Computational Characterization of the Cellular Origins of Electroencephalography

Electroencephalography (EEG) is a non-invasive technique used to measure brain activity. Despite its near ubiquitous presence in neuroscience, very little research has gone into connecting the electrical potentials it measures on the scalp to the underlying network activity which generates those signals. This results in most EEG analyses being more macroscopically focused (e.g. coherence and correlation analyses). Despite the many uses of macroscopically focuses analyses, limiting research to only these analyses neglects the insights which can be gained from studying network and microcircuit architecture. The ability to study these things through non-invasive techniques like EEG depends upon the ability to understand how the activity of individual neurons affect the electrical potentials recorded by EEG electrodes on the scalp. The research presented here is designed to take the first steps towards providing that link.Current dipole moments generated by multiple multi-compartment, morphologically accurate, three-dimensional neuron models were characterized into a single time series called a dipole response function (DRF). We found that when the soma of a neuron is directly stimulated to threshold, the resulting action potential caused an excess of current which backpropagated up the dendritic tree activating voltage gated ion channels along the way. This backpropigation created a dipole which had a magnitude and duration greater than the current dipoles created by neurons that were synaptically activated to near threshold. Additionally, we presented a novel technique, where, through the combination of the DRFs with point source network activity via convolution, dipoles generated by populations of neurons can be simulated. We validated this technique at multiple spatial scales using data from both animal models and human subjects. Our results show that this technique can provide a reasonable representation of the extracellular fields and EEG signals generated in their physiological counterparts. Finally, analysis of a simulated evoked potential generated via the convolutional methodology proposed showed that ∼ 98% of the variability of simulated signal could be accounted for by the dipoles originating from DRFs of spiking pyramidal cells

A Method for Neuronal Source Identification

Multi-sensor microelectrodes for extracellular action potential recording have significantly improved the quality of in vivo recorded neuronal signals. These microelectrodes have also been instrumental in the localization of neuronal signal sources. However, existing neuron localization methods have been mostly utilized in vivo, where the true neuron location remains unknown. Therefore, these methods could not be experimentally validated. This article presents experimental validation of a method capable of estimating both the location and intensity of an electrical signal source. A four-sensor microelectrode (tetrode) immersed in a saline solution was used to record stimulus patterns at multiple intensity levels generated by a stimulating electrode. The location of the tetrode was varied with respect to the stimulator. The location and intensity of the stimulator were estimated using the Multiple Signal Classification (MUSIC) algorithm, and the results were quantified by comparison to the true values. The localization results, with an accuracy and precision of ~ 10 microns, and ~ 11 microns respectively, imply that MUSIC can resolve individual neuronal sources. Similarly, source intensity estimations indicate that this approach can track changes in signal amplitude over time. Together, these results suggest that MUSIC can be used to characterize neuronal signal sources in vivo.Comment: 14 pages, 5 figure

Making Waves in the Brain: What Are Oscillations, and Why Modulating Them Makes Sense for Brain Injury.

Traumatic brain injury (TBI) can result in persistent cognitive, behavioral and emotional deficits. However, the vast majority of patients are not chronically hospitalized; rather they have to manage their disabilities once they are discharged to home. Promoting recovery to pre-injury level is important from a patient care as well as a societal perspective. Electrical neuromodulation is one approach that has shown promise in alleviating symptoms associated with neurological disorders such as in Parkinson's disease (PD) and epilepsy. Consistent with this perspective, both animal and clinical studies have revealed that TBI alters physiological oscillatory rhythms. More recently several studies demonstrated that low frequency stimulation improves cognitive outcome in models of TBI. Specifically, stimulation of the septohippocampal circuit in the theta frequency entrained oscillations and improved spatial learning following TBI. In order to evaluate the potential of electrical deep brain stimulation for clinical translation we review the basic neurophysiology of oscillations, their role in cognition and how they are changed post-TBI. Furthermore, we highlight several factors for future pre-clinical and clinical studies to consider, with the hope that it will promote a hypothesis driven approach to subsequent experimental designs and ultimately successful translation to improve outcome in patients with TBI

