NONLINEAR-ANALYSIS OF THE ELECTROENCEPHALOGRAM IN CREUTZFELDT-JAKOB-DISEASE

Creutzfeldt-Jakob disease is a rare, neurological, dementing disorder characterised by periodic sharp waves in the electroencephalogram (EEG ). Non-linear analysis of these EEG changes may provide insight into t he abnormal dynamics of cortical neural networks in this disorder. Bab loyantz et al. have suggested that the periodic sharp waves reflect lo w-dimensional chaotic dynamics in the brain. In the present study this hypothesis was re-examined using newly developed techniques for non-l inear time series analysis. We analysed the EEG of a patient with auto psy-proven Creutzfeldt-Jakob disease using the method of nonlinear for ecasting as introduced by Sugihara and May, and we tested for non-line arity with amplitude-adjusted, phase-randomised surrogate data. Two ep ochs with generalised periodic sharp waves showed clear evidence for n on-linearity. These epochs could be predicted better and further ahead in time than most of the irregular background activity. Testing again st cycle-randomised surrogate data and close inspection of the periodo grams showed that the non-linearity of the periodic sharp waves may be better explained by quasiperiodicity than by low-dimensional chaos. T he EEG further displayed at least one example of a sudden, large quali tative change in the dynamics, highly suggestive of a bifurcation. The presence of quasi-periodicity and bifurcations strongly argues for th e use of a non-linear model to describe the EEG in Creutzfeldt-Jakob d isease.