Dataset Information

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.

SUBMITTER: Kiiski H

PROVIDER: S-EPMC3448656 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Only low frequency event-related EEG activity is compromised in multiple sclerosis: insights from an independent component clustering analysis.

Kiiski Hanni H Reilly Richard B RB Lonergan Róisín R Kelly Siobhán S O'Brien Marie Claire MC Kinsella Katie K Bramham Jessica J Burke Teresa T O Donnchadha Seán S Nolan Hugh H Hutchinson Michael M Tubridy Niall N Whelan Robert R

PloS one 20120921 9

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 ...[more]

PMID: 23029079

Similar Datasets

Project description:The success of stereoelectroencephalographic (SEEG) investigations depends crucially on the hypotheses on the putative location of the seizure onset zone. This information is derived from non-invasive data either based on visual analysis or advanced source localization algorithms. While source localization applied to interictal spikes recorded on scalp is the classical method, it does not provide unequivocal information regarding the seizure onset zone. Raw ictal activity contains a mixture of signals originating from several regions of the brain as well as EMG artifacts, hampering direct input to the source localization algorithms. We therefore introduce a methodology that disentangles the various sources contributing to the scalp ictal activity using independent component analysis and uses equivalent current dipole localization as putative locus of ictal sources. We validated the results of our analysis pipeline by performing long-term simultaneous scalp - intracerebral (SEEG) recordings in 14 patients and analyzing the wavelet coherence between the independent component encoding the ictal discharge and the SEEG signals in 8 patients passing the inclusion criteria. Our results show that invasively recorded ictal onset patterns, including low-voltage fast activity, can be captured by the independent component analysis of scalp EEG. The visibility of the ictal activity strongly depends on the depth of the sources. The equivalent current dipole localization can point to the seizure onset zone (SOZ) with an accuracy that can be as high as 10 mm for superficially located sources, that gradually decreases for deeper seizure generators, averaging at 47 mm in the 8 analyzed patients. Independent component analysis is therefore shown to have a promising SOZ localizing value, indicating whether the seizure onset zone is neocortical, and its approximate location, or located in mesial structures. That may contribute to a better crafting of the hypotheses used as basis of the stereo-EEG implantations.

Project description:IntroductionElectroencephalogram (EEG) acquisition is easily affected by various noises, including those from electrocardiogram (ECG), electrooculogram (EOG), and electromyogram (EMG). Because noise interference can significantly limit the study and analysis of brain signals, there is a significant need for the development of improved methods to remove this interference for more accurate measurement of EEG signals.MethodsBased on the non-linear and non-stationary characteristics of brain signals, a strategy was developed to denoise brain signals using a time-frequency denoising algorithm framework of short-time Fourier transform (STFT), bidimensional empirical mode decomposition (BEMD), and non-local means (NLM). Time-frequency analysis can reveal the signal frequency component and its evolution process, allowing the elimination of noise according to the signal and noise distribution. BEMD can be used to decompose the time-frequency signals into sub-time-frequency signals for noise removal at different scales. NLM relies on structural self-similarity to locally smooth an image to remove noise and restore its main geometric structure, making this method appropriate for time-frequency signal denoising.ResultsThe experimental results show that the proposed method can effectively suppress the high-frequency components of brain signals, resulting in a smoother brain signal waveform after denoising. The correlation coefficient of the reference signal, a superposition average of multiple trial signals, and the original single trial signal was determined, and then correlation coefficients were calculated between the reference signal and single trial signals processed by time-frequency denoising, ensemble empirical mode decomposition (EEMD)-independent component analysis (ICA), EEMD-canonical correlation analysis (CCA), and wavelet threshold denoising methods. The correlation coefficient was highest for the signal processed by the time-frequency denoising method and the reference signal, indicating that the single trial signal after time-frequency denoising was most similar to the waveform of the reference signal and suggesting this is a feasible strategy to effectively reduce noise and more accurately determine signals.DiscussionThe proposed time-frequency denoising method exhibits excellent performance with promising potential for practical application.

Project description:The objective of this study was to investigate confirmed progression independent of relapse activity in relapsing-remitting multiple sclerosis patients under long-term natalizumab treatment. We performed a retrospective, cross-sectional study of clinical data captured between 1994 and 2019 at two German multiple sclerosis tertiary referral centres. Data files of all relapsing-remitting multiple sclerosis patients treated with natalizumab for ≥24 months were analysed. Confirmed progression independent of relapse activity was defined as ≥12 week confirmed disability progression on a roving Expanded Disability Status Scale reference score by 1 point in patients with an Expanded Disability Status Scale score ≤3 or 0.5 in patients with an Expanded Disability Status Scale score ≥3.5 in the absence of a relapse. Cox proportional hazard models were used to analyse the probability of developing confirmed progression independent of relapse activity depending on both disease and natalizumab treatment duration. Among the 184 patients identified, 44 (24%) developed confirmed progression independent of relapse activity under natalizumab irrespective of the Expanded Disability Status Scale score at natalizumab onset. Time to confirmed progression independent of relapse activity was not affected by Expanded Disability Status Scale at natalizumab onset (categorized by Expanded Disability Status Scale score ≤3.5 versus >3.5) nor by duration of disease nor by duration of therapy. Confirmed progression independent of relapse activity occurred earlier in the disease course in patients with an earlier natalizumab therapy onset with regard to disease duration. A stepwise forward regression analysis revealed disease duration as the main factor for confirmed progression independent of relapse activity development (P = 0.005). Taken together, confirmed progression independent of relapse activity occurs in a substantial proportion of patients on long-term natalizumab treatment and independent of Expanded Disability Status Scale score at natalizumab onset. Our findings suggest that patients who are initiated on natalizumab early during disease course, usually in order to treat an aggressive clinical phenotype, have a higher risk of early confirmed progression independent of relapse activity.

Dataset Information

Only low frequency event-related EEG activity is compromised in multiple sclerosis: insights from an independent component clustering analysis.

Publications

Only low frequency event-related EEG activity is compromised in multiple sclerosis: insights from an independent component clustering analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets