Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index: a simulation study

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The investigation of functional neuronal synchronization has recently become a growing field of research. With high temporal resolution, electroencephalography and magnetoencephalography are well-suited measurement techniques to identify networks of interacting sources underlying the recorded data. The analysis of the data in terms of effective connectivity, nevertheless, contains intrinsic issues such as the problem of volume conduction and the non-uniqueness of the inverse solution. Here, we briefly introduce a series of existing methods assessing these problems. To determine the locations of interacting brain sources robust to volume conduction, all computations are solely based on the imaginary part of the cross-spectrum as a trustworthy source of information. Furthermore, we demonstrate the feasibility of estimating causal relationships of systems of neuronal sources with the phase slope index in realistically simulated data. Finally, advantages and drawbacks of the applied methodology are highlighted and discussed

Biological Earth observation with animal sensors

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmen-tal change

Neue multivariate Datenanalyseverfahren zur Bestimmung von funktionell verknüpften Netzwerken im Gehirn auf der Basis von EEG oder MEG Daten

Die Funktionsweise des Gehirns wird maßgeblich durch das Zusammenspiel von verschiedenen Gehirnregionen bestimmt. Ein Kommunikationsmechanismus ist dabei die Synchronisation von oszillatorischen Signalen, die von großen Neuronenpopulationen erzeugt werden. Mit ihrer hervorragenden zeitlichen Auflösung im Bereich von Millisekunden und ihrer Nicht-Invasivität sind die elektrophysiologischen Messmodalitäten Elektroenzephalografie (EEG) und Magnetoenzephalografie (MEG) gut geeignete Werkzeuge, um diese Synchronisationseffekte zu untersuchen. Die Sensoren an der Kopfoberfläche messen jedoch eine Mischung der im Gehirn generierten Quellsignale. Durch diesen sogenannten Volumenleitungseffekt ist es weder möglich, die Quellen aus den Sensorsignalen eindeutig zu rekonstruieren noch Beziehungen (Konnektivität) zwischen ihnen abzuleiten. Die vorliegende Arbeit stellt ein Reihe multivariater Datenanalysemethoden vor, um Synchronisation und damit Interaktionen zwischen verschiedenen Gehirnarealen zu erfassen. Alle Methoden basieren auf einem Konzept, dass einem etablierten Konnektivitätsmaß, dem sogenannten Imaginärteil der Kohärenz (englische Abkürzung: ImCoh), zu Grunde liegt. Der ImCoh vernachlässigt gleichzeitig stattfindende Synchronisationseffekte, da sie höchstwahrscheinlich auf Volumenleitungsartefakten beruhen, während echte neuronale Synchronisation im Gegensatz dazu zeitversetzt stattfindet. Im Wesentlichen werden in dieser Arbeit vier verschiedenen Methoden präsentiert, die nacheinander benutzt werden können. Zunächst wird die Maximierung des ImCohs durch einen optimalen räumlichen Filter beschrieben. Damit wird das Signal-Rausch-Verhältnis der Daten für anschließende Konnektivitätsanalysen und für die Quellrekonstruktion verbessert. Weiterhin werden aus der vorgestellten Theorie dieser Maximierung verschiedene neue Konnektivitätsmaße abgeleitet, auf die die eigentliche Mischung der Quellsignale in die Sensoren keinerlei Einfluss hat. Mit einem dieser Maße, dem Global Interaction Measure (GIM) lassen sich aufgrund dieser Eigenschaften die Messmodalitäten EEG und MEG valide vergleichen. Weiterhin können mit dem GIM versuchspersonenspezifische Frequenzbänder, bei denen eine neuronale Synchronisation stattfindet, automatisch detektiert werden. In einem zweiten Schritt werden die Gehirnquellen in dem zuvor bestimmten Frequenzband mit einem neuen Analyseverfahren namens Self Consistent Multiple Signal Classification (SC Music) lokalisiert. Dieses stellt eine algorithmische Erweiterung des bereits bestehenden Verfahrens Rap Music (Recursively Applied Multiple Signal Classification) dar. Im Vergleich zu Rap Music verringert SC Music den Einfluss anderer synchroner Quellen und verbessert so die Lokalisation. Drittens adressiert die neu eingeführte Methode Wedge Music die Frage, welche der vorher bestimmten Quellen wirklich interagieren. Es wird gezeigt, dass sowohl SC Music als auch Wedge Music im Gegensatz zu Maßen die auf dem eigentlichen ImCoh basieren in der Lage sind, Interaktionsunterschiede zwischen verschiedenen experimentellen Bedingungen zu ermitteln. Viertens wird eine Methode zur statistischen Validierung der Ergebnisse vorgestellt. Dies geschieht mit Hilfe von Surrogatdaten, die auf Basis gemessener Daten erzeugt werden. Bei den Surrogatdaten werden jegliche zu Grunde liegende Interaktionen künstlich zerstört, während andere statistische Eigenschaften der Daten erhalten bleiben. Alle angesprochenen Methoden werden theoretisch eingeführt und in Simulationen evaluiert. Weiterhin wird die Anwendbarkeit exemplarisch anhand von echten EEG and MEG Daten demonstriert.To understand the functionality of the brain, it is crucial to know how sources of ongoing activity inside the brain interplay. The synchronization of oscillatory signals generated by large populations of neurons has been identified to serve as a communication mechanism. Due to their excellent temporal resolution in the millisecond range and their non-invasiveness, the electrophysiological measurement modalities Electroencephalography (EEG) and Magnetoencephalography (MEG) are well-suited tools to investigate these synchronization effects. However, due to the mixing of source signals inside the brain into measurement sensors outside the head which is called volume conduction, it is neither possible to uniquely reconstruct the sources nor to study relationships among them. Artifacts of volume conduction impede the interpretation of relationships between sensors as well as between estimated sources. Within this thesis a novel series of multivariate data analysis methods is introduced that aims at detecting synchronization between large-scale brain sources robust to any volume conduction artifacts. All methods are based upon the concept of an established connectivity measure called the imaginary part of coherency (ImCoh). The ImCoh neglects instantaneous synchronization effects as they are most likely due to source mixing as true source synchronization usually requires some time to evolve. The computational procedure presented in this thesis is constructed out of four individual methods which can be used sequentially. First, the maximization of the imaginary part of coherency is introduced which leads to an increase of the signal-to-noise ratio for subsequent connectivity analysis and source localization. Furthermore, connectivity measures are derived from this maximization, which are, in contrast to the ImCoh, independent of the particular source mixing. Due to these properties, one of the measures, the Global Interaction Measure (GIM), is used to objectively compare the measurement modalities EEG and MEG. Moreover, the GIM serves as a basis to automatically determine subject specific frequency peaks of synchronization effects. Second, synchronized sources are determined with a novel technique called Self Consistent Multiple Signal Classification (SC Music). It forms an algorithmic improvement over the existing data analysis technique Rap Music (Recursively Applied Multiple Signal Classification) by diminishing the influence of other sources during the localization procedure. Third, a method called Wedge Music is used to address the question which of the sources, prior determined with SC Music, are actually interacting. Additionally, it is shown that both SC and Wedge Music are, in contrast to measures based on the classical ImCoh, capable of examining differences in interaction between different experimental conditions. Fourth, a method is presented to statistically test the results. This is achieved by generating surrogate data from the real data such that data properties are maintained but all interactions are artificially destroyed. All methods are introduced theoretically and are validated in simulations. Finally, the applicability is demonstrated on real EEG and MEG data

