Narcoleptic Patients Show Fragmented EEG-Microstructure During Early NREM Sleep

Kuhn, Alena; Brodbeck, Verena; Tagliazucchi, Enzo; Morzelewski, Astrid; Wegner, Frederic von; Laufs, Helmut

doi:10.1007/s10548-014-0387-1

Cited by 33 publications

(42 citation statements)

References 57 publications

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“…Our group followed this approach in all our previous publications on EEG microstates (Brodbeck et al, 2012; Kuhn et al, 2015; von Wegner et al, 2017). To find local maxima of the GFP time series, we first compute the discrete time derivative δ i = σ i +1 − σ i , and then identify local GFP maxima as the set of time points I max where the sign of δ i crosses from positive to negative values, i.e.,…”

Section: Methodsmentioning

confidence: 99%

“…Among these, the reader can find resting-state experiments (Britz et al, 2010; Musso et al, 2010; Van de Ville et al, 2010; Brodbeck et al, 2012), different task-related conditions (Dimitriadis et al, 2013, 2015; Milz et al, 2015; Dimitriadis and Salis, 2017) and sleep (Brodbeck et al, 2012). Clinical conditions include schizophrenia (Koenig et al, 1999), Alzheimer's disease (Nishida et al, 2013), and narcolepsy (Kuhn et al, 2015; Drissi et al, 2016). An overview of the field has recently been published in two reviews (Khanna et al, 2015; Michel and Koenig, 2017).…”

Section: Introductionmentioning

confidence: 99%

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EEG Microstate Sequences From Different Clustering Algorithms Are Information-Theoretically Invariant

Wegner

Knaut

Laufs

2018

Front. Comput. Neurosci.

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We analyse statistical and information-theoretical properties of EEG microstate sequences, as seen through the lens of five different clustering algorithms. Microstate sequences are computed for n = 20 resting state EEG recordings during wakeful rest. The input for all clustering algorithms is the set of EEG topographic maps obtained at local maxima of the spatial variance. This data set is processed by two classical microstate clustering algorithms (1) atomize and agglomerate hierarchical clustering (AAHC) and (2) a modified K-means algorithm, as well as by (3) K-medoids, (4) principal component analysis (PCA) and (5) fast independent component analysis (Fast-ICA). Using this technique, EEG topographies can be substituted with microstate labels by competitive fitting based on spatial correlation, resulting in a symbolic, non-metric time series, the microstate sequence. Microstate topographies and symbolic time series are further analyzed statistically, including static and dynamic properties. Static properties, which do not contain information about temporal dependencies of the microstate sequence include the maximum similarity of microstate maps within and between the tested clustering algorithms, the global explained variance and the Shannon entropy of the microstate sequences. Dynamic properties are sensitive to temporal correlations between the symbols and include the mixing time of the microstate transition matrix, the entropy rate of the microstate sequences and the location of the first local maximum of the autoinformation function. We also test the Markov property of microstate sequences, the time stationarity of the transition matrix and detect periodicities by means of time-lagged mutual information. Finally, possible long-range correlations of microstate sequences are assessed via Hurst exponent estimation. We find that while static properties partially reflect properties of the clustering algorithms, information-theoretical quantities are largely invariant with respect to the clustering method used. As each clustering algorithm has its own profile of computational speed, ease of implementation, determinism vs. stochasticity and theoretical underpinnings, our results convey a positive message concerning the free choice of method and the comparability of results obtained from different algorithms. The invariance of these quantities implies that the tested properties are algorithm-independent, inherent features of resting state EEG derived microstate sequences.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EEG Microstate Sequences From Different Clustering Algorithms Are Information-Theoretically Invariant

Wegner

Knaut

Laufs

2018

Front. Comput. Neurosci.

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“…Resting state EEG studies typically produce four consistently observable microstates, thought to originate from the abstract thoughts that typically arise during unstructured rest [91] and whose topographies have been arbitrarily labeled A, B, C, and D ( Figure 2) [92]- [94] . These four microstates are consistently observable and can be observed even in sleep [95] . [97] Previous studies in diseased populations have all observed changes in mean duration, frequency of occurrence, ratio of total time, and transition probability in patients relative to controls, which they interpreted as underlying changes in resting state brain dynamics characteristic of the disease (See Khanna et al [96] for a more complete review of the current resting state EEG microstate literature.…”

