Association Between Lower Preoperative Cognition With Intraoperative Electroencephalographic Features Consistent With Deep States of Anesthesia in Older Patients: An Observational Cohort Study

Boyd

et al. 2021

J Intensive Care Med

Background: Several studies have previously reported the presence of altered cerebral perfusion during sepsis. However, the role of non-invasive neuromonitoring, and the impact of altered cerebral perfusion, in sepsis patients with delirium remains unclear. Methods: We performed a systematic review of studies that used near-infrared spectroscopy (NIRS) and/or transcranial Doppler (TCD) to assess adults (≥18 years) with sepsis and delirium. From study inception to July 28, 2020, we searched the following databases: Ovid MedLine, Embase, Cochrane Library, and Web of Science. Results: Of 1546 articles identified, 10 met our inclusion criteria. Although NIRS-derived regional cerebral oxygenation was consistently lower, this difference was only statistically significant in one study. TCD-derived cerebral blood flow velocity was inconsistent across studies. Importantly, both impaired cerebral autoregulation during sepsis and increased cerebrovascular resistance were associated with delirium during sepsis. However, the heterogeneity in NIRS and TCD devices, duration of recording (from 10 seconds to 72 hours), and delirium assessment methods (e.g., electronic medical records, confusion assessment method for the intensive care unit), precluded meta-analysis. Conclusion: The available literature demonstrates that cerebral perfusion disturbances may be associated with delirium in sepsis. However, future investigations will require consistent definitions of delirium, delirium assessment training, harmonized NIRS and TCD assessments (e.g., consistent measurement site and length of recording), as well as the quantification of secondary and tertiary variables (i.e., Cox, Mxa, MAPOPT), in order to fully assess the relationship between cerebral perfusion and delirium in patients with sepsis.

mentioning

confidence: 99%

The Use of Near-Infrared Spectroscopy and/or Transcranial Doppler as Non-Invasive Markers of Cerebral Perfusion in Adult Sepsis Patients With Delirium: A Systematic Review

Boyd

et al. 2021

J Intensive Care Med

“…However, such a relationship between frontal alpha power and preoperative cognitive function was not observed in other EEG frequency bands. Gutiérrez et al [ 14 ] demonstrated that patients with lower MoCA scores exhibited lower alpha power and slower alpha peak frequency, which is consistent with the possibility that elderly patients with poor cognitive function receive an overdose of brain anesthesia compared to cognitively more robust patients. Similarly, Touchard et al [ 15 ] included 42 patients who had a preoperative cognitive assessment using MoCA with EEG information collected under a propofol-based general anesthesia, and they found that patients with cognitive decline had a lower alpha power and target TCI.…”

Section: Intraoperative Eeg Features and Pndmentioning

confidence: 65%

Electroencephalogram Features of Perioperative Neurocognitive Disorders in Elderly Patients: A Narrative Review of the Clinical Literature

et al. 2022

Postoperative neurocognitive disorder (PND) is a common postoperative complication, particularly in older patients. Electroencephalogram (EEG) monitoring, a non-invasive technique with a high spatial–temporal resolution, can accurately characterize the dynamic changes in brain function during the perioperative period. Current clinical studies have confirmed that the power density of alpha oscillation during general anesthesia decreased with age, which was considered to be associated with increased susceptibility to PND in the elderly. However, evidence on whether general anesthesia under EEG guidance results in a lower morbidity of PND is still contradictory. This is one of the reasons that common indicators of the depth of anesthesia were limitedly derived from EEG signals in the frontal lobe. The variation of multi-channel EEG features during the perioperative period has the potential to highlight the occult structural and functional abnormalities of the subcortical–cortical neurocircuit. Therefore, we present a review of the application of multi-channel EEG monitoring to predict the incidence of PND in older patients. The data confirmed that the abnormal variation in EEG power and functional connectivity between distant brain regions was closely related to the incidence and long-term poor outcomes of PND in older adults.

“…We hypothesized that the ability to more accurately identify and parameterize alpha oscillations would make it possible to characterize the relationship between alpha oscillations and cognitive health with greater statistical efficiency. To evaluate this idea, we fit a nested model to describe the presence of each patient's alpha oscillation and its frequency, if present, as a function of the patient's MoCA score in n = 35 subjects from the previously reported study described earlier [34]. We used a logistic regression model to represent the presence or absence of an alpha oscillation, in combination with a linear model to relate the alpha frequency to the MoCA score.…”

Section: Results: Eeg Under Anesthesiamentioning

confidence: 99%

An iterative search algorithm to identify oscillatory dynamics in neurophysiological time series

Beck¹,

He²,

Gutiérrez³

et al. 2022

Preprint

Self Cite

Neural oscillations have long been recognized for their mechanistic importance in coordinating activity within and between brain circuits. Co-occurring broad-band, non-periodic signals are also ubiquitous in neural data and are thought to reflect the characteristics of population-level neuronal spiking activity. Identifying oscillatory activity distinct from broadband signals is therefore an important, yet surprisingly difficult, problem in neuroscience. Commonly-used bandpass filters produce spurious oscillations when applied to broad-band noise and may be ill-informed by canonical frequency bands. Curve-fitting procedures have been developed to identify peaks in the power spectrum distinct from broadband noise. Unfortunately, these ad hoc methods are prone to overfitting and are difficult to interpret in the absence of generative models to formally represent oscillatory behavior. Here we present a novel method to identify and characterize neural oscillations distinct from broad-band noise. First, we propose a new conceptual construct that makes clear, from a dynamical systems perspective, when oscillations are present or not. We then use this construct to develop generative models for neural oscillations. We show through extensive analyses of simulated and human EEG data that our approach identifies oscillations and their characteristics far more accurately than widely used methods. We also show that our method can automatically identify subject-level variations in frequency to overcome the limitations of fixed canonical frequency bands.