EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease

Abstract

The dynamics of the resting brain exhibit transitions between a small number of discrete networks, each remaining stable for tens to hundreds of milliseconds. These functional microstates are thought to be the building blocks of spontaneous consciousness. The electroencephalogram (EEG) is a useful tool for imaging microstates, and EEG microstate analysis can potentially give insight into altered brain dynamics underpinning cognitive impairment in disorders such as Alzheimer’s disease (AD). Since EEG is non-invasive and relatively inexpensive, EEG microstates have the potential to be useful clinical tools for aiding early diagnosis of AD. In this study, EEG was collected from two independent cohorts of probable AD and cognitively healthy control participants, and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. The microstate associated with the frontoparietal working-memory/attention network was altered in AD due to parietal inactivation. Using a novel measure of complexity, we found microstate transitioning was slower and less complex in AD. When combined with a spectral EEG measure, microstate complexity could classify AD with sensitivity and specificity > 80%, which was tested on an independent cohort, and could predict progression from MCI to AD in a small preliminary test cohort of 11 participants. EEG microstates therefore have potential to be a non-invasive functional biomarker of AD.

MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

“…We studied a number of spatiotemporal statistics of the resulting source MEG microstate sequences. Global statistics of the microstate sequences include GEV (Murray et al, 2008), mean duration of microstates (Koenig et al, 2002), Hurst exponent of the sequences (Van De Ville et al, 2010), and microstate complexity (Tait et al, 2020b). Microstate complexity values were normalized against its theoretical asymptotic upper bound (Lempel and Ziv, 1976; Liu et al, 2016; Zhang et al, 2016), to result in a measure ∈ (0, 1].…”

“…Resting-state EEG microstates are robust and highly reproducible (Michel and Koenig, 2018), and have been associated with fMRI resting-state networks (Britz et al, 2010; Musso et al, 2010; Yuan et al, 2012; Schumacher et al, 2019; Abreu et al, 2020; Xu et al, 2020; Zoubi et al, 2020) and cognitive domains (Brodbeck et al, 2012; Britz et al, 2014; Milz et al, 2016; Seitzman et al, 2017; Zappasodi et al, 2019), earning EEG microstates the nickname the ‘atoms of thought’ (Lehmann, 1990). EEG microstates have also been demonstrated to be a potentially useful clinical tool for understanding and diagnosing neurological diseases such as Alzheimer’s disease and other dementias (Nishida et al, 2013; Musaeus et al, 2019; Smailovic et al, 2019; Schumacher et al, 2019; Tait et al, 2020b), schizophrenia (Lehmann et al, 2005; Andreou et al, 2014; Tomescu et al, 2014), and a range of other disorders (Khanna et al, 2014).…”

“…However, a key limitation of sensor-space microstate analysis is anatomical interpretation. A number studies have attempted to reconstruct the electrophysiological sources underpinning microstates maps by combining sensor-space microstate analysis with subsequent source reconstruction (Custo et al, 2014, 2017; Pascual- Marqui et al, 2014; Milz et al, 2016, 2017; Tait et al, 2020b). However, this approach may be limiting insight into functional brain states for a number of reasons.…”

A Large-Scale Brain Network Mechanism for Increased Seizure Propensity in Alzheimer’s Disease

“…Participants provided written informed consent before participating and were free to withdraw at any time. All procedures for this cohort were approved by the National [31,47,48], and are given in Table 1. People with AD had significantly lower cognitive test scores than controls as assessed with the mini-mental state examination (MMSE), and there was no significant difference in age or gender between groups [48].…”

+microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG

“…The resulting microstate maps are subsequently back-fit to the data, labelling each EEG sample with a microstate label based on maximal similarity to the map in order to obtain a temporal microstate sequence. Microstates have been useful for understanding healthy cognition (Brodbeck et al, 2012; Britz et al, 2014; Milz et al, 2016; Seitzman et al, 2017; Zappasodi et al, 2019), Alzheimer’s disease and other dementias (Nishida et al, 2013; Musaeus et al, 2019; Smailovic et al, 2019; Schumacher et al, 2019; Tait et al, 2020), schizophrenia (Lehmann et al, 2005; Andreou et al, 2014; Tomescu et al, 2014), and other neurological disorders (Khanna et al, 2014)…”