Altered EEG State Switching Dynamics in Mild Cognitive Impairment During Eyes-closed Resting
Poster No:
1326
Submission Type:
Abstract Submission
Authors:
Aland Astudillo1, Diana Karamacoska1, Frances De Blasio2, Genevieve Steiner-Lim1
Institutions:
1NICM Health Research Institute, Western Sydney University, Penrith, Australia, 2Brain & Behaviour Research Institute and School of Psychology, University of Wollongong, Wollongong, Australia
First Author:
Co-Author(s):
Diana Karamacoska, Doctor
NICM Health Research Institute, Western Sydney University
Penrith, Australia
NICM Health Research Institute, Western Sydney University
Penrith, Australia
Frances De Blasio, Doctor
Brain & Behaviour Research Institute and School of Psychology, University of Wollongong
Wollongong, Australia
Brain & Behaviour Research Institute and School of Psychology, University of Wollongong
Wollongong, Australia
Genevieve Steiner-Lim, AProf
NICM Health Research Institute, Western Sydney University
Penrith, Australia
NICM Health Research Institute, Western Sydney University
Penrith, Australia
Introduction:
Dementia cases are increasing in underdeveloped and developed countries, and it is currently the second leading cause of death and the leading cause of disability for older people in Australia. While there is currently no cure, ~45% of cases may be preventable or deferrable (Livingston et al., 2024). The identification of accurate and effective early biomarkers therefore remains a priority for primary and secondary prevention. EEG is a cost effective and non-invasive tool for measuring neuronal activity that provides promising utility for the early detection of dementia, such as during the mild cognitive impairment (MCI) phase. Differences in brain activity and potential alterations in the underlaying connectivity in MCI are apparent (Babiloni et al., 2013; Babiloni et al., 2021), appearing similar to dementia an Alzheimer's disease (AD) (Babiloni et al., 2021). The spontaneous EEG displays a repertoire of quasi-stationary spatiotemporal connectivity reflecting underlying neural networks, which may be related to cognitive function (Cabral et al., 2014; Trujillo-Barreto et al., 2024; Honcamp et al., 2022; Honcamp et al., 2024; Hunyadi et al., 2019). The aim of this study was to assess EEG differences in estimated brain states and their dynamical fluctuations during rest in people with MCI compared to cognitively healthy controls. Here, we characterised brain states by using Hidden semi-Markov models (HsMM) to evaluate the switching dynamics of the resting-state (RS) EEG in MCI (Trujillo-Barreto et al., 2024).
Methods:
Continuous RS EEG (2 min) was recorded with eyes closed from an MCI group (n = 17) and an age matched healthy control group (HC; n = 17). The EEG data were corrected for eye movements and artifacts using Revised Aligned-Artefact Average (RAAA) (Croft & Barry, 2000) and re-referenced to the mastoid mean. Channels with major artefacts were interpolated. The envelope of the alpha band pass filtered (7-13 Hz) signal was obtained by Hilbert Transform. Dimension reduction was applied using Principal Components Analysis (PCA) (d = 20) and subsampling (64 Hz). We used HsMM with Normal emission model for observations (data) and LogNormal densities to model state duration to estimate 9 states (Trujillo-Barreto et al., 2024). The Variational Bayes framework was used to infer model state parameters. Brain state metrics including fractional occupancy, duration, transition probabilities, and time courses were obtained. We compared the parameters between groups for each state.
Results:
State trajectories and transitions differed between the groups, with different state occupancy and fewer transitions seen for MCI. The state fractional occupancies (Fig 1A) were similar between the groups for all except states 3 (p <.001) and 9 (p = .003). The state durations (Fig 1B) differed between groups for state 2 (p <.001), state 4 (p <.001), state 5 (p = .0025), state 7 (p <.001), and state 8 (p = .0035). The brain state map (Fig 1C) patterns show consistency with brain states maps obtained for alpha band EEG data in other populations as ageing healthy participants and Parkinson's Disease (e.g., Kotz et al., 2023).
Conclusions:
Specific patterns of EEG state dynamics were found here to distinguish between MCI participants and healthy controls. We argue that these differences seen in brain states fractional occupancies and state durations may be in line with disease progression along the dementia continuum, possibly reflecting pathological alterations in connectivity. Future work should relate brain states trajectories, maps, and metrics to cognitive function, both to serve as markers of disease and disease progression, and as a framework for understanding the relationship between brain activation and cognition. This study contributes to our knowledge about how these fast transient brain states in MCI and how their different patterns may relate to alterations in connectivity and cognition in MCI and dementia.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Modeling and Analysis Methods:
Bayesian Modeling
EEG/MEG Modeling and Analysis 1
Task-Independent and Resting-State Analysis 2
Keywords:
Aging
Computational Neuroscience
Degenerative Disease
Electroencephaolography (EEG)
Machine Learning
Modeling
Other - Mild Cognitive Impairment
1|2Indicates the priority used for review
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