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

BACKGROUND: Patients with low cognitive performance are thought to have a higher risk of postoperative neurocognitive disorders. Here we analyzed the relationship between preoperative cognition and anesthesia-induced brain dynamics. We hypothesized that patients with low cognitive performance would be more sensitive to anesthetics and would show differences in electroencephalogram (EEG) activity consistent with a brain anesthesia overdose. METHODS: This is a retrospective analysis from a previously reported observational study. We evaluated cognitive performance using the Montreal cognitive assessment (MoCA) test. All patients received general anesthesia maintained with sevoflurane or desflurane during elective major abdominal surgery. We analyzed the EEG using spectral, coherence, and phase-amplitude modulation analyses. RESULTS: Patients were separated into a low MoCA group (<26 points, n = 12) and a high MoCA group (n = 23). There were no differences in baseline EEG, nor end-tidal age-corrected minimum alveolar concentration (MACage). However, under anesthesia, the low MoCA group had lower α-ß power (high MoCA: 2.9 [interquartile range {IQR}: 0.6-5.8 dB] versus low MoCA: -1.2 [IQR: -2.1 to 0.6 dB], difference 4.1 [1.0-5.7]) and a lower α peak frequency (high MoCA: 9.0 [IQR: 8.3-9.8 Hz] versus low MoCA: 7.5 [IQR: 6.3-9.0 Hz], difference 1.5 [0-2.3]) compared to the high MoCA group. The low MoCA group also had a lower α band coherence and a stronger peak-max phase-amplitude coupling (PAC). Finally, patients in the low MoCA group had longer emergence times (high MoCA 663 ± 345 seconds versus low MoCA: 960 ± 352 seconds, difference 297 [15-578]). Multiple linear regression shows up that both age and MoCA scores are independently associated with intraoperative α-ß power. CONCLUSIONS: All these EEG features, together with a prolonged emergence time, are consistent with the possibility that older patients with low cognitive performance are receiving a brain anesthesia overdose compare to cognitive normal patients.

State space methods for phase amplitude coupling analysis.

Phase amplitude coupling (PAC) is thought to play a fundamental role in the dynamic coordination of brain circuits and systems. There are however growing concerns that existing methods for PAC analysis are prone to error and misinterpretation. Improper frequency band selection can render true PAC undetectable, while non-linearities or abrupt changes in the signal can produce spurious PAC. Current methods require large amounts of data and lack formal statistical inference tools. We describe here a novel approach for PAC analysis that substantially addresses these problems. We use a state space model to estimate the component oscillations, avoiding problems with frequency band selection, nonlinearities, and sharp signal transitions. We represent cross-frequency coupling in parametric and time-varying forms to further improve statistical efficiency and estimate the posterior distribution of the coupling parameters to derive their credible intervals. We demonstrate the method using simulated data, rat local field potentials (LFP) data, and human EEG data.

Multitaper Infinite Hidden Markov Model for EEG.

Electroencephalographam (EEG) monitoring of neural activity is widely used for identifying underlying brain states. For inference of brain states, researchers have often used Hidden Markov Models (HMM) with a fixed number of hidden states and an observation model linking the temporal dynamics embedded in EEG to the hidden states. The use of fixed states may be limiting, in that 1) pre-defined states might not capture the heterogeneous neural dynamics across individuals and 2) the oscillatory dynamics of the neural activity are not directly modeled. To this end, we use a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), which discovers the set of hidden states that best describes the EEG data, without a-priori specification of state number. In addition, we introduce an observation model based on classical asymptotic results of frequency domain properties of stationary time series, along with the description of the conditional distributions for Gibbs sampler inference. We then combine this with multitaper spectral estimation to reduce the variance of the spectral estimates. By applying our method to simulated data inspired by sleep EEG, we arrive at two main results: 1) the algorithm faithfully recovers the spectral characteristics of the true states, as well as the right number of states and 2) the incorporation of the multitaper framework produces a more stable estimate than traditional periodogram spectral estimates.