Discrete structure of the brain rhythms

Neuronal activity in the brain generates synchronous oscillations of the Local Field Potential (LFP). The traditional analyses of the LFPs are based on decomposing the signal into simpler components, such as sinusoidal harmonics. However, a common drawback of such methods is that the decomposition primitives are usually presumed from the onset, which may bias our understanding of the signal's structure. Here, we introduce an alternative approach that allows an impartial, high resolution, hands-off decomposition of the brain waves into a small number of discrete, frequency-modulated oscillatory processes, which we call oscillons. In particular, we demonstrate that mouse hippocampal LFP contain a single oscillon that occupies the $\theta$-frequency band and a couple of $\gamma$-oscillons that correspond, respectively, to slow and fast $\gamma$-waves. Since the oscillons were identified empirically, they may represent the actual, physical structure of synchronous oscillations in neuronal ensembles, whereas Fourier-defined "brain waves" are nothing but poorly resolved oscillons.Comment: 17 pages, 9 figure

Time scale synchronization of chaotic oscillators

This paper presents the result of the investigation of chaotic oscillator synchronization. A new approach for detecting of synchronized behaviour of chaotic oscillators has been proposed. This approach is based on the analysis of different time scales in the time series generated by the coupled chaotic oscillators. This approach has been applied for the coupled Rossler and Lorenz systems.Comment: 19 pages, 12 figure

Decrease in early right alpha band phase synchronization and late gamma band oscillations in processing syntax in music

The present study investigated the neural correlates associated with the processing of music-syntactical irregularities as compared with regular syntactic structures in music. Previous studies reported an early ( approximately 200 ms) right anterior negative component (ERAN) by traditional event-related-potential analysis during music-syntactical irregularities, yet little is known about the underlying oscillatory and synchronization properties of brain responses which are supposed to play a crucial role in general cognition including music perception. First we showed that the ERAN was primarily represented by low frequency (<8 Hz) brain oscillations. Further, we found that music-syntactical irregularities as compared with music-syntactical regularities, were associated with (i) an early decrease in the alpha band (9-10 Hz) phase synchronization between right fronto-central and left temporal brain regions, and (ii) a late ( approximately 500 ms) decrease in gamma band (38-50 Hz) oscillations over fronto-central brain regions. These results indicate a weaker degree of long-range integration when the musical expectancy is violated. In summary, our results reveal neural mechanisms of music-syntactic processing that operate at different levels of cortical integration, ranging from early decrease in long-range alpha phase synchronization to late local gamma oscillations

Analytical methods and experimental approaches for electrophysiological studies of brain oscillations

Brain oscillations are increasingly the subject of electrophysiological studies probing their role in the functioning and dysfunction of the human brain. In recent years this research area has seen rapid and significant changes in the experimental approaches and analysis methods. This article reviews these developments and provides a structured overview of experimental approaches, spectral analysis techniques and methods to establish relationships between brain oscillations and behaviour

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

Single-trial multiwavelet coherence in application to neurophysiological time series

A method of single-trial coherence analysis is presented, through the application of continuous muldwavelets. Multiwavelets allow the construction of spectra and bivariate statistics such as coherence within single trials. Spectral estimates are made consistent through optimal time-frequency localization and smoothing. The use of multiwavelets is considered along with an alternative single-trial method prevalent in the literature, with the focus being on statistical, interpretive and computational aspects. The multiwavelet approach is shown to possess many desirable properties, including optimal conditioning, statistical descriptions and computational efficiency. The methods. are then applied to bivariate surrogate and neurophysiological data for calibration and comparative study. Neurophysiological data were recorded intracellularly from two spinal motoneurones innervating the posterior,biceps muscle during fictive locomotion in the decerebrated cat

Cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus

Neuronal oscillations allow for temporal segmentation of neuronal spikes. Interdependent oscillators can integrate multiple layers of information. We examined phase–phase coupling of theta and gamma oscillators in the CA1 region of rat hippocampus during maze exploration and rapid eye movement sleep. Hippocampal theta waves were asymmetric, and estimation of the spatial position of the animal was improved by identifying the waveform-based phase of spiking, compared to traditional methods used for phase estimation. Using the waveform-based theta phase, three distinct gamma bands were identified: slow gammaS (gammaS; 30–50 Hz), midfrequency gammaM (gammaM; 50–90 Hz), and fast gammaF (gammaF; 90–150 Hz or epsilon band). The amplitude of each sub-band was modulated by the theta phase. In addition, we found reliable phase–phase coupling between theta and both gammaS and gammaM but not gammaF oscillators. We suggest that cross-frequency phase coupling can support multiple time-scale control of neuronal spikes within and across structures.Fil: Belluscio, Mariano Andres. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mizuseki, Kenji. Rutgers University; Estados UnidosFil: Schmidt, Robert. Rutgers University; Estados UnidosFil: Kempter, Richard. Rutgers University; Estados UnidosFil: Buzsáki, György. Rutgers University; Estados Unido

Nonlinear System Identification of Neural Systems from Neurophysiological Signals

The human nervous system is one of the most complicated systems in nature. Complex nonlinear behaviours have been shown from the single neuron level to the system level. For decades, linear connectivity analysis methods, such as correlation, coherence and Granger causality, have been extensively used to assess the neural connectivities and input-output interconnections in neural systems. Recent studies indicate that these linear methods can only capture a small amount of neural activities and functional relationships, and therefore cannot describe neural behaviours in a precise or complete way. In this review, we highlight recent advances in nonlinear system identification of neural systems, corresponding time and frequency domain analysis, and novel neural connectivity measures based on nonlinear system identification techniques. We argue that nonlinear modelling and analysis are necessary to study neuronal processing and signal transfer in neural systems quantitatively. These approaches can hopefully provide new insights to advance our understanding of neurophysiological mechanisms underlying neural functions. These nonlinear approaches also have the potential to produce sensitive biomarkers to facilitate the development of precision diagnostic tools for evaluating neurological disorders and the effects of targeted intervention