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Micro-electrocorticograph (μECoG) arrays offer the flexibility to record local field potentials (LFPs) from the surface of the cortex, using high density electrodes that are sub-mm in diameter. Research to date has not provided conclusive evidence for the underlying signal generation of μECoG recorded LFPs, or if μECoG arrays can capture network activity from the cortex. We studied the pervading view of the LFP signal by exploring the spatial scale at which the LFP can be considered elemental. We investigated the underlying signal generation and ability to capture functional networks by implanting, μECoG arrays to record sensory-evoked potentials in four rats. The organization of the sensory cortex was studied by analyzing the sensory-evoked potentials with two distinct modeling techniques: (1) The volume conduction model, that models the electrode LFPs with an electrostatic representation, generated by a single cortical generator, and (2) the dynamic causal model (DCM), that models the electrode LFPs with a network model, whose activity is generated by multiple interacting cortical sources. The volume conduction approach modeled activity from electrodes separated < 1000 μm, with reasonable accuracy but a network model like DCM was required to accurately capture activity > 1500 μm. The extrinsic network component in DCM was determined to be essential for accurate modeling of observed potentials. These results all point to the presence of a sensory network, and that μECoG arrays are able to capture network activity in the neocortex. The estimated DCM network models the functional organization of the cortex, as signal generators for the μECoG recorded LFPs, and provides hypothesis-testing tools to explore the brain., (Copyright © 2017 Elsevier Inc. All rights reserved.)

This article presents a new spatio-temporal method for M/EEG source reconstruction based on the assumption that only a small number of events, localized in space and/or time, are responsible for the measured signal. Each space-time event is represented using a basis function expansion which reflects the most relevant (or measurable) features of the signal. This model of neural activity leads naturally to a Bayesian likelihood function which balances the model fit to the data with the complexity of the model, where the complexity is related to the number of included events. A novel Expectation-Maximization algorithm which maximizes the likelihood function is presented. The new method is shown to be effective on several MEG simulations of neurological activity as well as data from a self-paced finger tapping experiment.

An approach is presented for representing spatially extended cortical activity using a basis function expansion. The bases are designed to represent patches on the cortical surface. The basis function expansion coefficients are estimated for each patch by scanning modified linearly constrained minimum variance (LCMV) spatial filters over the entire surface. Next, a generalized likelihood ratio test (GLRT) is performed to detect patches with significant activity. In the last step, an image of the activity within each patch is reconstructed using a minimum norm solution to a local inverse problem. We show that the basis function representation enables the LCMV approach to identify patches of coherent activity that are missed by the conventional LCMV method and has potential for extended source detection and localization.

A nerve stimulation model has been developed, incorporating realistic cross-sectional nerve geometries and conductivities. The potential field in the volume conductor was calculated numerically using the variational method. Nerve fiber excitation was described by the model of McNeal. Cross-sectional geometries of small monofascicular rat common peroneal nerve and multifascicular human deep peroneal nerve were taken as sample geometries. Selective stimulation of a fascicle was theoretically analyzed for several electrode positions: outside the nerve, in the connective tissue of the nerve, and inside a fascicle. The model results predict that the use of intraneural or even intrafascicular electrodes is necessary for selective stimulation of fascicles not lying at the surface of the nerve. Model predictions corresponded with experimental results of Veltink et al. on intrafascicular and extraneural stimulation of rat common peroneal nerve and to results of McNeal and Bowman on muscle selective stimulation in multifascicular dog sciatic nerve using an extraneural multielectrode configuration.