Oscillations and neuronal synchronization in epilepsy: an approach based on oscillation theory and statistical mechanics.

openIn questo lavoro si propone di studiare i processi di sincronizzazione neuronale dal punto di vista dei sistemi dinamici, in particolare, della teoria delle oscillazioni. Si può dimostrare che esistono oscillazioni macroscopiche nel sistema talamocorticale dei topi epilettici. Questo fatto permette di modellare gli attacchi epilettici come processi di sincronizzazione di uno o due oscillatori auto-sostenuti, i cui parametri vengono ricavati dalle funzioni di risposta di fase ottenute sperimentalmente. Si osservano anche le cosiddette lingue di Arnold e i plateau di sincronizzazione, caratteristici della risposta di fase dei processi con un ciclo limite. Inoltre, utilizzando metodi della fisica statistica e la teoria di informazione, si ricava un rapporto fra la sincronizzazione e la quantità di informazione contenuta nelle rette funzionali del cervello. Si osserva che questa quantità di informazione è massima a livelli intermedi di sincronizzazione, in stati normali di veglia, e molto più bassa durante gli attacchi epiletticiIn this work we propose to study the neuronal synchronization processes from the point of view of the dynamical systems, in particular of the oscillations theory. It can be demonstrated that there are macroscopic oscillations in the thalamocortical network in epileptic rats. So we are able to model the epileptic seizures as synchronization processes of one or two self-sustained oscillator, whose parameters are extracted from the phase response functions obtained experimentally. We observe also the Arnold tongues and the synchronization plateau that are typical pf the phase response processes with a limit cycle. Moreover, using statistical physics and information theory methods, we obtain a relation between synchronization and quantity of information contained in the brain functional lines. This quantity of information has a peak at intermediate synchronization levels, as in conscious awareness states, and it is lower during epileptic seizures

Investigations of effective connectivity in small and large scale neural networks

The correct signal processing of neuronal signals requires coordination of different groups of neurons. To achieve this there has to be a connection between those neurons. This connection and especially the strength of the connection is not known a priori and can only be measured directly in rare cases. In this thesis I present three publications (Rosjat et al., 2014; Tóth et al., 2015; Popovych et al., under review) and the results from two additional studies focussing on the analysis of couplings in experimental measured neuronal activities. The publications can be divided into investigations of intrinsic, as well as extrinsic intra- and intersegmental connections in the stick insect Carausius morosus and into analysis and mathematical modeling of couplings from EEG-measurements of the human brain while subjects were performing different tasks. In both parts I made use of mathematical models to build hypotheses about so far unknown coupling mechanisms. The first study deals with connectivity changes in the thalamo-cortical loop caused by schizophrenia (Rosjat et al., 2014). To build a mathematical model consisting of neural populations representing the thalamus and the auditory cortex we made use of published EEG-data, which were collected while subjects performed a double-click paradigm. The individual populations comprised a large number of phase oscillators with continuously distributed natural frequencies. Applying reduction methods by Pikovsky and Rosenblum, Ott and Antonsen together with the reduction method by Watanabe and Strogatz we investigated the influences of the bidirectional connections between the brain areas on the synchronization of the neuronal populations. The model was able to replicate the experimental data adequately. We observed that the coupling strength from the thalamic region to the cortical region mainly affected the duration of synchrony while the feedback to the thalamic region had a bigger effect on the strength of synchrony. This led to the hypothesis that the back coupling to the thalamic region might be reduced in schizophrenia patients. The second study will show an analysis of intersegmental couplings in the protractorretractor system of the pro- and mesothoracic ganglion of the stick insect Carausius morosus using mathematical models based on experimental data (Tóth et al., 2015). We made use of phase-response curves that were calculated experimentally on the one hand and simulated by mathematical models on the other hand to determine the nature and the strength of their connection. We showed that connections on both sides from the prothoracic to the mesothoracic network were necessary to achieve a good agreement with the experimental phase-response curves. Additionally, it was found that the strength of the excitatory connection played a key role, while the strength of the inhibitory connection did not have a big influence on the shape of the phase-response curves. The third study deals with the identification of a neuronal marker of movement execution (Popovych et al., under review). In this work we investigated the influence of internally and externally triggered movement on the phase synchronization in the motor system. We tested the signals, that were recorded from electrodes lying above the motor cortex, in the phase space including the major frequency bands (delta-, theta-, alpha-, beta- and low gamma-frequencies) for inter-trial phase synchrony. The study revealed a strong lateralized phase synchronization in the lower frequency bands (delta and theta) in the electrodes above the contralateral primary motor cortex independent of the hand performing and the cue triggering the movement. The results suggest that this phase synchronization could serve as an electrophysiological marker of movement execution additionally to the well established event-related desynchronization and event-related synchronization that are based on the amplitude changes in alpha- and beta- frequency bands

The (un)conscious mouse as a model for human brain functions: key principles of anesthesia and their impact on translational neuroimaging