Localizing True Brain Interactions from EEG and MEG Data with Subspace Methods and Modified Beamformers

To address the problem of mixing in EEG or MEG connectivity analysis we exploit that noninteracting brain sources do not contribute systematically to the imaginary part of the cross-spectrum. Firstly, we propose to apply the existing subspace method “RAP-MUSIC” to the subspace found from the dominant singular vectors of the imaginary part of the cross-spectrum rather than to the conventionally used covariance matrix. Secondly, to estimate the specific sources interacting with each other, we use a modified LCMV-beamformer approach in which the source direction for each voxel was determined by maximizing the imaginary coherence with respect to a given reference. These two methods are applicable in this form only if the number of interacting sources is even, because odd-dimensional subspaces collapse to even-dimensional ones. Simulations show that (a) RAP-MUSIC based on the imaginary part of the cross-spectrum accurately finds the correct source locations, that (b) conventional RAP-MUSIC fails to do so since it is highly influenced by noninteracting sources, and that (c) the second method correctly identifies those sources which are interacting with the reference. The methods are also applied to real data for a motor paradigm, resulting in the localization of four interacting sources presumably in sensory-motor areas

EEG alpha spindles and prolonged brake reaction times during auditory distraction in an on-road driving study

Driver distraction is responsible for a substantial number of traffic accidents. This paper describes the impact of an auditory secondary task on drivers’ mental states during a primary driving task. N = 20 participants performed the test procedure in a car following task with repeated forced braking on a non-public test track. Performance measures (provoked reaction time to brake lights) and brain activity (EEG alpha spindles) were analyzed to describe distracted drivers. Further, a classification approach was used to investigate whether alpha spindles can predict drivers’ mental states. Results show that reaction times and alpha spindle rate increased with time-on-task. Moreover, brake reaction times and alpha spindle rate were significantly higher while driving with auditory secondary task opposed to driving only. In single-trial classification, a combination of spindle parameters yielded a median classification error of about 8% in discriminating the distracted from the alert driving. Reduced driving performance (i.e., prolonged brake reaction times) during increased cognitive load is assumed to be indicated by EEG alpha spindles, enabling the quantification of driver distraction in experiments on public roads without verbally assessing the drivers’ mental states

Humoral Immune Response in IBD Patients Three and Six Months after Vaccination with the SARS-CoV-2 mRNA Vaccines mRNA-1273 and BNT162b2

Severe acute respiratory syndrome coronovirus-2 (SARS-CoV-2) is the cause of the coronavirus disease 2019 (COVID-19) pandemic. Vaccination is considered the core approach to containing the pandemic. There is currently insufficient evidence on the efficacy of these vaccines in immunosuppressed inflammatory bowel disease (IBD) patients. The aim of this study was to investigate the humoral response in immunosuppressed IBD patients after COVID-19 mRNA vaccination. In this prospective study, IgG antibody levels (AB) against the SARS-CoV-2 receptor-binding domain (spike-protein) were quantitatively determined. For assessing the potential neutralizing capacity, a SARS-CoV-2 surrogate neutralization test (sVNT) was employed in IBD patients (n = 95) and healthy controls (n = 38). Sera were examined prior to the first/second vaccination and 3/6 months after second vaccination. Patients showed lower sVNT (%) and IgG-S (AU/mL) AB both before the second vaccination (sVNT p p p = 0.002; AB p = 0.001) and 6 months (sVNT p = 0.062; AB p = 0.061) after the second vaccination. Although seroconversion rates (sVNT, IgG-S) did not differ between the two groups 3 months after second vaccination, a significant difference was seen 6 months after second vaccination (sVNT p = 0.045). Before and three months after the second vaccination, patients treated with anti-tumor necrosis factor (TNF) agents showed significantly lower AB than healthy subjects. In conclusion, an early booster shot vaccination should be discussed for IBD patients on anti-TNF therapy