Only Low Frequency Event-Related EEG Activity Is Compromised in Multiple Sclerosis: Insights from an Independent Component Clustering Analysis

Abstract

Cognitive impairment (CI), often examined with neuropsychological tests such as the Paced Auditory Serial Addition Test (PASAT), affects approximately 65% of multiple sclerosis (MS) patients. The P3b event-related potential (ERP), evoked when an infrequent target stimulus is presented, indexes cognitive function and is typically compared across subjects' scalp electroencephalography (EEG) data. However, the clustering of independent components (ICs) is superior to scalp-based EEG methods because it can accommodate the spatiotemporal overlap inherent in scalp EEG data. Event-related spectral perturbations (ERSPs; event-related mean power spectral changes) and inter-trial coherence (ITCs; event-related consistency of spectral phase) reveal a more comprehensive overview of EEG activity. Ninety-five subjects (56 MS patients, 39 controls) completed visual and auditory two-stimulus P3b event-related potential tasks and the PASAT. MS patients were also divided into CI and non-CI groups (n = 18 in each) based on PASAT scores. Data were recorded from 128-scalp EEG channels and 4 IC clusters in the visual, and 5 IC clusters in the auditory, modality were identified. In general, MS patients had significantly reduced ERSP theta power versus controls, and a similar pattern was observed for CI vs. non-CI MS patients. The ITC measures were also significantly different in the theta band for some clusters. The finding that MS patients had reduced P3b task-related theta power in both modalities is a reflection of compromised connectivity, likely due to demyelination, that may have disrupted early processes essential to P3b generation, such as orientating and signal detection. However, for posterior sources, MS patients had a greater decrease in alpha power, normally associated with enhanced cognitive function, which may reflect a compensatory mechanism in response to the compromised early cognitive processing.

Neural oscillatory mechanisms during novel grammar learning underlying language analytical abilities

“…Siegel, Donner, & Engel, 2012;Wang, 2010). Spectral power variations reflect the number of neurons discharging at the same time (Kiiski et al, 2012;Klimesch, 1999), and are thus seen as a measure of local neuronal activity. Employing coherence analyses, EEG offers a view on functional cooperation between cortical regions: brain areas activated by a particular cognitive task exhibit increased coherence, and high coherence between two EEG signals is indicative of high cooperation (degree of information flow) and synchronisation between underlying brain regions within a certain frequency band (Weiss & Mueller, 2003).…”

A wavelet based algorithm for the identification of oscillatory event-related potential components

“…In both cases, standard ERP methods that depend on averaging many single trials in order to eliminate EEG responses not associated with the stimulus manipulation also end up eliminating important information only available when single trials are scored, namely, the degree to which the brain response is consistent across trials. For example, intra-individual variability of behavioral responses (MacDonald et al, 2006) and of ERPs (Segalowitz et al, 1997) relate to important cognitive and neural control functions, and potentially inform us of the progress of variable disease processes (Kiiski et al, 2012). However, the methods currently available to detect this variability are relatively crude.…”

“…However, the methods currently available to detect this variability are relatively crude. One such method is to apply a severe low-pass filter that eliminates everything except a low-frequency large component such as the P300 (Segalowitz et al, 1997).Others have specific parameter requirements, such as independent component analysis (ICA) which requires having an adequate number of electrode channels (e.g., 64 sites) and having stationery signals (Kiiski et al, 2012). The algorithm presented here permits the scoring on single-trials of the amplitude and latency (with respect to a stimulus-onset time) of an ERP component, thus yielding a latency-consistency measure.…”

Topographical segmentation: A new tool to optimally define temporal region-of-interests of significant difference in ERPs

“…As far as possible physiologically-relevant difference of brain responses were concerned, temporal ROIs, which met the following criteria, were considered in this analysis: (1) resulted in clusters included in the time-interval from 0 to 400 ms; (2) showed an increased standard deviations as compared to that within the pre-stimulus time-interval (-200 to 0 ms); and (3) lasted no less than 30 ms.…”

“…In this cluster-based statistical testing, the extraction of significant differences of EEG/ERP features (e.g., ERP components or event-related spectral perturbation) is achieved by defining the temporal regions of interests (ROIs) based on the temporal adjacency of statistical significance if data from single-electrode are tested, and based on the spatial and temporal adjacency of statistical significance if data from multi-electrodes are tested [1]. Indeed, this statistical method is a sensitive approach suited to solve the multiple comparison problem, and thus has been successfully applied in several EEG/ERP studies [2][3][4]. However, when the assessed significant differences are strong and sustained in time, this cluster-based approach would result in a long-lasting temporal ROI, within which, scalp distributions of the statistical significance varied greatly from time to time.…”