Intrinsic prefrontal functional connectivity according to cognitive impairment in patients with end-stage renaldisease
Bibliographic record
Abstract
BACKGROUND: This study aimed to investigate differences in intrinsic prefrontal functional connectivity according to the presence of cognitive impairment in patients with end-stage renal disease (ESRD) using functional near-infrared spectroscopy (fNIRS). METHODS: We prospectively enrolled 37 patients with ESRD who had been undergoing hemodialysis for more than 6 months and had no history of neurological or psychiatric disorders. All patients with ESRD underwent the Korean version of the Montreal Cognitive Assessment (MoCA-K) to assess cognitive function. The NIRSIT Lite device (OBELAB Inc.) was used to acquire fNIRS data, and the NIRSIT Lite Analysis Tool program was used to process the data and generate a functional connectivity matrix. We obtained functional connectivity measures by applying graph theory to the connectivity matrix using the BRAPH (brain analysis using graph theory) program. RESULTS: Of the 37 patients with ESRD, 23 had cognitive impairment, whereas 14 patients showed no cognitive impairment. Intrinsic prefrontal functional connectivity was significantly different between groups. Network measures of strength, global efficiency, and mean clustering coefficient were lower in ESRD patients with cognitive impairment than in those without cognitive impairment (4.458 vs. 5.129, p = 0.02; 0.397 vs. 0.437, p = 0.03; and 0.316 vs. 0.421, p = 0.003; respectively). There were no significant correlations between MoCA-K scores and clinical characteristics. CONCLUSION: We demonstrated a significant association between cognitive function and intrinsic prefrontal functional connectivity in patients with ESRD. ESRD patients with cognitive impairment have reduced connectivity and segregation in the prefrontal brain network compared to those without cognitive impairment.
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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 | 0.000 |
| 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".