ances in neural electrode technology are enabling brain recordings with increasingly fine spatial and temporal resolution. We explore spatio-temporal (ST) patterns of local field potential spikes using a new high-density active electrode array with 500 μm resolution. We record subdural micro-electrocorticographic (μECoG) signals in vivo from a feline model of acute neocortical epileptiform spikes and seizures induced with local administration of the GABA antagonist, picrotoxin. We employ a clustering algorithm to separate 2-dimensional (2-D) spike patterns to isolate distinct classes of spikes unique to the interictal and ictal states. Our findings indicate that the 2-D patterns can be used to distinguish seizures from non-seizure state. We find two statistically significant ST patterns that uniquely characterize ictal epochs. We conclude that millimeter-scale ST spike dynamics contain useful information about ictal state. This finding may be important to understanding mechanisms underlying local circuit activity during seizure generation. Such information about spatio-temporal profiles permits investigation of seizure dynamics and their underlying mechanisms and inform new electrical stimulation protocols for seizure termination.
Introduction: In epilepsy research, many different brain recording techniques have been employed to understand neural dynamics between, prior to and during seizures. Electrophysiological studies employ techniques that range in size and scale from the Utah array [1], which records multi-unit activity from penetrating electrodes 400 μm apart, to scalp Electroencephalography (EEG), which records electrical potentials that are distorted by the skull, scalp, cerebrospinal fluid (CSF) and soft tissues, from electrodes many centimeters apart. Other studies utilize modalities ranging from Magnetoencephalography (MEG) and functional Magnetic Resonance Imaging (fMRI) to Voltage Sensitive Dyes (VSDs). MEG is a non-invasive technique that records magnetic components of potentials perpendicular to the EEG, and suffers from low spatial resolution, similar to EEG. Image-based technologies such as fMRI and VSDs complement standard electrophysiology, though each has its own spatial and temporal resolution limitations. In this study, we use a new, high-spatial density subdural surface active electrode array of 360 channels covering an area of 10 mm×9 mm to measure local field potential (LFP)-scale electrical signals in vivo from an acute feline epilepsy model. We examine spatio-temporal (ST) patterns of LFP spike activity recorded on a millimeter-scale that are unique to seizures.
METHODS. Animal Recording: We analyze micro-electrocorticographic (μECoG) data from an acute in vivo feline model of epilepsy. Adult cats are anesthetized with a continuous infusion (3˜10 mg/kg/hr) of intravenous thiopental. A craniotomy and durotomy were performed to expose a 2×3 cm region of cortex. The high resolution electrode array is then placed on the surface of the brain over primary visual cortex, localized by electrophysiological recordings of visual evoked potentials. Picrotoxin, a GABA-A receptor antagonist that blocks inhibition, is topically applied adjacent to the anterior-medial corner of the electrode array in an amount sufficient to induce abnormal electrical spikes and seizures from the covered region [2].
The active electrode array placed on the cortex is used to record data from 360 independent channels arranged in 20 columns and 18 rows, spaced 500 μm apart. Each electrode contact comprises a 300 μm×300 μm square of platinum. Two high-performance, flexible silicon transistors for each electrode buffered and multiplexed the recorded signals[3]. The total array size is 10 mm×9 mm. Signals are recorded with an effective sampling rate of 277.7 Hz per channel. FIG. 34 is a photograph of the array placed on the surface of the visual cortex of a cat.
We analyze 13 minutes and 40 seconds of data for this study, containing 724 automatically detected spikes and 2 seizures, verified by expert review.
Pre-processing: All recordings are first band pass filtered between 1 and 50 Hz using a 6th-order butterworth filter in the forward and reverse direction, using Matlab's filtfilt function, to achieve zero-phase filtering.
64 of 360 channels are non-functional due to manufacturing imperfections. The missing data for these channels are interpolated from the surrounding electrodes using a 2-D averaging spatial filter of window size 3×3 pixels.
Small offsets in time that result from row multiplexing are corrected by upsampling and shifting the data in order to accurately align rows in time. Data are first upsampled by the number of rows within the array (18) via Matlab's interp function, which applies a low pass filter interpolation algorithm. Data are then temporally shifted by their respective row offset.
