Model-based estimation of intra-cortical connectivity using electrophysiological data.

This paper provides a new method for model-based estimation of intra-cortical connectivity from electrophysiological measurements. A novel closed-form solution for the connectivity function of the Amari neural field equations is derived as a function of electrophysiological observations. The resultant intra-cortical connectivity estimate is driven from experimental data, but constrained by the mesoscopic neurodynamics that are encoded in the computational model. A demonstration is provided to show how the method can be used to image physiological mechanisms that govern cortical dynamics, which are normally hidden in clinical data from epilepsy patients. Accurate estimation performance is demonstrated using synthetic data. Following the computational testing, results from patient data are obtained that indicate a dominant increase in surround inhibition prior to seizure onset that subsides in the cases when the seizures spread.

Spatiotemporal multi-resolution approximation of the Amari type neural field model.

Neural fields are spatially continuous state variables described by integro-differential equations, which are well suited to describe the spatiotemporal evolution of cortical activations on multiple scales. Here we develop a multi-resolution approximation (MRA) framework for the integro-difference equation (IDE) neural field model based on semi-orthogonal cardinal B-spline wavelets. In this way, a flexible framework is created, whereby both macroscopic and microscopic behavior of the system can be represented simultaneously. State and parameter estimation is performed using the expectation maximization (EM) algorithm. A synthetic example is provided to demonstrate the framework.

A data-driven framework for neural field modeling.

This paper presents a framework for creating neural field models from electrophysiological data. The Wilson and Cowan or Amari style neural field equations are used to form a parametric model, where the parameters are estimated from data. To illustrate the estimation framework, data is generated using the neural field equations incorporating modeled sensors enabling a comparison between the estimated and true parameters. To facilitate state and parameter estimation, we introduce a method to reduce the continuum neural field model using a basis function decomposition to form a finite-dimensional state-space model. Spatial frequency analysis methods are introduced that systematically specify the basis function configuration required to capture the dominant characteristics of the neural field. The estimation procedure consists of a two-stage iterative algorithm incorporating the unscented Rauch-Tung-Striebel smoother for state estimation and a least squares algorithm for parameter estimation. The results show that it is theoretically possible to reconstruct the neural field and estimate intracortical connectivity structure and synaptic dynamics with the proposed framework.

Placement of deep brain electrodes in the dog using the Brainsight frameless stereotactic system: a pilot feasibility study.

BACKGROUND: Deep brain stimulation (DBS) together with concurrent EEG recording has shown promise in the treatment of epilepsy. A novel device is capable of combining these 2 functions and may prove valuable in the treatment of epilepsy in dogs. However, stereotactic implantation of electrodes in dogs has not yet been evaluated. OBJECTIVE: To evaluate the feasibility and safety of implanting stimulating and recording electrodes in the brain of normal dogs using the Brainsight system and to evaluate the function of a novel DBS and recording device. ANIMALS: Four male intact Greyhounds, confirmed to be normal by clinical and neurologic examinations and hematology and biochemistry testing. METHODS: MRI imaging of the brain was performed after attachment of fiducial markers. MRI scans were used to calculate trajectories for electrode placement in the thalamus and hippocampus, which was performed via burr hole craniotomy. Postoperative CT scanning was performed to evaluate electrode location and accuracy of placement was calculated. Serial neurologic examinations were performed to evaluate neurologic deficits and EEG recordings obtained to evaluate the effects of stimulation. RESULTS: Electrodes were successfully placed in 3 of 4 dogs with a mean accuracy of 4.6 ± 1.5 mm. EEG recordings showed evoked potentials in response to stimulation with a circadian variation in time-to-maximal amplitude. No neurologic deficits were seen in any dog. CONCLUSIONS AND CLINICAL IMPORTANCE: Stereotactic placement of electrodes is safe and feasible in the dog. The development of a novel device capable of providing simultaneous neurostimulation and EEG recording potentially represents a major advance in the treatment of epilepsy.

Epilepsy: Ever-changing states of cortical excitability.

It has been proposed that the underlying epileptic process is mediated by changes in both excitatory and inhibitory circuits leading to the formation of hyper-excitable seizure networks. In this review we aim to shed light on the many physiological factors that modulate excitability within these networks. These factors have been discussed extensively in many reviews each as a separate entity and cannot be extensively covered in a single manuscript. Thus for the purpose of this work in which we aim to bring those factors together to explain how they interact with epilepsy, we only provide brief descriptions. We present reported evidence supporting the existence of the epileptic brain in several states; interictal, peri-ictal and ictal, each with distinct excitability features. We then provide an overview of how many physiological factors influence the excitatory/inhibitory balance within the interictal state, where the networks are presumed to be functioning normally. We conclude that these changes result in constantly changing states of cortical excitability in patients with epilepsy.