Identification of minimal hepatic encephalopathy based on dynamic functional connectivity

To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic without any (noHE) and healthy controls (HCs). Resting-state MRI data were acquired 62 with cirrhosis (MHE, n?=?30; noHE, n?=?32) 41 HCs. We used the sliding time window approach analysis to extract time-varying properties of brain connectivity. Three DFC characteristics (i.e., strength, stability, variability) calculated. For comparison, we also calculated static (SFC). A linear support vector machine was differentiate MHE noHE HCs using SFC as classification features. The leave-one-out cross-validation method estimate performance. strength (DFC-Dstrength) achieved best accuracy (MHE vs. 72.5%; HCs, 84%; 88%) compared other Compared features, accuracies DFC-Dstrength feature improved by 10.5%, 8%, 14% for HC, respectively. Based on DFC-Dstrength, seven nodes identified most discriminant features classify including left inferior parietal lobule, supramarginal gyrus, calcarine, superior frontal cerebellum, right postcentral insula. In summary, have a higher in identifying patients. Our findings suggest usefulness capturing neural processes disease-related biomarkers important identification.