Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals

Dictionary Learning has proven to be a powerful tool for many image processing tasks, where atoms are typically defined on small image patches. As a drawback, the dictionary only encodes basic structures. In addition, this approach treats patches of different locations in one single set, which means a loss of information when features are well-aligned across signals. This is the case, for instance, in multi-trial magneto- or electroencephalography (M/EEG). Learning the dictionary on the entire signals could make use of the alignement and reveal higher-level features. In this case, however, small missalignements or phase variations of features would not be compensated for. In this paper, we propose an extension to the common dictionary learning framework to overcome these limitations by allowing atoms to adapt their position across signals. The method is validated on simulated and real neuroelectric data.Comment: 9 pages, 5 figures, minor correction

Identifying Neural Drivers with Functional MRI: An Electrophysiological Validation

Whether functional magnetic resonance imaging (fMRI) allows the identification of neural drivers remains an open question of particular importance to refine physiological and neuropsychological models of the brain, and/or to understand neurophysiopathology. Here, in a rat model of absence epilepsy showing spontaneous spike-and-wave discharges originating from the first somatosensory cortex (S1BF), we performed simultaneous electroencephalographic (EEG) and fMRI measurements, and subsequent intracerebral EEG (iEEG) recordings in regions strongly activated in fMRI (S1BF, thalamus, and striatum). fMRI connectivity was determined from fMRI time series directly and from hidden state variables using a measure of Granger causality and Dynamic Causal Modelling that relates synaptic activity to fMRI. fMRI connectivity was compared to directed functional coupling estimated from iEEG using asymmetry in generalised synchronisation metrics. The neural driver of spike-and-wave discharges was estimated in S1BF from iEEG, and from fMRI only when hemodynamic effects were explicitly removed. Functional connectivity analysis applied directly on fMRI signals failed because hemodynamics varied between regions, rendering temporal precedence irrelevant. This paper provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI. As such, it has important implications for future studies on brain connectivity using functional neuroimaging

GluN2A NMDA Receptor Enhancement Improves Brain Oscillations, Synchrony, and Cognitive Functions in Dravet Syndrome and Alzheimer's Disease Models.

NMDA receptors (NMDARs) play subunit-specific roles in synaptic function and are implicated in neuropsychiatric and neurodegenerative disorders. However, the in vivo consequences and therapeutic potential of pharmacologically enhancing NMDAR function via allosteric modulation are largely unknown. We examine the in vivo effects of GNE-0723, a positive allosteric modulator of GluN2A-subunit-containing NMDARs, on brain network and cognitive functions in mouse models of Dravet syndrome (DS) and Alzheimer's disease (AD). GNE-0723 use dependently potentiates synaptic NMDA receptor currents and reduces brain oscillation power with a predominant effect on low-frequency (12-20 Hz) oscillations. Interestingly, DS and AD mouse models display aberrant low-frequency oscillatory power that is tightly correlated with network hypersynchrony. GNE-0723 treatment reduces aberrant low-frequency oscillations and epileptiform discharges and improves cognitive functions in DS and AD mouse models. GluN2A-subunit-containing NMDAR enhancers may have therapeutic benefits in brain disorders with network hypersynchrony and cognitive impairments

Effets antiépileptiques de la neurostimulation asservie dans un modèle d'épilepsie chez le rat

