CO2 cerebrovascular reactivity measured with phase‐contrast MRI: A potential biomarker of cognition and physical function in older adults
Bibliographic record
Abstract
Abstract Background Vascular cognitive decline is a prominent cause of late‐life cognitive impairment. Compared to Alzheimer’s disease(AD), there is a paucity of biomarkers for its diagnosis, stratification, and treatment monitoring. Cerebrovascular‐Reactivity(CVR) MRI measures small‐vessel dilation to vasoactive‐stimuli(e.g. CO2), and has been shown to be associated with cognitive impairment. However, previous CVR studies were based on Blood‐Oxygenation‐Level‐Dependent (BOLD) MRI signal, which reflects a complex interplay of many physiological parameters and thus presents interpretation challenges. We measured CVR using quantitative CBF‐imaging in older participants, and tested for associations with diagnosis (Healthy Control [HC] vs. mild‐cognitive‐impairment [MCI] vs. dementia) cognitive and physical function, amyloid and tau burden, and vascular risk. Methods A cross‐sectional study enrolled 67 participants aged 69±6.5 years (22 HC, 37 MCI, 8 dementia). MoCA (Montreal‐Cognitive‐Assessment) and composite cognition (a z‐score average of 4‐domains: verbal‐memory, executive‐function, language, processing‐speed) assessed overall cognition. Gait(sec,4‐meter‐walk) and chair‐stands (sec,5‐chair‐stands) assessed physical‐function. Clinical Dementia Rating(CDR)7 indexed disease‐severity. Phase‐contrast flow MRI(3T) was performed while participants breathed room‐air for 1‐minute, followed by ‘CO2‐enriched‐air’(5%CO2, 21%O2, 74%N2) for 2‐minutes. Blood‐flux at Superior‐Sagittal‐Sinus(SSS)(Figure‐1) was quantified for both states. Breathing rate and End‐tidal(Et)‐CO2 were recorded via capnography. CBF‐based CVR was then obtained by: CBF‐CVR = [HypercapniaSSSflux(ml/min)‐RoomairSSSflux(ml/min)]/RoomairSSSflux(ml/min) HypercapniaEtCo2(mmHg)‐RoomairEtCo2(mmHg) FLAIR‐MRI‐images were assigned Fazekas‐scores by a neuroradiologist. AD‐Biomarkers Aβ40, Aβ42, tau, and p‐tau181(pg/ml), were measured in cerebrospinal fluid (CSF) and vascular‐biomarkers HbA1c (mg/dL), homocysteine (umol/l), HDL(mg/dL), and LDL (mg/dL) in blood. Vascular‐Risk‐Score (VRS) is a composite score based on the presence of hypertension, hypercholesterolemia, diabetes, smoking or obesity (body mass index>30 kg/m2). Results Table‐1 summarizes participant‐demographics. CBF‐CVR (%/mmHg) differentiated diagnostic‐categories, and was lower in impaired vs. HC (p=0.027, Figure‐2a). Higher CBF‐CVR predicted better cognitive‐performance [MoCA(p=0.000089) (Figure‐2b), composite‐cognition (p=0.007) and language (p=0.000064)], physical function [faster gait(p=0.001), chair‐stands(p=0.021)], and disease‐severity [global‐CDR (p=0.003), CDR‐sum‐of‐boxes (p=0.001)(Table‐2). These relationships remained after adjusting for AD‐biomarkers. CBF‐CVR remained associated with cognition and physical‐function except chair‐stands, after adjusting for vascular‐markers: WMH‐Fazekas‐score, VRS, and whole‐brain‐CBF(Table‐2). CBF‐CVR was associated with HbA1c(p=0.007) and history of Diabetes (p=0.043), but not other blood biomarkers or VRS. Conclusion We show that a 3‐minute CBF‐based CVR MRI can differentiate diagnostic‐categories, and predict cognitive and physical function, independent of AD pathology.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".