Investigating neuroimaging differences by cognitive and metabolic status in a community‐dwelling cohort
Bibliographic record
Abstract
Abstract Background Little is know about how metabolic function relates to late‐life physical and cognitive health. In a community‐dwelling cohort of older adults with normal cognition (CN) and mild cognitive impairment (MCI), we investigated whether cognitive and metabolic status predicted neuroimaging biomarkers, and cognitive and physical function. Method Participants enrolled in the Wake Forest Alzheimer’s Disease Research Center (ADRC) Clinical Core cohort received neuropsychological assessment, MR imaging (T1, FLAIR, DTI, NODDI, pcASL), and PiB PET imaging on a subset. Participants were adjudicated as CN or MCI using NIA‐AA criteria. T1 MRI were processed (FreeSurfer v5.3) to calculate “AD‐signature” meta‐ROI cortical thickness. A set of AD‐sensitive cortical ROIs was used to extract mean PET SUVR signal. White matter hyperintensity (WMH) volume was calculated on FLAIR with LST in SPM12. DTI were processed using FSL, and NODDI using AMICO, to generate mean white matter (WM) Fractional Anisotropy (FA) and Free Water (FW), to assess WM microstructure. PcASL were processed to yield mean GM and WM cerebral blood flow (CBF). Cognitive measures included Montreal Cognitive Assessment (MoCA) and Free and Cued Selective Reminding Test (total free recall; FCSRT). Mobility function was assessed using the expanded Short Physical Performance Battery (eSPPB). Oral Glucose Tolerance Testing (OGTT) assessed blood glucose 120 minutes post‐challenge; OGTT was thresholded (>=140 mg/dl) to classify participants with impaired glucose tolerance (IGT) or normoglycemic (NG). We assessed group differences using t‐tests and multivariable linear regression (covariates: age, sex, race). Result Among 391 participants with both MRI and cognitive testing, MCI participants were older, less educated and had fewer females relative to CN (Table 1). IGT participants were older and less educated than NG participants. MCI participants had lower cortical thickness, higher WMH volume and FW, and higher PiB than CN (Table 2). MCI participants had lower MOCA and FCSRT (as expected) and eSPPB scores than CN, which remained significant in fully adjusted models. GM CBF differences did not survive adjustment. IGT was only associated with lower eSPPB score (p=.00004; adjusted p=.0051). Conclusion Cognitive impairment is associated with reduced brain health and physical function, while IGT relates more specifically with reduced physical function.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".