712 research outputs found
Spike voltage topography in temporal lobe epilepsy
We investigated the voltage topography of interictal spikes in patients with temporal lobe epilepsy (TLE) to see whether topography was related to etiology for TLE. Adults with TLE, who had epilepsy surgery for drug-resistant seizures from 2011 until 2014 at Jefferson Comprehensive Epilepsy Center were selected. Two groups of patients were studied: patients with mesial temporal sclerosis (MTS) on MRI and those with other MRI findings. The voltage topography maps of the interictal spikes at the peak were created using BESA software. We classified the interictal spikes as polar, basal, lateral, or others. Thirty-four patients were studied, from which the characteristics of 340 spikes were investigated. The most common type of spike orientation was others (186 spikes; 54.7%), followed by lateral (146; 42.9%), polar (5; 1.5%), and basal (3; 0.9%). Characteristics of the voltage topography maps of the spikes between the two groups of patients were somewhat different. Five spikes in patients with MTS had polar orientation, but none of the spikes in patients with other MRI findings had polar orientation (odds ratio = 6.98, 95% confidence interval = 0.38 to 127.38; p = 0.07). Scalp topographic mapping of interictal spikes has the potential to offer different information than visual inspection alone. The present results do not allow an immediate clinical application of our findings; however, detecting a polar spike in a patient with TLE may increase the possibility of mesial temporal sclerosis as the underlying etiology
Inverse Modeling for MEG/EEG data
We provide an overview of the state-of-the-art for mathematical methods that
are used to reconstruct brain activity from neurophysiological data. After a
brief introduction on the mathematics of the forward problem, we discuss
standard and recently proposed regularization methods, as well as Monte Carlo
techniques for Bayesian inference. We classify the inverse methods based on the
underlying source model, and discuss advantages and disadvantages. Finally we
describe an application to the pre-surgical evaluation of epileptic patients.Comment: 15 pages, 1 figur
Fully Complex Magnetoencephalography
Complex numbers appear naturally in biology whenever a system can be analyzed
in the frequency domain, such as physiological data from magnetoencephalography
(MEG). For example, the MEG steady state response to a modulated auditory
stimulus generates a complex magnetic field for each MEG channel, equal to the
Fourier transform at the stimulus modulation frequency. The complex nature of
these data sets, often not taken advantage of, is fully exploited here with new
methods. Whole-head, complex magnetic data can be used to estimate complex
neural current sources, and standard methods of source estimation naturally
generalize for complex sources. We show that a general complex neural vector
source is described by its location, magnitude, and direction, but also by a
phase and by an additional perpendicular component. We give natural
interpretations of all the parameters for the complex equivalent-current dipole
by linking them to the underlying neurophysiology. We demonstrate complex
magnetic fields, and their equivalent fully complex current sources, with both
simulations and experimental data.Comment: 23 pages, 1 table, 5 figures; to appear in Journal of Neuroscience
Method
Focal limbic sources create the large slow oscillations of the EEG in human deep sleep
Background: Initial observations with the human electroencephalogram (EEG) have interpreted slow oscillations (SOs) of the EEG during deep sleep (N3) as reflecting widespread surface-negative traveling waves that originate in frontal regions and propagate across the neocortex. However, mapping SOs with a high-density array shows the simultaneous appearance of posterior positive voltage fields in the EEG at the time of the frontal-negative fields, with the typical inversion point (apparent source) around the temporal lobe. Methods: Overnight 256-channel EEG recordings were gathered from 10 healthy young adults. Individual head conductivity models were created using each participant's own structural MRI. Source localization of SOs during N3 was then performed. Results: Electrical source localization models confirmed that these large waves were created by focal discharges within the ventral limbic cortex, including medial temporal and caudal orbitofrontal cortex. Conclusions: Although the functional neurophysiology of deep sleep involves interactions between limbic and neocortical networks, the large EEG deflections of deep sleep are not created by distributed traveling waves in lateral neocortex but instead by relatively focal limbic discharges.Fil: Morgan, Kyle K.. Brain Electrophysiology Laboratory Company; Estados UnidosFil: Hathaway, Evan. Brain Electrophysiology Laboratory Company; Estados UnidosFil: Carson, Megan. Brain Electrophysiology Laboratory Company; Estados UnidosFil: Fernandez Corazza, Mariano. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - La Plata. Instituto de Investigaciones en ElectrĂłnica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en ElectrĂłnica, Control y Procesamiento de Señales; ArgentinaFil: Shusterman, Roma. Brain Electrophysiology Laboratory Company; Estados UnidosFil: Luu, Phan. Brain Electrophysiology Laboratory Company; Estados Unidos. University of Oregon; Estados UnidosFil: Tucker, Don M.. University of Oregon; Estados Unidos. Brain Electrophysiology Laboratory Company; Estados Unido
Covariance-domain Dictionary Learning for Overcomplete EEG Source Identification
We propose an algorithm targeting the identification of more sources than
channels for electroencephalography (EEG). Our overcomplete source
identification algorithm, Cov-DL, leverages dictionary learning methods applied
in the covariance-domain. Assuming that EEG sources are uncorrelated within
moving time-windows and the scalp mixing is linear, the forward problem can be
transferred to the covariance domain which has higher dimensionality than the
original EEG channel domain. This allows for learning the overcomplete mixing
matrix that generates the scalp EEG even when there may be more sources than
sensors active at any time segment, i.e. when there are non-sparse sources.
This is contrary to straight-forward dictionary learning methods that are based
on the assumption of sparsity, which is not a satisfied condition in the case
of low-density EEG systems. We present two different learning strategies for
Cov-DL, determined by the size of the target mixing matrix. We demonstrate that
Cov-DL outperforms existing overcomplete ICA algorithms under various scenarios
of EEG simulations and real EEG experiments
Exploring the temporal dynamics of speech production with EEG and group ICA
Speech production is a complex skill whose neural implementation relies on a large number of different regions in the brain. How neural activity in these different regions varies as a function of time during the production of speech remains poorly understood. Previous MEG studies on this topic have concluded that activity proceeds from posterior to anterior regions of the brain in a sequential manner. Here we tested this claim using the EEG technique. Specifically, participants performed a picture naming task while their naming latencies and scalp potentials were recorded. We performed group temporal Independent Component Analysis (group tICA) to obtain temporally independent component timecourses and their corresponding topographic maps. We identified fifteen components whose estimated neural sources were located in various areas of the brain. The trial-by-trial component timecourses were predictive of the naming latency, implying their involvement in the task. Crucially, we computed the degree of concurrent activity of each component timecourse to test whether activity was sequential or parallel. Our results revealed that these fifteen distinct neural sources exhibit largely concurrent activity during speech production. These results suggest that speech production relies on neural activity that takes place in parallel networks of distributed neural sources
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