MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale

+microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG

+microstate is a MATLAB toolbox for brain functional microstate analysis. It builds upon previous EEG microstate literature and toolboxes by including algorithms for source-space microstate analysis. +microstate includes codes for performing individual- and group-level brain microstate analysis in resting-state and task-based data including event-related potentials/fields. Functions are included to visualise and perform statistical analysis of microstate sequences, including novel advanced statistical approaches such as statistical testing for associated functional connectivity patterns, cluster-permutation topographic ANOVAs, and analysis of microstate probabilities in response to stimuli. Additionally, codes for simulating microstate sequences and their associated M/EEG data are included in the toolbox, which can be used to generate artificial data with ground truth microstates and to validate the methodology. +microstate integrates with widely used toolboxes for M/EEG processing including Fieldtrip, SPM, LORETA/sLORETA, EEGLAB, and Brainstorm to aid with accessibility, and includes wrappers for pre-existing toolboxes for brain-state estimation such as Hidden Markov modelling (HMM-MAR) and independent component analysis (FastICA) to aid with direct comparison with these techniques. In this paper, we first introduce +microstate before subsequently performing example analyses using open access datasets to demonstrate and validate the methodology. MATLAB live scripts for each of these analyses are included in +microstate, to act as a tutorial and to aid with reproduction of the results presented in this manuscript

Using multiple short epochs optimises the stability of infant EEG connectivity parameters

Atypicalities in connectivity between brain regions have been implicated in a range of neurocognitive disorders. We require metrics to assess stable individual differences in connectivity in the developing brain, while facing the challenge of limited data quality and quantity. Here, we examine how varying core processing parameters can optimise the test-retest reliability of EEG connectivity measures in infants. EEG was recorded twice with a 1-week interval between sessions in 10- month-olds. EEG alpha connectivity was measured across different epoch lengths and numbers, with the phase lag index (PLI) and debiased weighted PLI (dbWPLI), for both whole-head connectivity and graph theory metrics. We calculated intra-class correlations between sessions for infants with sufficient data for both sessions (N’s = 19 – 41, depending on the segmentation method). Reliability for the whole brain dbWPLI was higher across many short epochs, whereas reliability for the whole brain PLI was higher across fewer long epochs. However, the PLI is confounded by the number of available segments. Reliability was higher for whole brain connectivity than graph theory metrics. Thus, segmenting available data into a high number of short epochs and calculating the dbWPLI is most appropriate for characterising connectivity in populations with limited availability of EEG data

MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale

Test-retest reliability of EEG network characteristics in infants

Functional Electroencephalography (EEG) networks in infants have been proposed as useful biomarkers for developmental brain disorders. However, the reliability of these networks and their characteristics has not been established. We evaluated the reliability of these networks and their characteristics in 10-month-old infants. Data were obtained during two EEG sessions 1 week apart and was subsequently analyzed at delta (0.5-3 Hz), theta (3-6 Hz), alpha1 (6-9 Hz), alpha2 (9-12 Hz), beta (12-25 Hz), and low gamma (25-45 Hz) frequency bands. Connectivity matrices were created by calculating the phase lag index between all channel pairs at given frequency bands. To determine the reliability of these connectivity matrices, intra-class correlations were calculated of global connectivity, local connectivity, and several graph characteristics. Comparing both sessions, global connectivity, as well as global graph characteristics (characteristic path length and average clustering coefficient) are highly reliable across multiple frequency bands; the alpha1 and theta band having the highest reliability in general. In contrast, local connectivity characteristics were less reliable across all frequency bands. We conclude that global connectivity measures are highly reliable over sessions. Local connectivity measures show lower reliability over sessions. This research therefore underlines the possibility of these global network characteristics to be used both as biomarkers of neurodevelopmental disorders, but also as important factors explaining development of typical behavior. [Abstract copyright: © 2019 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.

