novel deep-learning technique based on convolutional neural networks was used to enhance clinical scans into 1 mm MPRAGEs and analyze the GM volume at the time of MCI diagnosis in the earlier study. PATIENTS: Twenty-four bilingual and 24 monolingual patients were diagnosed with MCI at a hospital memory clinic. RESULTS: Bilingual patients had more GM loss than monolingual patients in areas related to language processing, attention, decision-making, motor function, and episodic memory retrieval. Bilingualism and age were the strongest predictors of atrophy after other variables such as immigration and education were included in a multivariate model. DISCUSSION: CR from bilingualism is evident in the initial stages of neurodegeneration after MCI has been diagnosed.
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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) | 0.002 | 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".