Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis

Abstract

Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal‐based abnormality in ALS. High‐density resting‐state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24‐month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A‐D) using K‐means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late‐stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher‐order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.

Distinct sources of resting-state EEG microstates in amyotrophic lateral sclerosis

“…The right precentral gyrus, left postcentral gyrus and supplementary motor area were identi ed as higher contributors of microstates A, B and D, respectively. These microstates all demonstrated altered temporal characteristics in ALS, as indicated in previous research [3]. A higher contribution may seem counterintuitive, considering the atrophy commonly observed in the somatosensory cortex in ALS [27].…”

“…2) partly corresponded as the right superior occipital lobe seemed to be involved in generating microstate class B, with the addition of fronto-temporal areas. Both microstates A and B are likely to represent the activation of sensory networks, especially considering their modulations in multiple sclerosis [25] and in movement disorders such as ALS [3].…”

“…The computation of microstates was accomplished using the Microstate EEGlab toolbox [18], as described in our microstate study at sensor-level [3]. In brief, we identi ed microstates based on quasi-stable topographies of resting-state EEG (1-30Hz).…”

“…Additional information was gained from examining the temporal dynamics of EEG signals. Discrimination between ALS and control groups was demonstrated, using microstates-distinct, recurrent scalp electrical potential topographies [3]. Analysing properties of four microstate classes (A-D) in ALS and HC groups revealed consistent topographies but differences in coverage, occurrence, duration, and transition probabilities.…”

Electrophysiological correlation of auditory selective spatial attention in the “cocktail party” situation

“…Studies on resting‐state EEG microstates have identified four distinct categories, microstates A, B, C, and D, which have shown high similarity across different studies (Tarailis et al, 2023 ). Nevertheless, the parameters and topography of these microstates were associated with various factors, including neurological and psychiatric disorders, age, and cognitive processes (Khanna et al, 2015 ; Metzger et al, 2023 ). Additionally, research has explored the correlation between resting‐state EEG microstates and resting‐state fMRI (Britz et al, 2010 ).…”