8,652 research outputs found
The Surface Laplacian Technique in EEG: Theory and Methods
This paper reviews the method of surface Laplacian differentiation to study
EEG. We focus on topics that are helpful for a clear understanding of the
underlying concepts and its efficient implementation, which is especially
important for EEG researchers unfamiliar with the technique. The popular
methods of finite difference and splines are reviewed in detail. The former has
the advantage of simplicity and low computational cost, but its estimates are
prone to a variety of errors due to discretization. The latter eliminates all
issues related to discretization and incorporates a regularization mechanism to
reduce spatial noise, but at the cost of increasing mathematical and
computational complexity. These and several others issues deserving further
development are highlighted, some of which we address to the extent possible.
Here we develop a set of discrete approximations for Laplacian estimates at
peripheral electrodes and a possible solution to the problem of multiple-frame
regularization. We also provide the mathematical details of finite difference
approximations that are missing in the literature, and discuss the problem of
computational performance, which is particularly important in the context of
EEG splines where data sets can be very large. Along this line, the matrix
representation of the surface Laplacian operator is carefully discussed and
some figures are given illustrating the advantages of this approach. In the
final remarks, we briefly sketch a possible way to incorporate finite-size
electrodes into Laplacian estimates that could guide further developments.Comment: 43 pages, 8 figure
Bioresorbable silicon electronics for transient spatiotemporal mapping of electrical activity from the cerebral cortex.
Bioresorbable silicon electronics technology offers unprecedented opportunities to deploy advanced implantable monitoring systems that eliminate risks, cost and discomfort associated with surgical extraction. Applications include postoperative monitoring and transient physiologic recording after percutaneous or minimally invasive placement of vascular, cardiac, orthopaedic, neural or other devices. We present an embodiment of these materials in both passive and actively addressed arrays of bioresorbable silicon electrodes with multiplexing capabilities, which record in vivo electrophysiological signals from the cortical surface and the subgaleal space. The devices detect normal physiologic and epileptiform activity, both in acute and chronic recordings. Comparative studies show sensor performance comparable to standard clinical systems and reduced tissue reactivity relative to conventional clinical electrocorticography (ECoG) electrodes. This technology offers general applicability in neural interfaces, with additional potential utility in treatment of disorders where transient monitoring and modulation of physiologic function, implant integrity and tissue recovery or regeneration are required
Frequency dependence of signal power and spatial reach of the local field potential
The first recording of electrical potential from brain activity was reported
already in 1875, but still the interpretation of the signal is debated. To take
full advantage of the new generation of microelectrodes with hundreds or even
thousands of electrode contacts, an accurate quantitative link between what is
measured and the underlying neural circuit activity is needed. Here we address
the question of how the observed frequency dependence of recorded local field
potentials (LFPs) should be interpreted. By use of a well-established
biophysical modeling scheme, combined with detailed reconstructed neuronal
morphologies, we find that correlations in the synaptic inputs onto a
population of pyramidal cells may significantly boost the low-frequency
components of the generated LFP. We further find that these low-frequency
components may be less `local' than the high-frequency LFP components in the
sense that (1) the size of signal-generation region of the LFP recorded at an
electrode is larger and (2) that the LFP generated by a synaptically activated
population spreads further outside the population edge due to volume
conduction
Seizure-onset mapping based on time-variant multivariate functional connectivity analysis of high-dimensional intracranial EEG : a Kalman filter approach
The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (< 60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach
Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity
The increasing number of recording electrodes enhances the capability of
capturing the network's cooperative activity, however, using too many monitors
might alter the properties of the measured neural network and induce noise.
Using a technique that merges simultaneous multi-patch-clamp and
multi-electrode array recordings of neural networks in-vitro, we show that the
membrane potential of a single neuron is a reliable and super-sensitive probe
for monitoring such cooperative activities and their detailed rhythms.
Specifically, the membrane potential and the spiking activity of a single
neuron are either highly correlated or highly anti-correlated with the
time-dependent macroscopic activity of the entire network. This surprising
observation also sheds light on the cooperative origin of neuronal burst in
cultured networks. Our findings present an alternative flexible approach to the
technique based on a massive tiling of networks by large-scale arrays of
electrodes to monitor their activity.Comment: 36 pages, 9 figure
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