Reducing the Effect of Spurious Phase Variations in Neural Oscillatory Signals

The phase-reset model of oscillatory EEG activity has received a lot of attention in the last decades for decoding different cognitive processes. Based on this model, the ERPs are assumed to be generated as a result of phase reorganization in ongoing EEG. Alignment of the phase of neuronal activities can be observed within or between different assemblies of neurons across the brain. Phase synchronization has been used to explore and understand perception, attentional binding and considering it in the domain of neuronal correlates of consciousness. The importance of the topic and its vast exploration in different domains of the neuroscience presses the need for appropriate tools and methods for measuring the level of phase synchronization of neuronal activities. Measuring the level of instantaneous phase (IP) synchronization has been used extensively in numerous studies of ERPs as well as oscillatory activity for a better understanding of the underlying cognitive binding with regard to different set of stimulations such as auditory and visual. However, the reliability of results can be challenged as a result of noise artifact in IP. Phase distortion due to environmental noise artifacts as well as different pre-processing steps on signals can lead to generation of artificial phase jumps. One of such effects presented recently is the effect of low envelope on the IP of signal. It has been shown that as the instantaneous envelope of the analytic signal approaches zero, the variations in the phase increase, effectively leading to abrupt transitions in the phase. These abrupt transitions can distort the phase synchronization results as they are not related to any neurophysiological effect. These transitions are called spurious phase variation. In this study, we present a model to remove generated artificial phase variations due to the effect of low envelope. The proposed method is based on a simplified form of a Kalman smoother, that is able to model the IP behavior in narrow-bandpassed oscillatory signals. In this work we first explain the details of the proposed Kalman smoother for modeling the dynamics of the phase variations in narrow-bandpassed signals and then evaluate it on a set of synthetic signals. Finally, we apply the model on ongoing-EEG signals to assess the removal of spurious phase variations

Generalized time-frequency coherency for assessing neural interactions in electrophysiological recordings

Time-frequency coherence has been widely used to quantify statistical dependencies in bivariate data and has proven to be vital for the study of neural interactions in electrophysiological recordings. Conventional methods establish time-frequency coherence by smoothing the cross and power spectra using identical smoothing procedures. Smoothing entails a trade-off between time-frequency resolution and statistical consistency and is critical for detecting instantaneous coherence in single-trial data. Here, we propose a generalized method to estimate time-frequency coherency by using different smoothing procedures for the cross spectra versus power spectra. This novel method has an improved trade-off between time resolution and statistical consistency compared to conventional methods, as verified by two simulated data sets. The methods are then applied to single-trial surface encephalography recorded from human subjects for comparative purposes. Our approach extracted robust alpha- and gamma-band synchronization over the visual cortex that was not detected by conventional methods, demonstrating the efficacy of this method

Neuronal assembly dynamics in supervised and unsupervised learning scenarios

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions

Assessing neural network dynamics under normal and altered states of consciousness with MEG : methodological challenges and proposed solutions for atypical power spectra