EMG Modeling

The aim of this chapter is to describe the approaches used for modelling electromyographic (EMG) signals as well as the principles of electrical conduction within the muscle. Sections are organized into a progressive, step-by-step EMG modeling of structures of increasing complexity. First, the basis of the electrical conduction that allows for the propagation of the EMG signals within the muscle is presented. Second, the models used for describing the electrical activity generated by a single fibre described. The third section is devoted to modeling the organization of the motor unit and the generation of motor unit potentials. Based on models of the architectural organization of motor units and their activation and firing mechanisms, the last section focuses on modeling the electrical activity of a complete muscle as recorded at the surface

The mechanisms of tinnitus: perspectives from human functional neuroimaging

In this review, we highlight the contribution of advances in human neuroimaging to the current understanding of central mechanisms underpinning tinnitus and explain how interpretations of neuroimaging data have been guided by animal models. The primary motivation for studying the neural substrates of tinnitus in humans has been to demonstrate objectively its representation in the central auditory system and to develop a better understanding of its diverse pathophysiology and of the functional interplay between sensory, cognitive and affective systems. The ultimate goal of neuroimaging is to identify subtypes of tinnitus in order to better inform treatment strategies. The three neural mechanisms considered in this review may provide a basis for TI classification. While human neuroimaging evidence strongly implicates the central auditory system and emotional centres in TI, evidence for the precise contribution from the three mechanisms is unclear because the data are somewhat inconsistent. We consider a number of methodological issues limiting the field of human neuroimaging and recommend approaches to overcome potential inconsistency in results arising from poorly matched participants, lack of appropriate controls and low statistical power

Feasibility and resolution limits of opto-magnetic imaging of neural network activity in brain slices using color centers in diamond

We suggest a novel approach for wide-field imaging of the neural network dynamics of brain slices that uses highly sensitivity magnetometry based on nitrogen-vacancy (NV) centers in diamond. Invitro recordings in brain slices is a proven method for the characterization of electrical neural activity and has strongly contributed to our understanding of the mechanisms that govern neural information processing. However, this traditional approach only acquires signals from a few positions, which severely limits its ability to characterize the dynamics of the underlying neural networks. We suggest to extend its scope using NV magnetometry-based imaging of the neural magnetic fields across the slice. Employing comprehensive computational simulations and theoretical analyses, we determine the spatiotemporal characteristics of the neural fields and the required key performance parameters of an NV magnetometry-based imaging setup. We investigate how the technical parameters determine the achievable spatial resolution for an optimal 2D reconstruction of neural currents from the measured field distributions. Finally, we compare the imaging of neural slice activity with that of a single planar pyramidal cell. Our results suggest that imaging of slice activity will be possible with the upcoming generation of NV magnetic field sensors, while single-shot imaging of planar cell activity remains challenging

Extracellular electrophysiology with close-packed recording sites: spike sorting and characterization

Advances in recording technologies now allow us to record populations of neurons simultaneously, data necessary to understand the network dynamics of the brain. Extracellular probes are fabricated with ever greater numbers of recording sites to capture the activity of increasing numbers of neurons. However, the utility of this extracellular data is limited by an initial analysis step, spike sorting, that extracts the activity patterns of individual neurons from the extracellular traces. Commonly used spike sorting methods require manual processing that limits their scalability, and errors can bias downstream analyses. Leveraging the replication of the activity from a single neuron on nearby recording sites, we designed a spike sorting method consisting of three primary steps: (1) a blind source separation algorithm to estimate the underlying source components, (2) a spike detection algorithm to find the set of spikes from each component best separated from background activity and (3) a classifier to evaluate if a set of spikes came from one individual neuron. To assess the accuracy of our method, we simulated multi-electrode array data that encompass many of the realistic variations and the sources of noise in in vivo neural data. Our method was able to extract individual simulated neurons in an automated fashion without any errors in spike assignment. Further, the number of neurons extracted increased as we increased recording site count and density. To evaluate our method in vivo, we performed both extracellular recording with our close-packed probes and a co-localized patch clamp recording, directly measuring one neuron’s ground truth set of spikes. Using this in vivo data we found that when our spike sorting method extracted the patched neuron, the spike assignment error rates were at the low end of reported error rates, and that our errors were frequently the result of failed spike detection during bursts where spike amplitude decreased into the noise. We used our in vivo data to characterize the extracellular recordings of burst activity and more generally what an extracellular electrode records. With this knowledge, we updated our spike detector to capture more burst spikes and improved our classifier based on our characterizations