EEG power spectral density under Propofol and its association with burst suppression, a marker of cerebral fragility

Propofol Requirement and EEG Alpha Band Power During General Anesthesia Provide Complementary Views on Preoperative Cognitive Decline

“…FIGURE 3 | Generalization testing on burst suppression data. To probe the generalization of the proposed model beyond the given sample we considered the observational data from Touchard et al (2019) in which burst suppression was studied as a proxy for cognitive decline. The dataset comprised 56 patients sedated with Propofol.…”

“…Despite important effect sizes, the number of patients was small. We attempted to address this issue by generalizing the HELP model to the dataset from our previous study (Touchard et al, 2019) and found that using iso-electrical suppression was linked to cognitive fragility. Still, as explained in detail by Wildes and colleagues, burst-suppression interpretation is protocol-dependent, hence might not always be the best indicator of CD.…”

“…We further proceeded by evaluating the generalization performance of the proposed model of CD (Figure 3) to distinct but related data on iso-electrical suppressions (IES), the flat component of the burst-suppression. We considered 56 patients from a previous observational study on the relationship between ABP, TCI and IES (Touchard et al, 2019) in which IES was investigated as a proxy for CD. Results suggest that our proposed model generalizes well-beyond the current dataset and captures physiological variance linked to cognitive decline and drug sensitivity.…”

“…On the other hand, it is also recognized that the effective dosage of hypnotics under general anesthesia decreases with age (Schnider model and MAC) but also with preoperative cognitive status (Laalou et al, 2010). We previously presented evidence for an important association of sedation levels and EEG derived variables (Propofol requirement and Alpha band power) with chronological age and BS (Touchard et al, 2019). Yet, the precise relationship between these two variables and CD is still unclear.…”

Excess brain age in the sleep electroencephalogram predicts reduced life expectancy

“…Several aging biomarkers have been proposed, including molecular-cellular level biomarkers such as leukocyte telomere length (Kruk et al, 1995) and DNA methylation status (Bocklandt et al, 2011;Chen et al, 2016;Horvath, 2013). Among organ-system level biomarkers, the concept of "brain age" has emerged recently, which represents the age predicted by analyzing age-related patterns in brain structure (from brain MRI images) (Cole et al, 2017b;Cole and Frank, 2017;Franke et al, 2010;Franke and Gaser, 2012) and function (from EEG signals) (Al Zoubi et al, 2018;Purdon et al, 2015;Sun et al, 2019;Touchard et al, 2019). The sleep state is uniquely attractive for computational analysis because it exhibits a rich set of features that are distinct for each sleep stage, are conserved across individuals, and change systematically with age.…”