Sensitivity-analysis-guided Bayesian parameter estimation for neural mass models: Applications in epilepsy.

It is well established that neural mass models (NMMs) can effectively simulate the mesoscopic and macroscopic dynamics of electroencephalography (EEG), including epileptic EEG. However, the use of NMMs to gain insight on the neuronal system by parameter estimation is hampered by their high dimensionality and the lack of knowledge on what NMM parameters can be reliably estimated. In this article, we analyze the parameter sensitivity of the Jansen and Rit NMM (JR NMM) in order to identify the most sensitive JR-NMM parameters for reliable parameter estimation from EEG data.

Standardized Kalman filtering for dynamical source localization of concurrent subcortical and cortical brain activity.

Objective: We introduce standardized Kalman filtering (SKF) as a new spatiotemporal method for tracking brain activity. Via the Kalman filtering scheme, the computational workload is low, and by spatiotemporal standardization, we reduce the depth bias of non-standardized Kalman filtering (KF).

Methods: We describe the standardized KF methodology for spatiotemporal tracking from the Bayesian perspective.

Astrocytes facilitate gabazine-evoked electrophysiological hyperactivity and distinct biochemical responses in mature neuronal cultures.

Gamma-aminobutyric acid (GABA) is the principal inhibitory neurotransmitter in the adult brain that binds to GABA receptors and hyperpolarizes the postsynaptic neuron. Gabazine acts as a competitive antagonist to type A GABA receptors (GABAR), thereby causing diminished neuronal hyperpolarization and GABAR-mediated inhibition. However, the biochemical effects and the potential regulatory role of astrocytes in this process remain poorly understood.

Waveform complexity: A new metric for EEG analysis.

Background: EEG represents a cost-effective mechanism to evaluate brain function. To realize its potential, it is essential to identify aspects of the signal that provide insight into differences in cognitive, emotional and behavioral outcomes and can therefore aid in diagnostic measurement. Here we define a new metric of the EEG signal that assesses the diversity of waveform shapes in the signal.

Non-Linear Dynamical Analysis of Resting Tremor for Demand-Driven Deep Brain Stimulation.

Parkinson's Disease (PD) is currently the second most common neurodegenerative disease. One of the most characteristic symptoms of PD is resting tremor. Local Field Potentials (LFPs) have been widely studied to investigate deviations from the typical patterns of healthy brain activity.

Tracking of dynamic functional connectivity from MEG data with Kalman filtering.

Owing to their millisecond-scale temporal resolution, magnetoencephalography (MEG) and electroencephalography (EEG) are well-suited tools to study dynamic functional connectivity between regions in the human brain. However, current techniques to estimate functional connectivity from MEG/EEG are based on a two-step approach; first, the MEG/EEG inverse problem is solved to estimate the source activity, and second, connectivity is estimated between the sources. In this work, we propose a method for simultaneous estimation of source activities and their dynamic functional connectivity using a Kalman filter.

Electroencephalographic signals during anesthesia recorded from surface and depth electrodes.

Purpose: Anesthesiologists have increasingly started to use EEG-based indexes to estimate the level and type of unconsciousness. However, the physiology and biophysics are poorly understood in anesthesiological literature.

Methods: EEG was recorded from electrodes on the surface of head, including scalp, as well as DBS (deep brain stimulation) electrodes implanted deep in the brain.

Recurrence network analysis of multiple local field potential bands from the orofacial portion of primary motor cortex.

Local field potentials (LFPs), which have been considered as aggregate signals that reflect activities of a large number of neurons in the cerebral cortex, have been observed to mediate gross functional activities of a relatively small volume of the brain tissues. Historically there have been several frequency bands observed and defined across various brain areas. However, detailed analysis, either spectral analysis or any dynamical analysis of LFPs particularly in the orofacial part of the primary motor cortex (MIo) has not been done before.

Dynamics of intracranial electroencephalographic recordings from epilepsy patients using univariate and bivariate recurrence networks.

Recently Andrezejak et al. combined the randomness and nonlinear independence test with iterative amplitude adjusted Fourier transform (iAAFT) surrogates to distinguish between the dynamics of seizure-free intracranial electroencephalographic (EEG) signals recorded from epileptogenic (focal) and nonepileptogenic (nonfocal) brain areas of epileptic patients. However, stationarity is a part of the null hypothesis for iAAFT surrogates and thus nonstationarity can violate the null hypothesis.

Cortical potential imaging using L-curve and GCV method to choose the regularisation parameter.

Background: The electroencephalography (EEG) is an attractive and a simple technique to measure the brain activity. It is attractive due its excellent temporal resolution and simple due to its non-invasiveness and sensor design. However, the spatial resolution of EEG is reduced due to the low conducting skull.