EEG Microstates Indicate Heightened Somatic Awareness in Insomnia: Toward Objective Assessment of Subjective Mental Content

People with Insomnia Disorder (ID) not only experience abundant nocturnal mentation, but also report altered spontaneous mental content during daytime wakefulness, such as an increase in bodily experiences (heightened somatic awareness). Previous studies have shown that resting-state EEG can be temporally partitioned into quasi-stable microstates, and that these microstates form a small number of canonical classes that are consistent across people. Furthermore, the microstate classes have been associated with individual differences in resting mental content including somatic awareness. To address the hypothesis that altered resting mental content in ID would be reflected in an altered representation of the corresponding EEG microstates, we analyzed resting-state high-density EEG of 32 people with ID and 32 age- and sex-matched controls assessed during 5-min eyes-closed wakefulness. Using data-driven topographical k-means clustering, we found that 5 microstate classes optimally explained the EEG scalp voltage map sequences across participants. For each microstate class, 3 dynamic features were obtained: mean duration, frequency of occurrence, and proportional coverage time. People with ID had a shorter mean duration of class C microstates, and more frequent occurrence of class D microstates. The finding is consistent with previously established associations of these microstate properties with somatic awareness, and increased somatic awareness in ID. EEG microstate assessment could provide objective markers of subjective experience dimensions in studies on consciousness during the transition between wake and sleep, when self-report is not possible because it would interfere with the very process under study. Addressing somatic awareness may benefit psychotherapeutic treatment of insomnia

+microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG

+microstate is a MATLAB toolbox for brain functional microstate analysis. It builds upon previous EEG microstate literature and toolboxes by including algorithms for source-space microstate analysis. +microstate includes codes for performing individual- and group-level brain microstate analysis in resting-state and task-based data including event-related potentials/fields. Functions are included to visualise and perform statistical analysis of microstate sequences, including novel advanced statistical approaches such as statistical testing for associated functional connectivity patterns, cluster-permutation topographic ANOVAs, an

A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Imaging for Modeling Brain Activities

Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits. In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the advantages of each one. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events and capture the brain response under specific experimental conditions. Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results. EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from each one is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented in this work. A HMM modeling approach is used in order to derive a novel feature-based representation of the EEG signal that characterizes the topographic information of the EEG. We use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI is studied using CPLS. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of mental tasks. Extensions on the proposed models are examined along with future research directions and applications

Neuropsychologische, neuropsychiatrische und elektroencephalografische Aspekte der Apathie bei Patienten mit der Parkinsonerkrankung