About 20% of epileptic patients do not respond to pharmacological and surgical treatment. Another therapeutic option is the neurostimulation of neural circuits involved in the control or in the initiation of paroxysmal activity. Deep brain stimulation constitutes the main therapeutic alternative approach in some forms of pharmaco-resistant epilepsy which cannot benefit from resective surgery. The aim of this work was to develop a new system of adaptative deep brain stimulation which is effective for the long term suppression of seizures in a genetic rat model of absence epilepsy. Three main points were approached: (i) the characterisation of basal ganglia (BG) activities during seizures; (ii) the development of a new system based on deep brain stimulation combined to seizure-detection; (iii) the validation of long term closed loop deep brain stimulation. These data confirm the existence of a rapid propagation of spike-and-wave discharges (SWDs) from the cortex towards the thalamus and the BG, and an endogenous control of seizures by the substantia nigra pars reticulata (SNr) target used for seizures modulation. Despite a refractory period of 40 seconds during which the deep brain stimulation is ineffective, we show an antiepileptic effects of closed loop deep brain stimulation by the SNr. During long term closed loop SNr stimulation up to 97% of SWDs were interrupted accompanied by a decrease of SWDs with time. These results constitute a proof of concept for the use of closed loop device for the control of some forms of epileptic seizures.Malgré un traitement pharmacologique et chirurgical adapté, les crises d'épilepsie persistent chez environ 20% des patients. La neurostimulation des circuits générateurs et/ou de contrôle des crises constitue actuellement la principale approche thérapeutique non lésionnelle innovante dans certaines formes d'épilepsies pharmaco-résistantes qui ne peuvent bénéficier d'une chirurgie résective curative. L'objectif de ce travail a été de développer un nouveau système de stimulation intracérébrale profonde qui soit efficace pour la suppression des crises sur le long terme sur un modèle d'épilepsie absence chez le rat. Trois points principaux ont été abordés : (i) la caractérisation de l'activité des ganglions de la base (GB) pendant les crises; (ii) le développement d'un système de stimulation asservie sur le principe de "détection/stimulation" des crises; (iii) la quantification des effets antiépileptiques à long terme de la stimulation asservie. Les résultats obtenus ont permis de confirmer l'existence d'une propagation rapide des décharges de pointes ondes (DPO) du cortex vers le thalamus et les GB, et d'un contrôle endogène des crises par la substance noire réticulée (SNr), cible utilisée pour la modulation des crises. Malgré une période réfractaire de 40 s au cours de laquelle la stimulation est inefficace, les effets antiépileptiques de la stimulation asservie de la SNr se sont avérés probants : taux élevé d'interruption des DPO par la stimulation (97%) accompagné d'une diminution de la survenue des DPO au cours du temps. Ces résultats constituent une preuve de concept de l'utilisation de la neurostimulation stimulation asservie dans le traitement de certaines formes d'épilepsie

Adaptive Waveform Learning: A Framework for Modeling Variability in Neurophysiological Signals

International audienceWhen analyzing brain activity such as local field potentials (LFP), it is often desired to represent neural events by stereotypic waveforms. Due to the non-deterministic nature of the neural responses, an adequate waveform estimate typically requires to record multiple repetitions of the neural events. It is common practice to segment the recorded signal into event-related epochs and calculate their average. This approach suffers from two major drawbacks: (i) epoching can be problematic, especially in the case of overlapping neural events and (ii) variability of the neural events across epochs (such as varying onset latencies) is not accounted for, which may lead to a distorted average. In this paper, we propose a novel method called adaptive waveform learning (AWL). It is designed to learn multi-component representations of neural events while explicitly capturing and compensating for waveform variability, such as changing latencies or more general shape variations. Thanks to its generality, it can be applied to both epoched (i.e., segmented) and continuous (i.e., non-epoched) signals by making the corresponding specializations to the algorithm. We evaluate AWL's performance and robustness to noise on simulated data and demonstrate its empirical utility on an electrophysiological recording containing intracranial epileptiform discharges (epileptic spikes)

Electro-Metabolic Coupling Investigated with Jitter Invariant Dictionary Learning

International audienceThis work aims at establishing a relationship between neurophysiological and hemodynamic activity in an animal model of epilepsy. For the analysis, we propose a novel algorithm that is suited to learn meaningful representations of the multimodal datasets. As a result, we are able to learn a hemodynamic response and discover spike synchronization with hemodynamic activity

A differential evolution-based approach for fitting a nonlinear biophysical model to fMRI BOLD data

