Altered hippocampal activation during memory encoding in type 2 diabetes
Bibliographic record
Abstract
Type 2 diabetes (T2DM) increases risk for cognitive decline and dementia. While much is known about T2DM‐associated cognitive changes and brain atrophy, the impact of T2DM on brain function is underexplored. Blood oxygenation level dependent (BOLD) signal changes were compared in older adults with T2DM and hypertension (DO, n = 9) and older adults with hypertension only (HO, n = 10) during a cerebrovascular reactivity (CVR) task and a word‐list learning task. We hypothesized that memory deficits and altered BOLD signaling would be apparent in DO compared to HO but that CVR, obtained during a breath‐hold task, would be comparable. There were no group differences in performance on neuropsychological memory tests nor on the word‐list learning task. A region‐of‐interest analysis on hippocampal (HC) activation during word‐list encoding showed stronger right HC activation in the DO compared to the HO group after accounting for CVR (p < .01). Across groups, there was a significant negative correlation between right HC activation and number of words learned during encoding, controlling for performance on the first trial (r = −0.61, p < .01). Collectively these findings suggest that 1) T2DM elevates memory‐related activation of right HC independent of vascular reactivity, and 2) increases in right HC activation parallel decreased learning success and may signal efforts to compensate for cognitive decline (CIHR – MOP111244). Grant Funding Source : Canadian Institutes of Health Research
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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 | 0.000 |
| 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".