Cette dernière décennie a vu un certain nombre d'avancées significatives en mathématiques, en apprentissage computationnel et en traitement de signal, qui n'ont pas encore été pleinement exploitées en neurosciences. En particulier, l'évaluation de la connectivité dans les réseaux neuronaux peut grandement bénéficier de ces travaux. Nous proposons ici d'exploiter ces outils pour combler partiellement le fossé considérable qui existe encore entre la recherche connectomique à grande échelle (largement centrée sur des mesures indirectes de l'activité cérébrale comme l'Imagerie par résonance magnétique fonctionnelle (IRMf)) et les mesures physiologiques plus directes de l'activité cérébrale. Il est particulièrement important de combler ce fossé pour l'étude des propriétés physiologiques associées à divers états de conscience normaux et anormaux, notamment les troubles psychiatriques, le sommeil, l'anesthésie ou les états induits par les drogues. Les travaux récents sur l'induction d'états de conscience altérés par des agonistes non sélectifs de la sérotonine, tels que la psilocybine et le Diéthyllysergamide (LSD), en sont de bons exemples. Au cours des cinq dernières années, une résurgence rapide de la recherche sur la neurobiologie des tryptamines psychédéliques s'est produite, après une interruption d'un demi-siècle. Bien que ces substances présentent un grand potentiel pour éclairer des aspects jusqu'ici non interrogés du fonctionnement normal et anormal du cerveau, l'ampleur et le caractère inhabituel des changements qu'elles provoquent posent de sérieux défis aux chercheurs. La découverte de méthodes convaincantes et évolutives pour étudier ces données est d'une grande importance si nous voulons tirer parti de la fenêtre unique que ces substances atypiques offrent sur les aspects centraux de la conscience et des fonctions cérébrales anormales. Dans la présente thèse, nous résumons l'état actuel de la neuro-imagerie électrophysiologique en ce qui concerne l'étude des tryptamines psychédéliques, et nous démontrons un certain nombre de lacunes évidentes dans la recherche électrophysiologique actuelle sur les psychédéliques. Nous offrons également quelques modestes contributions méthodologiques au domaine. L'utilité de ces contributions est soutenue par quelques résultats empiriques intrigants, bien que préliminaires. Dans le premier chapitre, nous présentons l'histoire de la recherche neuroscientifique sur le LSD. Il a été rapporté que le LSD induit des déplacements de pics dans les spectres de puissance, en même temps que des diminutions de l'amplitude des pics. Le fait que ces effets soient liés entre eux et que la plupart des recherches menées jusqu'à présent n'aient pas cherché à les distinguer est uniformément négligé dans la littérature, ce qui, selon nous, peut conduire à de fausses interprétations. Le chapitre 2 examine certains des avantages plausibles ainsi que les obstacles sérieux à la recherche sur la connectivité du cerveau entier par magnétoencéphalographie (MEG), et propose plusieurs stratégies pour surmonter ces limites méthodologiques. Celles-ci comprennent des stratégies d'imagerie de source convaincantes, des développements nouveaux et récents dans la décomposition spectrale, des mesures de connectivité insensibles à la conduction volumique, et des implémentations évolutives de métriques de couplage interfréquence bien établies. Nous montrons que ces techniques peuvent être étendues à une grille corticale et sous-corticale de plus haute résolution que celle qui existe actuellement. Nous discutons également d'une mise en œuvre allégée de statistiques non paramétriques adaptées à ces données. Le troisième chapitre a pour but de démontrer l'efficacité de ces procédures, en montrant les résultats empiriques d'une étude de la connectivité du cerveau entier sous LSD par MEG. Le quatrième et dernier chapitre discute de ces résultats, ainsi que des précautions nécessaires et des orientations futures prometteuses pour ce type de recherche. Il propose des approches computationnelles supplémentaires qui pourraient étendre la portée de ces recherches et, plus généralement, de l'électrophysiologie du cerveau entier. Dans l'ensemble, le cadre méthodologique proposé dans ce travail surmonte les limitations endémiques précédentes, non seulement dans la recherche sur les psychédéliques, mais aussi dans la recherche électrophysiologique en général, et jette une lumière nouvelle sur sur les mécanismes centraux qui sous-tendent ces états de conscience anormaux, ainsi que sur les importantes précautions à prendre dans la recherche électrophysiologique.The past decade has seen a number of significant advances in mathematics, computational learning, and signal processing, which have yet to be deployed in neuroscience. In particular the assessment of connectivity in neural networks has much to gain from this work. Here we propose these tools be leveraged to partially bridge the considerable gap that still exists between large-scale connectomics research (largely centered around indirect measures of brain activity such as fMRI), and more direct, physiological measures of brain activity. Bridging this gap is especially important to the study of physiological properties associated with various normal and abnormal states of consciousness including Psychiatric conditions, sleep, anaesthesia or drug-induced states. Exemplary of such research, is recent work surrounding the induction of altered states of consciousness by non-selective serotonin agonists such as Psilocybin and LSD. During the past five years, a rapid resurgence of research into the neurobiology of Psychedelic tryptamines has transpired, following a half-century hiatus. While these substances hold great potential to illuminate hitherto uninterrogated aspects of normal and abnormal brain function, the scope and unusual character of the changes they illicit pose serious challenges to researchers. Uncovering cogent and scalable methods for investigating such data is a matter of great importance if we are to leverage the unique window such atypical substances provide into central aspects of consciousness and abnormal brain function. In the present thesis, we summarize the current state of electrophysiological neuroimaging as it pertains to the study of Psychedelic tryptamines, and demonstrate a number of clear shortcomings in current electrophysiological research on Psychedelics. We also offer some modest methodological contributions to the field. The utility of these contributions is supported by some intriguing, albeit preliminary, empirical findings. In the first chapter, we present the history of neuroscientific research on LSD. LSD has been reported to induce peak shifts in power spectra, alongside decreases in peak amplitude. The fact that these effects are inter-related and most research so far has not sought to disambiguate them is uniformly overlooked in the literature, which we believe may lead to false interpretations. Chapter Two discusses some of the plausible advantages as well as serious barriers to whole-brain connectivity research in MEG, proposing several strategies to overcome these methodological limitations. These include cogent source imaging strategies, novel and recent developments in spectral decomposition, connectivity measures insensitive to volume conduction, and scalable implementations of well-established cross-frequency coupling metrics. We show that these techniques can be extended to a higher resolution cortical and subcortical grid than previously shown. We also discuss a lightweight implementation of non-parametric statistics suitable to such data. Chapter Three serves to demonstrate the efficacy of these procedures, showing empirical results from a whole-brain study of connectivity under LSD in MEG. The fourth and final chapter discusses these results, as well as necessary precautions and promising future directions for this kind of research. It proposes additional computational approaches that might extend the scope of such research and whole-brain electrophysiology more generally. Taken together, the methodological framework proposed in this work overcomes previous limitations endemic not only in Psychedelics research, but electrophysiological research broadly, and sheds new light on central mechanisms underlying these abnormal states of consciousness, as well as important precautions in electrophysiological research