International audiencePhysiological and biophysical models have been proposed to link neuronal activity to the Blood Oxygen Level-Dependent (BOLD) signal in functional MRI (fMRI). Those models rely on a set of parameter values that cannot always be extracted from the literature. In some applications, interesting insight into the brain physiology or physiopathology can be gained from an estimation of the model parameters from measured BOLD signals. This estimation is challenging because there are more than 10 potentially interesting parameters involved in nonlinear equations and whose interactions may result in identifiability issues. However, the availability of statistical prior knowledge about these parameters can greatly simplify the estimation task. In this work we focus on the extended Balloon model and propose the estimation of 15 parameters using two stochastic approaches: an Evolutionary Computation global search method called Differential Evolution (DE) and a Markov Chain Monte Carlo version of DE. To combine both the ability to escape local optima and to incorporate prior knowledge, we derive the target function from Bayesian modeling. The general behavior of these algorithms is analyzed and compared with the de facto standard Expectation Maximization Gauss-Newton (EM/GN) approach, providing very promising results on challenging real and synthetic fMRI data sets involving rats with epileptic activity. These stochastic optimizers provided a better performance than EM/GN in terms of distance to the ground truth in 3 out of 6 synthetic data sets and a better signal fitting in 11 out of 12 real data sets. Non-parametric statistical tests showed the existence of statistically significant differences between the real data results obtained by DE and EM/GN. Finally, the estimates obtained from DE for these parameters seem both more realistic and more stable or at least as stable across sessions as the estimates from EM/GN

Estimating Biophysical Parameters from BOLD Signals through Evolutionary-Based Optimization

International audiencePhysiological and biophysical models have been proposed to link neural activity to the Blood Oxygen Level-Dependent (BOLD) signal in functional MRI (fMRI). They rely on a set of parameter values that cannot always be extracted from the literature. Their estimation is challenging because there are more than 10 potentially interesting parameters involved in non-linear equations and whose interactions may result in identifiability issues. However, the availability of statistical prior knowledge on these parameters can greatly simplify the estimation task. In this work we focus on the extended Balloon model and propose the estimation of 15 parameters using an Evolutionary Computation (EC) global search method. To combine both the ability to escape local optima and to incorporate prior knowledge, we derive the EC objective function from Bayesian modeling. This novel method provides promising results on a challenging real fMRI data set involving rats with epileptic activity and compares favorably with the conventional Expectation Maximization Gauss-Newton approach

A New Computational Model for Neuro-Glio-Vascular Coupling: Astrocyte Activation Can Explain Cerebral Blood Flow Nonlinear Response to Interictal Events

International audienceDeveloping a clear understanding of the relationship between cerebral blood flow (CBF) response and neuronal activity is of significant importance because CBF increase is essential to the health of neurons, for instance through oxygen supply. This relationship can be investigated by analyzing multimodal (fMRI, PET, laser Doppler.. .) recordings. However, the important number of intermediate (non-observable) variables involved in the underlying neurovascular coupling makes the discovery of mechanisms all the more difficult from the sole multimodal data. We present a new computational model developed at the population scale (voxel) with physiologically relevant but simple equations to facilitate the interpretation of regional multimodal recordings. This model links neuronal activity to regional CBF dynamics through neuro-glio-vascular coupling. This coupling involves a population of glial cells called astrocytes via their role in neurotransmitter (glutamate and GABA) recycling and their impact on neighboring vessels. In epilepsy, neuronal networks generate epilepti-form discharges, leading to variations in astrocytic and CBF dynamics. In this study, we took advantage of these large variations in neuronal activity magnitude to test the capacity of our model to reproduce experimental data. We compared simulations from our model with isolated epileptiform events, which were obtained in vivo by simultaneous local field potential and laser Doppler recordings in rats after local bicuculline injection. We showed a predominant neuronal contribution for low level discharges and a significant astrocytic contribution for higher level discharges. Besides, neuronal contribution to CBF was linear while astrocytic contribution was nonlinear. Results thus indicate that the relationship between neuronal activity and CBF magnitudes can be nonlinear for isolated events and that this nonlinearity is due to astrocytic activity, highlighting the importance of astrocytes in the interpretation of regional recordings