ArticleMetabolic brain disease2014
Assessing inter- and intra-individual cognitive variability in patients at risk for cognitive impairment: the case of minimal hepatic encephalopathy.
Article in Metabolic brain disease, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- The role of multifocal visual evoked potential in detection of minimal hepatic encephalopathy in patients with compensated liver cirrhosis.BMC neurology · 2025Article
- Remote monitoring of cognition in cirrhosis and encephalopathy: future opportunity and challenge.Metabolic brain disease · 2023Review
- Psychometric methods for diagnosing and monitoring minimal hepatic encephalopathy -current validation level and practical use.Metabolic brain disease · 2022Review
- Clinical neurophysiology of hepatic encephalopathy.Journal of clinical and experimental hepatology · 2015Review
- It's a Matter of Mind! Cognitive Functioning Predicts the Athletic Performance in Ultra-Marathon Runners.PloS one · 2015Article
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Authors and funding
7 authors.
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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.
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