An in-depth analysis of parameter settings and probability distributions of specific ordinal patterns in the Shannon permutation entropy during different states of consciousness in humans.

As electrical activity in the brain has complex and dynamic properties, the complexity measure permutation entropy (PeEn) has proven itself to reliably distinguish consciousness states recorded by the EEG. However, it has been shown that the focus on specific ordinal patterns instead of all of them produced similar results. Moreover, parameter settings influence the resulting PeEn value. We evaluated the impact of the embedding dimension m and the length of the EEG segment on the resulting PeEn. Moreover, we analysed the probability distributions of monotonous and non-occurring ordinal patterns in different parameter settings. We based our analyses on simulated data as well as on EEG recordings from volunteers, obtained during stable anaesthesia levels at defined, individualised concentrations. The results of the analysis on the simulated data show a dependence of PeEn on different influencing factors such as window length and embedding dimension. With the EEG data, we demonstrated that the probability P of monotonous patterns performs like PeEn in lower embedding dimension (m = 3, AUC = 0.88, [0.7, 1] in both), whereas the probability P of non-occurring patterns outperforms both methods in higher embedding dimensions (m = 5, PeEn: AUC = 0.91, [0.77, 1]; P(non-occurring patterns): AUC = 1, [1, 1]). We showed that the accuracy of PeEn in distinguishing consciousness states changes with different parameter settings. Furthermore, we demonstrated that for the purpose of separating wake from anaesthesia EEG solely pieces of information used for PeEn calculation, i.e., the probability of monotonous patterns or the number of non-occurring patterns may be equally functional.

Effects of N-Methyl-d-Aspartate Receptor Antagonists on Gamma-Band Activity During Auditory Stimulation Compared With Electro/Magneto-encephalographic Data in Schizophrenia and Early-Stage Psychosis: A Systematic Review and Perspective.

BACKGROUND AND HYPOTHESIS: N-Methyl-d-aspartate receptor (NMDA-R) hypofunctioning has been hypothesized to be involved in circuit dysfunctions in schizophrenia (ScZ). Yet, it remains to be determined whether the physiological changes observed following NMDA-R antagonist administration are consistent with auditory gamma-band activity in ScZ which is dependent on NMDA-R activity. STUDY DESIGN: This systematic review investigated the effects of NMDA-R antagonists on auditory gamma-band activity in preclinical (n = 15) and human (n = 3) studies and compared these data to electro/magneto-encephalographic measurements in ScZ patients (n = 37) and 9 studies in early-stage psychosis. The following gamma-band parameters were examined: (1) evoked spectral power, (2) intertrial phase coherence (ITPC), (3) induced spectral power, and (4) baseline power. STUDY RESULTS: Animal and human pharmacological data reported a reduction, especially for evoked gamma-band power and ITPC, as well as an increase and biphasic effects of gamma-band activity following NMDA-R antagonist administration. In addition, NMDA-R antagonists increased baseline gamma-band activity in preclinical studies. Reductions in ITPC and evoked gamma-band power were broadly compatible with findings observed in ScZ and early-stage psychosis patients where the majority of studies observed decreased gamma-band spectral power and ITPC. In regard to baseline gamma-band power, there were inconsistent findings. Finally, a publication bias was observed in studies investigating auditory gamma-band activity in ScZ patients. CONCLUSIONS: Our systematic review indicates that NMDA-R antagonists may partially recreate reductions in gamma-band spectral power and ITPC during auditory stimulation in ScZ. These findings are discussed in the context of current theories involving alteration in E/I balance and the role of NMDA hypofunction in the pathophysiology of ScZ.