Decoding Electrophysiological Correlates of Selective Attention by Means of Circular Data

Sustaining our attention to a relevant sensory input in a complex listening environment, is of great importance for a successful auditory communication. To avoid the overload of the auditory system, the importance of the stimuli is estimated in the higher levels of the auditory system. Based on these information, the attention is drifted away from the irrelevant and unimportant stimuli. Long-term habituation, a gradual process independent from sensory adaptation, plays a major role in drifting away our attention from irrelevant stimuli. A better understanding of attention-modulated neural activity is important for shedding light on the encoding process of auditory streams. For instance, these information can have a direct impact on developing smarter hearing aid devices in which more accurate objective measures can be used to re ect the hearing capabilities of patients with hearing pathologies. As an example, an objective measures of long-term habituation with respect to di erent level of sound stimuli can be used more accurately for adjustment of hearing aid devices in comparison to verbal reports. The main goal of this thesis is to analyze the neural decoding signatures of long-term habituation and neural modulations of selective attention by exploiting circular regularities in electrophysiological (EEG) data, in which we can objectively measure the level of attentional-binding to di erent stimuli. We study, in particular, the modulations of the instantaneous phase (IP) in event related potentials (ERPs) over trials for di erent experimental settings. This is in contrast to the common approach where the ERP component of interest is computed through averaging a su ciently large number of ERP trials. It is hypothesized that a high attentional binding to a stimulus is related to a high level of IP cluster. As the attention binding reduces, IP is spread more uniformly on a unit circle. This work is divided into three main parts. In the initial part, we investigate the dynamics of long-term habituation with di erent acoustical stimuli (soft vs. loud) over ERP trials. The underlying temporal dynamics in IP and the level of phase cluster of the ERPs are assessed by tting circular probability functions (pdf) over data segments. To increase the temporal resolution of detecting times at which a signi cant change in IP occurs, an abrupt change point model at di erent pure-tone stimulations is used. In a second study, we improve upon the results and methodology by relaxing some of the constrains in order to integrate the gradual process of long-term habituation into the model. For this means, a Bayesian state-space model is proposed. In all of the aforementioned studies, we successfully classi ed between di erent stimulation levels, using solely the IP of ERPs over trials. In the second part of the thesis, the experimental setting is expanded to contain longer and more complex auditory stimuli as in real-world scenarios. Thereby, we study the neural-correlates of attention in spontaneous modulations of EEG (ongoing activity) which uses the complete temporal resolution of the signal. We show a mapping between the ERP results and the ongoing EEG activity based on IP. A Markov-based model is developed for removing spurious variations that can occur in ongoing signals. We believe the proposed method can be incorporated as an important preprocessing step for a more reliable estimation of objective measures of the level of selective attention. The proposed model is used to pre-process and classify between attending and un-attending states in a seminal dichotic tone detection experiment. In the last part of this thesis, we investigate the possibility of measuring a mapping between the neural activities of the cortical laminae with the auditory evoked potentials (AEP) in vitro. We show a strong correlation between the IP of AEPs and the neural activities at the granular layer, using mutual information.Die Aufmerksamkeit auf ein relevantes auditorisches Signal in einer komplexen H orumgebung zu lenken ist von gro er Bedeutung f ur eine erfolgreiche akustische Kommunikation. Um eine Uberlastung des H orsystems zu vermeiden, wird die Bedeutung der Reize in den h oheren Ebenen