Patterned functional network disruption in amyotrophic lateral sclerosis

Abstract

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting motor function, with additional evidence of extensive nonmotor involvement. Despite increasing recognition of the disease as a multisystem network disorder characterised by impaired connectivity, the precise neuroelectric characteristics of impaired cortical communication remain to be fully elucidated. Here, we characterise changes in functional connectivity using beamformer source analysis on resting‐state electroencephalography recordings from 74 ALS patients and 47 age‐matched healthy controls. Spatiospectral characteristics of network changes in the ALS patient group were quantified by spectral power, amplitude envelope correlation (co‐modulation) and imaginary coherence (synchrony). We show patterns of decreased spectral power in the occipital and temporal (δ‐ to β‐band), lateral/orbitofrontal (δ‐ to θ‐band) and sensorimotor (β‐band) regions of the brain in patients with ALS. Furthermore, we show increased co‐modulation of neural oscillations in the central and posterior (δ‐, θ‐ and γl‐band) and frontal (δ‐ and γl‐band) regions, as well as decreased synchrony in the temporal and frontal (δ‐ to β‐band) and sensorimotor (β‐band) regions. Factorisation of these complex connectivity patterns reveals a distinct disruption of both motor and nonmotor networks. The observed changes in connectivity correlated with structural MRI changes, functional motor scores and cognitive scores. Characteristic patterned changes of cortical function in ALS signify widespread disease‐associated network disruption, pointing to extensive dysfunction of both motor and cognitive networks. These statistically robust findings, that correlate with clinical scores, provide a strong rationale for further development as biomarkers of network disruption for future clinical trials.

Reproducibility of EEG functional connectivity in Alzheimer’s disease

“…It is reported to be decreased in the alpha (8)(9)(10)(11)(12)(13) band in AD subjects by multiple studies but some other studies have additionally reported an increase in delta (0.5-4 Hz) band coherence [18,19] and an increase in theta (4-8 Hz) band coherence [20]. In another comparison of AD and cognitively normal patients, both the phase lag index (PLI) and imaginary coherence showed a decrease in functional connectivity in the beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) frequency band [21], where other studies showed differences in the alpha band PLI [22] and alpha [23] but also delta and theta band [24] imaginary coherence.…”

“…The freely available 'Brainwave' software [40] was used to estimate functional connectivity. Mean global coherence (Coh), imaginary coherence (iCoh), phase locking value (PLV), amplitude envelope correlation (AEC), AEC with leakage correction (AEC-c), phase lag index (PLI) and weighted PLI (wPLI) were estimated in five frequency bands: broadband (0.5-30 Hz), delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Functional connectivity per electrode was estimated by averaging the values for each possible electrode pair per electrode (for example, the value of electrode Fp1 is the average of each potential electrode pair with electrode Fp1).…”

“…amplitude, phase, frequency), the chosen bandwidth and regional changes. As shown by research in other neurodegenerative diseases, different functional connectivity measures can show different regional increases or decreases in the same or different bandwidths [26,27]. Neurodegeneration also proves to be a nonlinear process which can cause different states in different stages of the disease [28,29].…”

An Implementation of Empirical Bayesian Inference and Non-Null Bootstrapping for Threshold Selection and Power Estimation in Multiple and Single Statistical Testing

“…The new implementation of EBI was applied on reallife experimental High-Density EEG data to further demonstrate the utility in practice. The experimental EEG was recorded during steady state conditions, including a resting-state condition (S. Dukic et al, 2017;Stefan Dukic et al, 2019) and a sustained isometric motor task (Coffey et al, 2019). The data included 363 and 99 epochs of 1 second duration in each condition respectively.…”

EEG power spectral density in locked-in and completely locked-in state patients: a longitudinal study

“…Many studies have compared electrophysiological signatures from ALS patients and controls (Jayaram et al 2015;Nasseroleslami et al 2019;Dukic et al 2019;Maruyama et al 2020), reporting features distinguishing the two groups. The most reliable evidence found is a decrease in alpha relative power, with a shift of the peak in the alpha frequency band (generally present in healthy patients' EEG power spectrum) to lower frequencies (Mai et al 1998;Hohmann et al 2018).…”