Combination of rs‐fMRI, QSM, and ASL Reveals the Cerebral Neurovascular Coupling Dysfunction Is Associated With Cognitive Decline in Patients With Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: Neurovascular coupling (NVC) reflects the close connection between neural activity and cerebral blood flow (CBF) responses, providing new insights to explore the neuropathological mechanisms of various diseases. Non-dialysis patients with chronic kidney disease (CKD) exhibit cognitive decline, but the underlying pathological mechanisms are unclear. METHODS: The prospective study involved 53 patients with stage 1-3a CKD (CKD1-3a), 78 patients with stage 3b-5 CKD (CKD3b-5), and 52 healthy controls (HC). Our investigation involved voxel-based assessments of both global and regional BOLD signal characteristics. Additionally, we explored the correlations between neuroimaging indices, Montreal Cognitive Assessment (MoCA) scores, and clinical laboratory findings. RESULTS: Compared to HC, the CKD3b-5 and CKD1-3a groups exhibited lower ALLF and ReHo in the default mode network (DMN), higher CBF in bilateral hippocampus (HIP), higher susceptibility values in bilateral caudate nucleus (CAU) and putamen (PUT), and lower susceptibility values in bilateral HIP. At the global level, the coupling coefficients were lower in CKD1-3a and CKD3b-5 groups than in HC. At the ROI level, the CBF-ALFF and CBF-ReHo coupling in HIP and basal ganglia regions were lower in CKD3b-5 groups than in the CKD1-3a group. Most importantly, susceptibility-ALFF in ANG.R may mediate the effects of phosphorus on cognitive decompensation in patients with CKD1-3a. CONCLUSIONS: Non-dialysis patients with CKD exhibit abnormal NCV, which is associated with the cognitive decline. Specifically, the susceptibility-ALFF may serve as a valuable biomarker for early assessment of cognitive decline in CKD, offering insights into the pathogenesis of cognitive decline in CKD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".