Network inference in a stochastic multi-population neural mass model via approximate Bayesian computation

In this article, we propose a 6N-dimensional stochastic differential equation (SDE), modelling the activity of N coupled populations of neurons in the brain. This equation extends the Jansen and Rit neural mass model, which has been introduced to describe human electroencephalography (EEG) rhythms, in particular signals with epileptic activity. Our contributions are threefold: First, we introduce this stochastic N-population model and construct a reliable and efficient numerical method for its simulation, extending a splitting procedure for one neural population. Second, we present a modified Sequential Monte Carlo Approximate Bayesian Computation (SMC-ABC) algorithm to infer both the continuous and the discrete model parameters, the latter describing the coupling directions within the network. The proposed algorithm further develops a previous reference-table acceptance rejection ABC method, initially proposed for the inference of one neural population. On the one hand, the considered SMC-ABC approach reduces the computational cost due to the basic acceptance-rejection scheme. On the other hand, it is designed to account for both marginal and coupled interacting dynamics, allowing to identify the directed connectivity structure. Third, we illustrate the derived algorithm on both simulated data and real multi-channel EEG data, aiming to infer the brain's connectivity structure during epileptic seizure. The proposed algorithm may be used for parameter and network estimation in other multi-dimensional coupled SDEs for which a suitable numerical simulation method can be derived.Comment: 28 pages, 11 figure

Differentiating Epileptic from Psychogenic Nonepileptic EEG Signals using Time Frequency and Information Theoretic Measures of Connectivity

Differentiating psychogenic nonepileptic seizures from epileptic seizures is a difficult task that requires timely recording of psychogenic events using video electroencephalography (EEG). Interpretation of video EEG to distinguish epileptic features from signal artifacts is error prone and can lead to misdiagnosis of psychogenic seizures as epileptic seizures resulting in undue stress and ineffective treatment with antiepileptic drugs. In this study, an automated surface EEG analysis was implemented to investigate differences between patients classified as having psychogenic or epileptic seizures. Surface EEG signals were grouped corresponding to the anatomical lobes of the brain (frontal, parietal, temporal, and occipital) and central coronal plane of the skull. To determine if differences were present between psychogenic and epileptic groups, magnitude squared coherence (MSC) and cross approximate entropy (C-ApEn) were used as measures of neural connectivity. MSC was computed within each neural frequency band (delta: 0.5Hz-4Hz, theta: 4-8Hz, alpha: 8-13Hz, beta: 13-30Hz, and gamma: 30-100Hz) between all brain regions. C-ApEn was computed bidirectionally between all brain regions. Independent samples t-tests were used to compare groups. The statistical analysis revealed significant differences between psychogenic and epileptic groups for both connectivity measures with the psychogenic group showing higher average connectivity. Average MSC was found to be lower for the epileptic group between the frontal/central, parietal/central, and temporal/occipital regions in the delta band and between the temporal/occipital regions in the theta band. Average C-ApEn was found to be greater for the epileptic group between the frontal/parietal, parietal/frontal, parietal/occipital, and parietal/central region pairs. These results suggest that differences in neural connectivity exist between psychogenic and epileptic patient groups

SCA with Magnitude Squared Coherence

International audienceMagnitude Squared Coherence is a signal processing tool that indicates how well two time domain signals match one with the other by tracking linear dependencies in their spectral decomposition. SCAN was the first way to use it as a SCA. This paper introduces two ways of using the Magnitude Squared Coherence in Side Channel Analysis. The first way is to use it as a distinguisher while the second consists in using it to transform the side channel traces in an worthwhile manner. Additionally, an algorithm for fast computation of the SCAN is provided