In recent years, technical and procedural advances have brought functional magnetic resonance imaging (fMRI) to the field of murine neuroscience. Due to its unique capacity to measure functional activity non-invasively, across the entire brain, fMRI allows for the direct comparison of large-scale murine and human brain functions. This opens an avenue for bidirectional translational strategies to address fundamental questions ranging from neurological disorders to the nature of consciousness. The key challenges of murine fMRI are: (1) to generate and maintain functional brain states that approximate those of calm and relaxed human volunteers, while (2) preserving neurovascular coupling and physiological baseline conditions. Low-dose anesthetic protocols are commonly applied in murine functional brain studies to prevent stress and facilitate a calm and relaxed condition among animals. Yet, current mono-anesthesia has been shown to impair neural transmission and hemodynamic integrity. By linking the current state of murine electrophysiology, Ca(2+) imaging and fMRI of anesthetic effects to findings from human studies, this systematic review proposes general principles to design, apply and monitor anesthetic protocols in a more sophisticated way. The further development of balanced multimodal anesthesia, combining two or more drugs with complementary modes of action helps to shape and maintain specific brain states and relevant aspects of murine physiology. Functional connectivity and its dynamic repertoire as assessed by fMRI can be used to make inferences about cortical states and provide additional information about whole-brain functional dynamics. Based on this, a simple and comprehensive functional neurosignature pattern can be determined for use in defining brain states and anesthetic depth in rest and in response to stimuli. Such a signature can be evaluated and shared between labs to indicate the brain state of a mouse during experiments, an important step toward translating findings across species

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Mathematical frameworks for oscillatory network dynamics in neuroscience

The tools of weakly coupled phase oscillator theory have had a profound impact on the neuroscience community, providing insight into a variety of network behaviours ranging from central pattern generation to synchronisation, as well as predicting novel network states such as chimeras. However, there are many instances where this theory is expected to break down, say in the presence of strong coupling, or must be carefully interpreted, as in the presence of stochastic forcing. There are also surprises in the dynamical complexity of the attractors that can robustly appear—for example, heteroclinic network attractors. In this review we present a set of mathemat- ical tools that are suitable for addressing the dynamics of oscillatory neural networks, broadening from a standard phase oscillator perspective to provide a practical frame- work for further successful applications of mathematics to understanding network dynamics in neuroscience

Nonlinear brain dynamics as macroscopic manifestation of underlying many-body field dynamics

Neural activity patterns related to behavior occur at many scales in time and space from the atomic and molecular to the whole brain. Here we explore the feasibility of interpreting neurophysiological data in the context of many-body physics by using tools that physicists have devised to analyze comparable hierarchies in other fields of science. We focus on a mesoscopic level that offers a multi-step pathway between the microscopic functions of neurons and the macroscopic functions of brain systems revealed by hemodynamic imaging. We use electroencephalographic (EEG) records collected from high-density electrode arrays fixed on the epidural surfaces of primary sensory and limbic areas in rabbits and cats trained to discriminate conditioned stimuli (CS) in the various modalities. High temporal resolution of EEG signals with the Hilbert transform gives evidence for diverse intermittent spatial patterns of amplitude (AM) and phase modulations (PM) of carrier waves that repeatedly re-synchronize in the beta and gamma ranges at near zero time lags over long distances. The dominant mechanism for neural interactions by axodendritic synaptic transmission should impose distance-dependent delays on the EEG oscillations owing to finite propagation velocities. It does not. EEGs instead show evidence for anomalous dispersion: the existence in neural populations of a low velocity range of information and energy transfers, and a high velocity range of the spread of phase transitions. This distinction labels the phenomenon but does not explain it. In this report we explore the analysis of these phenomena using concepts of energy dissipation, the maintenance by cortex of multiple ground states corresponding to AM patterns, and the exclusive selection by spontaneous breakdown of symmetry (SBS) of single states in sequences.Comment: 31 page

A model for cerebral cortical neuron group electric activity and its implications for cerebral function

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 245-265).The electroencephalogram, or EEG, is a recording of the field potential generated by the electric activity of neuronal populations of the brain. Its utility has long been recognized as a monitor which reflects the vigilance states of the brain, such as arousal, drowsiness, and sleep stages. Moreover, it is used to detect pathological conditions such as seizures, to calibrate drug action during anesthesia, and to understand cognitive task signatures in healthy and abnormal subjects. Being an aggregate measure of neural activity, understanding the neural origins of EEG oscillations has been limited. With the advent of recording techniques, however, and as an influx of experimental evidence on cellular and network properties of the neocortex has become available, a closer look into the neuronal mechanisms for EEG generation is warranted. Accordingly, we introduce an effective neuronal skeleton circuit at a neuronal group level which could reproduce basic EEG-observable slow ( 3mm). The effective circuit makes use of the dynamic properties of the layer 5 network to explain intra-cortically generated augmenting responses, restful alpha, slow wave (< 1Hz) oscillations, and disinhibition-induced seizures. Based on recent cellular evidence, we propose a hierarchical binding mechanism in tufted layer 5 cells which acts as a controlled gate between local cortical activity and inputs arriving from distant cortical areas. This gate is manifested by the switch in output firing patterns in tufted(cont.) layer 5 cells between burst firing and regular spiking, with specific implications on local functional connectivity. This hypothesized mechanism provides an explanation of different alpha band (10Hz) oscillations observed recently under cognitive states. In particular, evoked alpha rhythms, which occur transiently after an input stimulus, could account for initial reogranization of local neural activity based on (mis)match between driving inputs and modulatory feedback of higher order cortical structures, or internal expectations. Emitted alpha rhythms, on the other hand, is an example of extreme attention where dominance of higher order control inputs could drive reorganization of local cortical activity. Finally, the model makes predictions on the role of burst firing patterns in tufted layer 5 cells in redefining local cortical dynamics, based on internal representations, as a prelude to high frequency oscillations observed in various sensory systems during cognition.by Fadi Nabih Karameh.Ph.D