ArticleNeuroImage. Clinical2016
Fluctuations of spontaneous EEG topographies predict disease state in relapsing-remitting multiple sclerosis.
Article in NeuroImage. Clinical, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Functional Aspects of Resting EEG Microstates: A Systematic Review.Brain topography · 2024Pooled it
- Analysis of electroencephalogram microstates and spectral power in children and adolescents with attention deficit hyperactivity disorder.European child & adolescent psychiatry · 2026Article
- EEG microstate alterations in Parkinson's disease dementia.Scientific reports · 2026Article
- Microstate in rats' EEG: a proof of concept study.Translational psychiatry · 2025Article
- Virtual Reality Experience as Reflected in EEG Microstates.Brain topography · 2025Article
- EEG microstate analysis in children with prolonged disorders of consciousness.Scientific reports · 2025Article
- Effects of two types of repetitive transcranial magnetic stimulation on brain network in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Heterogeneous Brain Dynamics Between Acute Cerebellar and Brainstem Infarction.Cerebellum (London, England) · 2024Article
- Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms.Brain topography · 2024Article
- Fatigue in Multiple Sclerosis: A Resting-State EEG Microstate Study.Brain topography · 2024Article
- Deep-learning-optimized microstate network analysis for early Parkinson's disease with mild cognitive impairment.Cognitive neurodynamics · 2024Article
- On the Reliability of the EEG Microstate Approach.Brain topography · 2024Article
- Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis.Human brain mapping · 2024Article
- Article
- EEG Microstate as a Marker of Adolescent Idiopathic Scoliosis.IEEE open journal of engineering in medicine and biology · 2024Article
- EEG spectral and microstate analysis originating residual inhibition of tinnitus induced by tailor-made notched music training.Frontiers in neuroscience · 2023Article
- The effect of acupuncture at theFrontiers in integrative neuroscience · 2023Article
- Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness.Frontiers in neuroscience · 2023Article
- Spike-microstates correlate with interictal epileptogenic discharges: a marker for hidden epileptic activity.Brain communications · 2023Article
- Microstates in multiple sclerosis: an electrophysiological signature of altered large-scale networks functioning?Brain communications · 2023Article
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9 authors.
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Abstract
Spontaneous fluctuations of neuronal activity in large-scale distributed networks are a hallmark of the resting brain. In relapsing-remitting multiple sclerosis (RRMS) several fMRI studies have suggested altered resting-state connectivity patterns. Topographical EEG analysis reveals much faster temporal fluctuations in the tens of milliseconds time range (termed "microstates"), which showed altered properties in a number of neuropsychiatric conditions. We investigated whether these microstates were altered in patients with RRMS, and if the microstates' temporal properties reflected a link to the patients' clinical features. We acquired 256-channel EEG in 53 patients (mean age 37.6 years, 45 females, mean disease duration 9.99 years, Expanded Disability Status Scale ≤ 4, mean 2.2) and 49 healthy controls (mean age 36.4 years, 33 females). We analyzed segments of a total of 5 min of EEG during resting wakefulness and determined for both groups the four predominant microstates using established clustering methods. We found significant differences in the temporal dynamics of two of the four microstates between healthy controls and patients with RRMS in terms of increased appearance and prolonged duration. Using stepwise multiple linear regression models with 8-fold cross-validation, we found evidence that these electrophysiological measures predicted a patient's total disease duration, annual relapse rate, disability score, as well as depression score, and cognitive fatigue measure. In RRMS patients, microstate analysis captured altered fluctuations of EEG topographies in the sub-second range. This measure of high temporal resolution provided potentially powerful markers of disease activity and neuropsychiatric co-morbidities in RRMS.
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