Spike Detection: We use a voltage-threshold detector to detect spikes on the signal obtained by averaging all 360 channels. The voltage threshold is set by visual inspection at about 500 μV. When the average signal crossed the threshold from above, a 160 msec segment of the recording is stored (60 msec prior to the crossing and 100 msec post-crossing).
We analyze only single spikes (i.e. no poly-spikes) and retain only spikes which occur on a majority of the electrodes in the 2-D array in order to simplify the analysis of spike propagation. Specifically, the following quantitative criteria are used to retain spikes: 1) a single negative-going threshold crossing followed by a single positive-going threshold crossing within the clipped 160 msec window surrounding the triggering threshold crossing; 2) at least 50% of electrodes have a root-mean-square (RMS) value >40% of the maximal RMS across channels (within the 160 msec window). 724 average spikes met the above criteria. FIG. 35 shows 16 representative detected spike waveforms.
Feature Selection: For each of the 724 spikes, a 63-element feature vector is generated in the following manner: The average spike waveform is cross-correlated with each of the 360 single-channel spikes. This yields a single value per channel capturing the delay of the spike on each channel of the array. The zero-meaned RMS (i.e. standard deviation) for each channel is then calculated. This yielded a single value per channel capturing a representation of the power of the spike on each channel of the array. The 724×360 delay values are then normalized by dividing by each row maximum. The 724×360 ‘power’ values are similarly normalized. The 724×360 matrix of delay values is then concatenated with the 724×360 matrix of ‘power’ values. The result is a 724×720 matrix representing 724 spikes, each with 720 features. Principal components analysis (PCA) is performed and a number of dimensions accounting for >99% of the data variance is retained. This results in a dimensionality reduction from 720 to 63.
Clustering: k-mediods clustering [4] is performed and the gap statistic [5] is used to determine the number of clusters, similar to methods applied in other EEG classification tasks [6]. 16 clusters of ST patterns are identified. FIG. 36 shows delay maps for spikes clustered closest in L1 distance to the centroid of each distinct cluster. Blue indicates electrodes with early delay values relative to the average spike waveform and red indicates electrodes with later delay values. All analysis is performed in the Matlab® environment (The Mathworks Inc., Natick, Mass., USA).
Statistical Testing: We hypothesize that some ST patterns occur preferentially during seizure epochs. We use Pearson's chi-squared test to test the null hypothesis that the proportion of spikes occurring during seizure is equal across clusters (i.e. ST patterns). To address the issue of identifying a specific cluster which might account for a rejection of the null hypothesis, we conduct a permutation test. We hold cluster membership of each spike fixed while randomly permuting the seizure and non-seizure labels for one million permutations. For every permutation we record the maximum (over all clusters) of the proportion of seizure-spikes within each cluster to obtain the null distribution. We then compare the observed maximum to this null distribution.
RESULTS: FIG. 37 displays spike clustering and seizure analysis results. Each of the pies represents one of the 16 different spike ST patterns identified. Delay maps of representative spikes from each corresponding cluster are shown in FIG. 36. The whole area of each pie in FIG. 37 is scaled in proportion to the total number of spikes within the cluster. The blue section within each pie represents the percentage of spikes associated with non-seizure epochs. The white section within each pie represents the percentage of spikes recorded during seizures. Clusters 3 and 6 appear to have disproportionately large numbers of spikes occurring during seizure relative to outside.
We found a strong relationship between ST pattern and seizure state (i.e. within or outside of a seizure epoch). We reject the null hypothesis that the proportion of spikes occurring during seizure in each of the 16 clusters is the same (χ2(15, N=724)=415.1, p<<0.0001). Furthermore, we find the proportion of within-seizure spikes contained specifically in clusters 3 and 6 are significantly higher than would be expected by chance (p<<0.0001 for both clusters).
DISCUSSION: Our analysis indicates that two specific ST spike patterns correlate with seizure epochs. In addition, we found other ST spike patterns that appear to be more loosely associated with seizures. We believe those patterns indicate periods of transition from the interictal to ictal states. We believe these patterns may hold information about the progression of abnormal electrical activity as seizures approach. Analyzing the brain's electrical activity using any of the electrode arrays provided herein provide new opportunities to increase our understanding of epileptiform spikes and their patterns of propagation. One possibility raised by these waveforms, is that spikes and seizures, when viewed at this resolution, may have features in common with cardiac dysrhythmias. In