Abstract
Recent evidence reveals that inter- and intra-individual variability significantly affects cognitive performance in a number of neuropsychological pathologies. We applied a flexible family of statistical models to elucidate the contribution of inter-and intra-individual variables on cognitive functioning in healthy volunteers and patients at risk for hepatic encephalopathy (HE). Sixty-five volunteers (32 patients with cirrhosis and 33 healthy volunteers) were assessed by means of the Inhibitory Control Task (ICT). A Generalized Additive Model for Location, Scale and Shape (GAMLSS) was fitted for jointly modeling the mean and the intra-variability of Reaction Times (RTs) as a function of socio-demographic and task related covariates. Furthermore, a Generalized Linear Mixed Model (GLMM) was fitted for modeling accuracy. When controlling for the covariates, patients without minimal hepatic encephalopathy (MHE) did not differ from patients with MHE in the low-demanding condition, both in terms of RTs and accuracy. Moreover, they showed a significant decline in accuracy compared to the control group. Compared to patients with MHE, patients without MHE showed faster RTs and higher accuracy only in the high-demanding condition. The results revealed that the application of GAMLSS and GLMM models are able to capture subtle cognitive alterations, previously not detected, in patients' subclinical pathologies.