Multimodal Approaches to Investigating Neural Dynamics in Cognition and Related Clinical Conditions: Integrating EEG, MEG, and fMRI Data

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Introduction: Parkinson’s disease (PD) is a neurodegenerative disorder affecting the whole brain, leading to several motor and non-motor symptoms. In the past, it has been shown that PD alters resting state networks (RSN) in the brain. These networks are usually derived from fMRI BOLD signals. This study investigated RSN changes in PD patients based on maximum phase-amplitude coupling (PAC) throughout the cortex. We also tested the hypothesis that levodopa medication shifts network activity back toward a healthy state.

Methods: We recorded 23 PD patients and 24 healthy age-matched participants for 30 min at rest with magnetoencephalography (MEG). PD patients were measured once in the dopaminergic medication ON and once in the medication OFF state. A T1-MRI brain scan was acquired from each participant for source reconstruction. After correcting the data for artifacts and performing source reconstruction using a linearly constrained minimum variance beamformer, we extracted visual, sensorimotor (SMN), and frontal RSNs based on PAC.

Results: We found significant changes in all networks between healthy participants and PD patients in the medication OFF state. Levodopa had a significant effect on the SMN but not on the other networks. There was no significant change in the optimal PAC coupling frequencies between healthy participants and PD patients.

Discussion: Our results suggest that RSNs, based on PAC in different parts of the cortex, are altered in PD patients. Furthermore, levodopa significantly affects the SMN, reflecting the clinical alleviation of motor symptoms and leading to a network normalization compared to healthy controls.

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Original Research
15 June 2023
Diversity of electroencephalographic patterns during propofol-induced burst suppression
Keith G. Jones
10 more and 
Brian J. Mickey
Time- and frequency-domain representations of burst suppression during an individual propofol infusion session. Data is from the BIS Monitor EEG of subject 2, infusion 4. (A) The estimated effect site concentration, (B) raw EEG and (C) scalogram from 22 min of a propofol infusion. (D) A zoomed in section showing the three types of EEG activity and (E) scalogram. Black arrows indicate canonical broadband bursts; green, low-frequency bursts; yellow, spindles. (F) Individual examples of all three features including a (G) canonical broadband burst, (H) low-frequency burst, and (I) spindle.

Burst suppression is a brain state consisting of high-amplitude electrical activity alternating with periods of quieter suppression that can be brought about by disease or by certain anesthetics. Although burst suppression has been studied for decades, few studies have investigated the diverse manifestations of this state within and between human subjects. As part of a clinical trial examining the antidepressant effects of propofol, we gathered burst suppression electroencephalographic (EEG) data from 114 propofol infusions across 21 human subjects with treatment-resistant depression. This data was examined with the objective of describing and quantifying electrical signal diversity. We observed three types of EEG burst activity: canonical broadband bursts (as frequently described in the literature), spindles (narrow-band oscillations reminiscent of sleep spindles), and a new feature that we call low-frequency bursts (LFBs), which are brief deflections of mainly sub-3-Hz power. These three features were distinct in both the time and frequency domains and their occurrence differed significantly across subjects, with some subjects showing many LFBs or spindles and others showing very few. Spectral-power makeup of each feature was also significantly different across subjects. In a subset of nine participants with high-density EEG recordings, we noted that each feature had a unique spatial pattern of amplitude and polarity when measured across the scalp. Finally, we observed that the Bispectral Index Monitor, a commonly used clinical EEG monitor, does not account for the diversity of EEG features when processing the burst suppression state. Overall, this study describes and quantifies variation in the burst suppression EEG state across subjects and repeated infusions of propofol. These findings have implications for the understanding of brain activity under anesthesia and for individualized dosing of anesthetic drugs.

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