des auditorischen Systems bewertet. Basierend auf diesen Informationen wird die Aufmerksamkeit von den irrelevanten und unwichtigen Reizen abgelenkt. Dabei spielt die sog. Langzeit- Habituation, die einen graduellen Prozess darstellt der unabh angig von der sensorischen Adaptierung ist, eine wichtige Rolle. Ein besseres Verst andnis der aufmerksamkeits-modulierten neuronalen Aktivit at ist wichtig, um den Kodierungsprozess von sog. auditory streams zu beleuchten. Zum Beispiel k onnen diese Informationen einen direkten Ein uss auf die Entwicklung intelligenter H orsysteme haben bei denen genauere, objektive Messungen verwendet werden k onnen, um die H orf ahigkeiten von Patienten mit H orpathologien widerzuspiegeln. So kann beispielsweise ein objektives Ma f ur die Langzeit- Habituation an unterschiedliche Schallreize genutzt werden um - im Vergleich zu subjektiven Selbsteinsch atzungen - eine genauere Anpassung der H orsysteme zu erreichen. Das Hauptziel dieser Dissertation ist die Analyse neuronaler Dekodierungssignaturen der Langzeit- Habituation und neuronaler Modulationen der selektiver Aufmerksamkeit durch Nutzung zirkul arer Regularit aten in elektroenzephalogra schen Daten, in denen wir objektiv den Grad der Aufmerksamkeitsbindung an verschiedene Reize messen k onnen. Wir untersuchen insbesondere die Modulation der Momentanphase (engl. Instantaneous phase, IP) in ereigniskorrelierten Potenzialen (EKPs) in verschiedenen experimentellen Settings. Dies steht im Gegensatz zu dem traditionellen Ansatz, bei dem die interessierenden EKP-Komponenten durch Mittelung einer ausreichend gro en Anzahl von Einzelantworten im Zeitbereich ermittelt werden. Es wird vermutet, dass eine hohe Aufmerksamkeitsbindung an einen Stimulus mit einem hohen Grad an IP-Clustern verbunden ist. Nimmt die Aufmerksamkeitsbindung hingegen ab, so ist die Momentanphase uniform auf dem Einheitskreis verteilt. Diese Arbeit gliedert sich in drei Teile. Im ersten Teil untersuchen wir die Dynamik der Langzeit-Habituation mit verschiedenen akustischen Reizen (leise vs. laut) in EKP-Studien. Die zugrundeliegende zeitliche Dynamik der Momentanphase und die Ebene des Phasenclusters der EKPs werden durch die Anpassung von zirkul aren Wahrscheinlichkeitsfunktionen (engl. probability density function, pdf) uber Datensegmente bewertet. Mithilfe eines sog. abrupt change-point Modells wurde die zeitliche Au osung der Daten erh oht, sodass signi kante Anderungen in der Momentanphase bei verschiedenen Reintonstimulationen detektierbar sind. In einer zweiten Studie verbessern wir die Ergebnisse und die Methodik, indem wir einige der Einschr ankungen lockern, um den gradualen Prozess der Langzeit-Habituation in das abrupt changepoint Modell zu integrieren. Dazu wird ein bayes`sches Zustands-Raum-Modell vorgeschlagen. In den zuvor genannten Studien konnte erfolgreich mithilfe der Momentanphase zwischen verschiedenen Stimulationspegeln unterschieden werden. Im zweiten Teil der Arbeit wird der experimentelle Rahmen erweitert, um komplexere auditorische Reize wie in realen H orsituationen untersuchen zu k onnen. Dabei analysieren wir die neuronalen Korrelate der Aufmerksamkeit anhand spontaner Modulationen der kontinuierlichen EEG-Aktivit at, die eine zeitliche Au osung erm oglicht. Wir zeigen eine Abbildung zwischen den EKP-Ergebnissen und der kontinuierlichen EEG-Aktivit at auf Basis der Momentanphase. Ein Markov-basiertes Modell wird entwickelt, um st orende Variationen zu entfernen, die in kontinuierlichen EEG-Signalen auftreten k onnen. Wir glauben, dass die vorgeschlagene Methode als wichtiger Vorverarbeitungsschritt zur soliden objektiven Absch atzung des Aufmerksamkeitsgrades mithilfe von EEG-Daten verwendet werden kann. In einem dichotischen Tonerkennungsexperiment wird das vorgeschlagene Modell zur Vorverarbeitung der EEG-Daten und zur Klassi zierung zwischen gerichteten und ungerichteten Aufmerksamkeitszust anden erfolgreich verwendet. Im letzten Teil dieser Arbeit untersuchen wir den Zusammenhang zwischen den neuronalen Aktivit aten der kortikalen Laminae und auditorisch evozierten Potentialen (AEP) in vitro im Tiermodell. Wir zeigen eine starke Korrelation zwischen der Momentanphase der AEPs und den neuronalen Aktivit aten in der Granularschicht unter Verwendung der Transinformation

