Multimodal functional neuroimaging of hippocampal engagement in cognitively normal older individuals
Bibliographic record
Abstract
BACKGROUND: Hippocampal hyperactivation in fMRI has been observed in individuals at risk for Alzheimer's Disease (AD), both those with normal cognition (NC) and those with mild cognitive impairment (MCI). This hyperactivity may serve as a biomarker for identifying at-risk individuals, as well as a promoter of tau pathology, thus offering the potential to delay or avert cognitive decline through early interventions. Multimodal neuroimaging approaches are essential to better understand hippocampal hyperactivity. fMRI provides indirect hemodynamic measures of neural activity with high spatial resolution, whereas magnetoencephalography (MEG) is a more direct indicator with high temporal resolution. We compared fMRI and MEG responses in older individuals, with no risk factors for AD, to examine the relationship between these measures of hippocampal episodic memory engagement and provide insights into hyperactivity mechanisms to inform future interventions. METHODS: =70±7 [range=58-81] years; 14 females) underwent fMRI (pattern separation task) and MEG (repetition suppression task). In the bilateral hippocampus, we calculated per subject beta coefficients for critical conditions (similar lures in fMRI, repeated stimuli in MEG). Additionally, we calculated the lure discrimination index (LDI) to indicate episodic memory performance, then utilized robust regression to test relationships between fMRI, MEG, and LDI. RESULTS: = 0.15, F(1,19)=3.27, p = 0.087). CONCLUSIONS: The importance of these results is multifaceted. First, we defined a direct neural read-out of hippocampal activity in MEG that can index memory processes known to be affected in AD pathology. Second, we demonstrated the relationship between two complementary measures of hippocampal engagement (fMRI pattern separation, MEG repetition suppression) and their association to lure discrimination performance. Third, we established a normative distribution of both putative biomarkers that can be used for participant selection in future intervention trials.
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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 | 0.000 | 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".