Propofol disrupts alpha dynamics in functionally distinct thalamocortical networks during loss of consciousness

Abstract

During propofol-induced general anesthesia, alpha rhythms measured using electroencephalography undergo a striking shift from posterior to anterior, termed anteriorization, where the ubiquitous waking alpha is lost and a frontal alpha emerges. The functional significance of alpha anteriorization and the precise brain regions contributing to the phenomenon are a mystery. While posterior alpha is thought to be generated by thalamocortical circuits connecting nuclei of the sensory thalamus with their cortical partners, the thalamic origins of the propofol-induced alpha remain poorly understood. Here, we used human intracranial recordings to identify regions in sensory cortices where propofol attenuates a coherent alpha network, distinct from those in the frontal cortex where it amplifies coherent alpha and beta activities. We then performed diffusion tractography between these identified regions and individual thalamic nuclei to show that the opposing dynamics of anteriorization occur within two distinct thalamocortical networks. We found that propofol disrupted a posterior alpha network structurally connected with nuclei in the sensory and sensory associational regions of the thalamus. At the same time, propofol induced a coherent alpha oscillation within prefrontal cortical areas that were connected with thalamic nuclei involved in cognition, such as the mediodorsal nucleus. The cortical and thalamic anatomy involved, as well as their known functional roles, suggests multiple means by which propofol dismantles sensory and cognitive processes to achieve loss of consciousness.

Oscillatory activity underlying cognitive performance in children and adolescents with autism: a systematic review

“…It has been shown that when recording brain activity from the human scalp, the dominant frequencies are in the alpha and beta ranges, which span from 8 to 12 Hz and 13 to 30 Hz, respectively ( Pfurtscheller and Lopes Da Silva, 1999 ; Kilavik et al, 2013 ; Piai et al, 2015 ; Nyhus, 2018 ; Griffiths et al, 2019 ; Barone and Rossiter, 2021 ). Intracranial recordings have demonstrated that alpha oscillations, generated through cortical and thalamocortical mechanisms ( Pfurtscheller and Lopes Da Silva, 1999 ; Weiner et al, 2023 ), are observable in parietal, visual, motor, and auditory cortices. Synchronized alpha-band oscillations have been observed during mental inactivity, often described as an “idling system” ( Pfurtscheller and Lopes Da Silva, 1999 ; Buzsáki and Draguhn, 2004 ).…”

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings

“…Generally, the source-to-sensor mixing complicates the interpretation of the topographies generated by signal processing tools that treat each channel individually [6]. In a nonparametric frequency-domain approach, the only way to discover this spatially-correlated structure across sensors is to perform eigenvalue analysis on the cross-spectral density on a frequency by frequency basis, also known as global coherence analysis [48, 57, 58]. If the eigenvalue distribution exhibits sufficient skewness, the associated frequency is deemed coherent and the leading eigenvectors are identified as the ‘principal’ sensor networks at that frequency; in other words a proxy for a strong oscillatory component.…”

Differences in the EEG Power Spectrum and Cross-Frequency Coupling Patterns between Young and Elderly Patients during Sevoflurane Anesthesia

“…During anesthesia, we found that the alpha power in elderly patients decreased more than other frequencies, which is consistent with the previously reported decrease of spectral power within alpha frequencies in older populations [ 5 ]. Alpha power is generated within thalamocortical circuits [ 26 ]. Cortical development during typical aging follows a ‘last to develop, first to degenerate’ pattern, which means that brain regions with high postnatal area expansion also show the most significant declines in early aging [ 27 ].…”