On the interpretation of synchronization in EEG hyperscanning studies:a cautionary note

EEG Hyperscanning is a method for studying two or more individuals simultaneously with the objective of elucidating how co-variations in their neural activity (i.e., hyperconnectivity) are influenced by their behavioral and social interactions. The aim of this study was to compare the performance of different hyper-connectivity measures using (i) simulated data, where the degree of coupling could be systematically manipulated, and (ii) individually recorded human EEG combined into pseudo-pairs of participants where no hyper-connections could exist. With simulated data we found that each of the most widely used measures of hyperconnectivity were biased and detected hyper-connections where none existed. With pseudo-pairs of human data we found spurious hyper-connections that arose because there were genuine similarities between the EEG recorded from different people independently but under the same experimental conditions. Specifically, there were systematic differences between experimental conditions in terms of the rhythmicity of the EEG that were common across participants. As any imbalance between experimental conditions in terms of stimulus presentation or movement may affect the rhythmicity of the EEG, this problem could apply in many hyperscanning contexts. Furthermore, as these spurious hyper-connections reflected real similarities between the EEGs, they were not Type-1 errors that could be overcome by some appropriate statistical control. However, some measures that have not previously been used in hyperconnectivity studies, notably the circular correlation co-efficient (CCorr), were less susceptible to detecting spurious hyper-connections of this type. The reason for this advantage in performance is discussed and the use of the CCorr as an alternative measure of hyperconnectivity is advocated. © 2013 Burgess

Inter-brain synchronization occurs without physical co-presence during cooperative online gaming

Inter-brain synchronization during social interaction has been linked with several positive phenomena, including closeness, cooperation, prosociality, and team performance. However, the temporal dynamics of inter-brain synchronization during collaboration are not yet fully understood. Furthermore, with collaboration increasingly happening online, the dependence of inter-brain phase synchronization of oscillatory activity on physical presence is an important but understudied question. In this study, physically isolated participants performed a collaborative coordination task in the form of a cooperative multiplayer game. We measured EEG from 42 subjects working together as pairs in the task. During the measurement, the only interaction between the participants happened through on-screen movement of a racing car, controlled by button presses of both participants working with distinct roles, either controlling the speed or the direction of the car. Pairs working together in the task were found to have elevated neural coupling in the alpha, beta, and gamma frequency bands, compared to performance matched false pairs. Higher gamma synchrony was associated with better momentary performance within dyads and higher alpha synchrony was associated with better mean performance across dyads. These results are in line with previous findings of increased inter-brain synchrony during interaction, and show that phase synchronization of oscillatory activity occurs during online real-time joint coordination without any physical co-presence or video and audio connection. Synchrony decreased during a playing session, but was found to be higher during the second session compared to the first. The novel paradigm, developed for the measurement of real-time collaborative performance, demonstrates that changes in inter-brain EEG phase synchrony can be observed continuously during interaction.Peer reviewe