Poor Outcomes of Patients With NAFLD and Moderate Renal Dysfunction or Short-Term Dialysis Receiving a Liver Transplant Alone.

This study aimed to characterize alterations in functional brain network organization in miners with approximately ten years of occupational exposure to extreme working conditions and shift work, using connectivity metrics derived from resting-state EEG recorded during both eyes-open (EO) and eyes-closed (EC) conditions. Directed Transfer Function (DTF), the imaginary part of Coherence, and the weighted Phase Lag Index were computed from non-overlapping 6-s epochs following two preprocessing pipelines: Independent Component Analysis and Artifact Subspace Reconstruction (ASR). Analyses were conducted for both miners (19 men, mean age 36.52 ± 5.08 years) and matched controls (19 men, mean age 35.42 ± 5.04 years). DTF demonstrated consistently excellent reliability (Intraclass Correlation Coefficients ≥ 0.75) and revealed significant group differences across all frequency bands when combined with ASR, as determined by linear mixed-effects models with false discovery rate correction (pc < 0.05), underscoring its high reproducibility. Specifically, miners exhibited reduced network segregation and integration, reflected by decreases in modularity (Q), global efficiency (GE), local efficiency (LE), clustering coefficient (CC), and transitivity (T) in the delta and theta bands, as well as reduced network resilience (R) at higher frequencies in the EC condition. Within the miner group, higher GE was associated with poorer executive function and slower processing speed, as measured by Trail Making Test subcomponents (0.51 ≤ r ≤ 0.71; 0.0008 ≤ pc ≤ 0.029). In addition, lower-frequency network metrics (CC, LE, T, and R) showed significant negative correlations with verbal recall performance (- 0.70 ≤ r ≤ - 0.54; 0.0009 ≤ pc ≤ 0.039). Collectively, these findings indicate that chronic occupational exposure disrupts the stability and large-scale organization of functional brain networks, resulting in reduced network efficiency and a decoupling between neural connectivity and cognitive performance. From a methodological perspective, the combination of DTF and ASR emerged as the most reliable approach for resting-state EEG connectivity analysis.