Einleitung. Zu den nicht-motorischen Symptomen (NMS) bei der Parkinsonerkrankung (PK) zählen Apathie und kognitive Störungen. Apathie ist ein Prädiktor für die Entwicklung der Demenz bei der PK und geht mit einer erheblich verminderten Lebensqualität einher. Eine einheitliche Terminologie und ein standardisiertes diagnostisches Procedere sowie auch eine evidenzbasierte Therapie fehlen. Die Dissertation hat zum Ziel zu untersuchen, ob Apathie mit der Checkliste zur Erfassung neuropsychiatrischer Symptome bei der PK (CENS-PE) valide erfasst werden kann und ob Apathie mit spezifischen exekutiven Prozesse (i. e. Initiieren, Hemmen, Wechseln) und/oder Depression assoziiert ist. Verbal-episodische Gedächtnisdefizite treten bei nicht-dementen Patienten mit der PK häufig auf. Ein weiteres Ziel war zu untersuchen ob diese, wie bei der Alzheimerkrankheit (AK), durch defizitäre Konsolidierung bedingt sind. Methode. Patienten mit idiopathischem Parkinsonsyndrom wurden in die verschiedenen Studien eingeschlossen. Sie wurden zwischen 2011 und 2015 im Rahmen der Basler Trainingsstudien, beziehungsweise in der Sprechstunde für Bewegungsstörungen auf der Neurologie des Universitätsspitals Basel untersucht. Für Studie 4 wurden zusätzlich gesunde Kontrollen und Patienten mit einer milden AK von der Memory Clinic, Universitäre Altersmedizin und Rehabilitation, Basel, rekrutiert und untersucht. Alle Studienteilnehmer durchliefen eine umfangreiche Abklärung mit neuropsychologischen, neuropsychiatrischen und neurologischen Untersuchungen. Zusätzlich wurde ein Elektroencephalogramm (EEG) mit 256 Elektroden abgeleitet. Die Analysen beinhalteten Korrelationsanalysen, Regressionsmodelle mit schrittweiser Selektion der relevanten Variablen und Varianzanalysen. Resultate. Die Items der CENS-PE (Studie 1) erwiesen sich als homogen und trennscharf, die interne Konsistenz als akzeptabel und die Konstruktvalidität der Stimmung/Apathie Domäne konnte nachgewiesen werden. In Studie 2 zeigte sich, dass der exekutive Prozess Initiieren ein signifikanter Prädiktor für Apathie ist, hingegen war kein Zusammenhang zwischen Depression und Apathie nachweisbar. Es konnte eine Abnahme des Phase Lag Index (i. e. Mass für Konnektivität) in links-frontalen Hirnregionen im Zusammenhang mit der Apathie und dem exekutiven Prozess Initiieren nachgewiesen werden (Studie 3). In Studie 4 war ein signifikant verminderter Primacy Effekt (i. e. Mass für Konsolidierung) bei Patienten mit kognitiver Störung bei der PK und Patienten mit der AK im Vergleich zu Patienten ohne kognitive Störung bei der PK und gesunden Kontrollen nachweisbar. Diskussion. Die Studienergebnisse legitimieren den Einsatz der CENS-PE zur Erfassung der Apathie im klinischen Alltag. Das klinische Bild der Apathie bei der PK manifestiert sich mit einer Verhaltensänderung im Sinne einer Initiierungsstörung, welches von der Depression abgrenzbar ist, und mit einer Dysfunktion des frontal-subkortikalen Schaltkreises einhergeht. Für die verbal-episodischen Gedächtnisstörungen scheint ein Defizit der Konsolidierung verantwortlich zu sein, entsprechend dem Muster, welches bei den Patienten mit der AK beobachtbar ist. Die Ergebnisse weisen darüber hinaus auf die Wichtigkeit hin, spezifische kognitive Prozesse im Zusammenhang mit den NMS bei der PK zu analysieren, dadurch können wichtige Informationen zu den pathophysiologischen Grundlagen und folglich auch zu möglichen Behandlungsstrategien gewonnen werden. Introduction. Apathy and cognitive dysfunctions are frequent and debilitating non-motor symptoms (NMS) in patients with Parkinson’s disease (PD). Apathy has a significant impact on quality of life and is a predictive factor for cognitive deterioration in PD. A consistent terminology, diagnostic procedures as well as evidence based treatment strategies are still largely lacking. The aim of this thesis was to analyse the validity of the Mood/Apathy domain and the psychometric characteristics of the Scale for Evaluation of Neuropsychiatric Disorders in Parkinson’s disease (SEND-PD) and to investigate whether apathy in PD is a subdomain of either depression and/or of specific executive processes (i. e. initiation, shifting, inhibition). Non-demented PD patients frequently exhibit deficits in verbal episodic memory. We aimed to investigate whether these deficits are due to deficient memory consolidation in patients with PD, similar to the pattern observed in patients with Alzheimer’s disease (AD). Methods. Patients with idiopathic PD were recruited between 2011 and 2013. The patients were either participants of a training study or outpatients from the movement disorders clinic, Department of Neurology, Hospital of the University, Basel. For study 4, we additionally included outpatients with very mild AD and healthy controls. They were recruited from the Memory Clinic, University Center for Medicine of Aging, Basel. All participants had extensive neuropsychological, neuropsychiatric and neurological testing. A resting state electroencephalography (EEG) was recorded with 256 electrodes. Statistical analyses included correlation analyses and linear regression model with stepwise elimination procedure and analyses of variance. Results. In study 1 the psychometric characteristics of the SEND-PD as well as aspects of validity of the Mood/Apathy domain were evaluated. The items of the scale were homogenous, selective and the domains showed acceptable internal consistency. The constructive validity of the Mood/Apathy domain showed good values. Study 2 revealed influences of the executive process initiation but not of depression or motor impairment on apathy in PD. Study 3 further investigated the relationship between apathy and executive process initiation with changes in brain networks. Lower connectivities (measured by Phase Lag Index) involving the left frontal region were related to higher apathy scores as well as to a severer deficit of initiation. In study 4, a significant attenuation of the primacy effect (i. e. measure for memory consolidation) was detectable in patients with PD and in patients with very mild AD. Discussion. The German version of the SEND-PD is sufficiently reliable and valid to be adopted in German speaking countries. The findings indicate, that initiation dysfunction in patients with PD heralds beginning apathy, probably due to a dysfunction of the cortico-basal loop. The present thesis corroborate the assumption, that depression and apathy can be dissociated. Verbal episodic memory deficits may reflect deficient memory consolidation, similar to the mechanism observed in AD patients. To conclude, the results confirm relationships between specific cognitive processes and NMS in PD, which may gain information of pathological underpinnings and may guide treatment