Section: Eeg Microstatesmentioning

confidence: 75%

Brain Networks and Dynamics in Narcolepsy

Drissi

2019

Linköping University Medical Dissertations

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Populärvetenskaplig sammanfattningNarkolepsi är en kronisk sömnsjukdom där den drabbade lider av obetvinglig sömnighet. Det gör att personer med sjukdomen blir mycket trötta under dagen och får sömnattacker som inte går att förhindra. Till symtomen hör också kataplexi som innebär plötslig kraftlöshet och en känsla av förlamning i samband med känslor som glädje och ilska. En del får hallucinationer strax innan de somnar eller vaknar.Narkolepsi delas in i två typer. Typ 1 beror på en autoimmun reaktion som innebär att kroppens immunsystem angriper de hjärnceller som producerar ett hormon som reglerar vakenhet och sömn. Personer som fick symtom efter att ha vaccinerats med vaccinet Pandemrix har narkolepsi typ 1. Narkolepsipatienter beskriver också ofta andra icke-sömnrelaterade besvär, så som svårigheter med koncentration och minnet, men också kraftig viktuppgång trots att de inte äter mer. I den här avhandlingen har en grupp narkolepsipatienter 13-20 år med narkolepsi typ 1 undersökts med magnetkamera som har mätt hjärnans struktur och aktivitet, samt undersökt sammansättning av kroppsfett.Brunt fett, också kallat aktivt fett, för att det ökar ämnesomsättningen påverkas i djur som har narkolepsi och vi trodde att det kunde vara brist på brunt fett hos narkolepsipatienter som gör att de går upp i vikt. Resultaten från mätningen av kroppsfett visade att narkolepsipatienterna inte hade mindre brunt fett än motsvarande friska individer. Däremot så hade de högre bukfetma, även om förhållandet mellan det "dåliga" fettet (som ökar risk för hjärt och kärlsjukdomar och diabetes typ 2) och det mer neutrala underhudsfettet var bättre hos narkolepsipatienterna. Resultaten visar också att narkolepsipatienter har flera avvikelser i de områden i hjärnan som behandlar uppmärksamhet. Detta påverkar troligtvis deras förmåga att ta in information och kan upplevas som att de har "dålig minne". Ett av de områden som vi fann avvikelser i har möjligheter för att kunna användas som en ny behandling för koncentrationssvårigheter i narkolepsi typ 1.ii Abstract Narcolepsy is a chronic sleep disorder, characterised by excessive daytime sleepiness with frequent uncontrollable sleep attacks. In addition to sleeprelated problems, changes in cognition have also been observed in patients with narcolepsy and have been linked to the loss of Orexin-A in a number of studies. Results from previous functional and structural neuroimaging studies would suggest that the loss of Orexin-A has numerous downstream effects in terms of both resting state glucose metabolism and perfusion and reduction in cortical grey matter. Specifically, studies investigating narcolepsy with positron emission tomography (PET) and single photon emission computed tomography (SPECT) have observed aberrant perfusion and glucose metabolism in the hypothalamus and thalamus, as well as in prefrontal cortex. A very recent PET study in a large cohort of adolescents with type 1 narcolepsy further observed that the hypo-and hypermetabolism in many of these cortico-frontal and subcortical...

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“…Some research in the resting state EEG defined the brain activity as 4 types of microstates [ 6 – 9 , 11 , 34 , 35 , 38 , 39 ], corresponding to the 4 resting state networks; Meanwhile, in task states, the number of microstates is usually unknown, and to find the optimal cluster number, researchers often use the cross validation method [ 32 , 40 ].…”

Section: Methodsmentioning

confidence: 99%

Calculation and Analysis of Microstate Related to Variation in Executed and Imagined Movement of Force of Hand Clenching

Chen

Xiong

2018

Computational Intelligence and Neuroscience

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Objective In order to investigate electroencephalogram (EEG) instantaneous activity states related to executed and imagined movement of force of hand clenching (grip force: 4 kg, 10 kg, and 16 kg), we utilized a microstate analysis in which the spatial topographic map of EEG behaves in a certain number of discrete and stable global brain states. Approach Twenty subjects participated in EEG collection; the global field power of EEG and its local maximum were calculated and then clustered using cross validation and statistics; the 4 parameters of each microstate (duration, occurrence, time coverage, and amplitude) were calculated from the clustering results and statistically analyzed by analysis of variance (ANOVA); finally, the relationship between the microstate and frequency band was analyzed. Main Results The experimental results showed that all microstates related to executed and imagined grip force tasks were clustered into 3 microstate classes (A, B, and C); these microstates generally transitioned from A to B and then from B to C. With the increase of the target value of executed and imagined grip force, the duration and time coverage of microstate B gradually decreased, while these parameters of microstate C gradually increased. The occurrence times of microstate B and C related to executed grip force were significantly more than those related to imagined grip force; furthermore, the amplitudes of these 3 microstates related to executed grip force were significantly greater than those related to imagined grip force. The correlation coefficients between the microstates and the frequency bands indicated that the microstates were correlated to mu rhythm and beta frequency bands, which are consistent with event-related desynchronization/synchronization (ERD/ERS) phenomena of sensorimotor rhythm. Significance It is expected that this microstate analysis may be used as a new method for observing EEG instantaneous activity patterns related to variation in executed and imagined grip force and also for extracting EEG features related to these tasks. This study may lay a foundation for the application of executed and imagined grip force training for rehabilitation of hand movement disorders in patients with stroke in the future.

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Narcoleptic Patients Show Fragmented EEG-Microstructure During Early NREM Sleep

Cited by 33 publications

References 57 publications

EEG Microstate Sequences From Different Clustering Algorithms Are Information-Theoretically Invariant

EEG Microstate Sequences From Different Clustering Algorithms Are Information-Theoretically Invariant

Brain Networks and Dynamics in Narcolepsy

Calculation and Analysis of Microstate Related to Variation in Executed and Imagined Movement of Force of Hand Clenching

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