Prefrontal blood flow and oxygenation measured by NIRS during long-term memory tasks are impaired by acute hyperglycemia (Conference Presentation)
Bibliographic record
Abstract
Our goal was to use 2-channel frequency domain near-infrared spectroscopy (NIRS) to investigate the hemodynamic and metabolic mechanisms underlying hyperglycemia-associated long-term memory impairment. We hypothesized that prefrontal cortex (PFC) oxygen saturation (%Sat) and perfusion (tHb, i.e. total hemoglobin) would decrease due to hyperglycemia during learning, and then increase during recall. During learning, participants’ blood glucose was manipulated with beverages containing either 47.4 mg saccharine control (CON, n = 10), or 50 g dextrose + 23.7 mg saccharine (GLC, n = 10). In the Symbol-Digit Modalities Test (SMDT) participants matched nine symbols to corresponding digits (1-9 inclusive), completing 105 learning and 15 testing trials on day 1 and 15 testing trials on day 2. From learning to recall, CON SMDT performance was unchanged, but GLC SMDT performance was decreased 11% (P = 0.0173). There were significant interactions (2-way ANOVA) between the CON-GLC treatment effects and the learning-recall effects for both PFC perfusion and oxygen saturation. Specifically, comparing learning to recall, CON exhibited no tHb differences but for GLC there was a large tHb decrease during learning with a partial recovery toward CON values during recall (P = 0.0012); and, comparing learning to recall, CON exhibited a large %Sat decrease but GLC exhibited a large %Sat increase (P = 0.021). We speculate that, during learning, after overnight fasting (CON) the PFC demands more hemodynamic and metabolic resources and “works” harder, but with readily available sugar (GLC) the PFC exhibits decreased “effort.”
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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) | 0.002 | 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".