Microstructural and functional connectivity changes of decision-related brain networks in end-stage kidney disease patients undergoing peritoneal dialysis
Bibliographic record
Abstract
BACKGROUND: We aimed to explore changes in decision-related brain microstructure, brain functional activities, and functional connectivity, and their correlations with cognitive function in end-stage kidney disease (ESKD) patients undergoing peritoneal dialysis (PD). Furthermore, the impact of dialysis on these changes was examined. METHODS: Thirty ESKD patients undergoing PD, 20 predialysis patients with chronic kidney disease (CKD) stage 5 (predialysis CKD stage 5), and 30 healthy controls (HC) were recruited in this study. Various assessments have been conducted, including neuropsychological scale testing, decision-related behavioral tests, and structural and functional magnetic resonance imaging (MRI) scans. RESULTS: Compared to the HC group, PD and predialysis patients performed poorly in the neuropsychology and Iowa Gambling Task (IGT), PD patients showed decreased functional activation and gray matter volume in multiple decision-related brain areas, including the ventromedial prefrontal cortex (vmPFC), orbitofrontal cortex (OFC), and anterior cingulate cortex (ACC). The default mode network (DMN) and salience network (SAN) were the main brain regions with decreased functional connectivity to vmPFC and ACC. Additionally, compared to the predialysis group, PD patients showed enhanced brain activation in decision-related brain regions such as the ACC, vmPFC, and insula. The structure and function variabilities of the vmPFC were correlated with IGT and Montreal Cognitive Assessment score, and the activation of OFC was negatively associated with blood creatinine, cystatin, and parathyroid hormone levels. CONCLUSION: In summary, PD can change the structure and function of decision-related brain circuits (vmPFC-OFC-ACC) and reduce integration within DMN and SAN, which is correlated with cognitive function and clinical features. These suggest that PD can improve patients' cognitive function and disease progression to a certain extent.
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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".