DOI: 10.5061/dryad.hx3ffbgm0
title: Spike-induced cytoarchitectonic changes in epileptic human cortex are reduced via MAP2K inhibition

description: Interictal spikes are electroencephalographic discharges that occur at or
 near brain regions that produce epileptic seizures. While their role in
 generating seizures is not well understood, spikes have profound effects
 on cognition and behavior, depending on where and when they occur. We
 previously demonstrated that spiking areas of the human neocortex show
 sustained MAPK activation in superficial cortical layers I-III and are
 associated with microlesions in deeper cortical areas characterized by
 reduced neuronal nuclear protein (NeuN) staining and increased microglial
 infiltration. Based on these findings, we chose to investigate additional
 neuronal populations within microlesions, specifically inhibitory
 interneurons. Additionally, we hypothesized that spiking would be
 sufficient to induce similar cytoarchitectonic changes within the rat
 cortex and that inhibition of MAPK signaling, using a MAP2K inhibitor,
 would not only inhibit spike formation but also reduce these
 cytoarchitectonic changes and improve behavioral outcomes. To
 test these hypotheses, we analyzed tissue samples from 16 patients with
 intractable epilepsy who required cortical resections. We also utilized a
 tetanus toxin-induced animal model of interictal spiking, designed to
 produce spikes without seizures in male Sprague-Dawley rats. Rats were
 fitted with epidural electrodes, to permit EEG recording for the duration
 of the study, and automated algorithms were implemented to quantify
 spikes. After 6 months, animals were sacrificed to assess the effects of
 chronic spiking on cortical cytoarchitecture. Here, we show that
 microlesions may promote excitability due to a significant reduction of
 inhibitory neurons that could be responsible for promoting interictal
 spikes in superficial layers. Similarly, we found that the induction of
 epileptic spikes in the rat model produced analogous changes, including
 reduced NeuN, calbindin, and parvalbumin-positive neurons and increased
 microglia, suggesting that spikes are sufficient for inducing these
 cytoarchitectonic changes in humans. Finally, we implicated MAPK signaling
 as a driving force producing these pathological changes. Using CI-1040 to
 inhibit MAP2K, both acutely and after spikes developed, resulted in fewer
 interictal spikes, reduced microglial activation, and less inhibitory
 neuron loss. Treated animals had significantly fewer high-amplitude,
 short-duration spikes, which correlated with improved spatial memory
 performance on the Barnes maze. Together, our results provide
 evidence for a cytoarchitectonic pathogenesis underlying the epileptic
 cortex, which can be ameliorated through both early and delayed MAP2K
 inhibition. These findings highlight the potential role of CI-1040 as a
 pharmacological treatment that could prevent the development of epileptic
 activity and reduce cognitive impairment in both patients with epilepsy
 and those with non-epileptic spike-associated neurobehavioral disorders.
description: 40 two-month-old male Sprague-Dawley rats were utilized for this
 study. Briefly, rats were anesthetized and 7 electrode holes were drilled
 through the skull (3 left and 3 right electrode holes located at the
 following locations relative to bregma: AP +4 mm, ML 3.5 mm; AP -1 mm, ML
 3.5 mm; AP -6 mm, ML 3.5 mm; 1 reference electrode hole above the nasal
 sinus: AP +10.5 mm, ML 0.5 mm left). Before electrode screw placement,
 rats received an injection of either 1 uL of sterile PBS alone (sham rats)
 or 80 ng of tetanus toxin (TeNT rats) in 1 uL of sterile PBS into the left
 somatosensory cortex (corresponding to the site of the left middle
 electrode: AP -1 mm, ML 3.5 mm left, DV -1.5 mm). Epidural electrode
 screws were then placed, and rats were fitted with head cap
 apparatuses. Beginning on postoperative day 5,
 electroencephalography (EEG) recordings were conducted every 5 days
 through postoperative day 35. From post-operative days 35-84, EEG
 recordings were conducted once weekly. After postoperative day 84, EEG
 recordings occurred once per month until the 6-month study
 endpoint. Rats in both the TeNT and sham groups were
 further subdivided into 3 drug treatment groups: early drug treatment with
 an MEK inhibitor (post-operative days 0-7), delayed drug treatment
 (post-operative days 15-21), or no drug treatment. EEG
 data was acquired at a sampling rate of 1000 Hz using Stellate HARMONIE
 software (Version 6.0, Stellate Systems Inc.) and converted into European
 Data Format (EDF) files for subsequent visualization (EDFbrowser, Version
 1.76; Free Software Foundation) and data analysis. To analyze EEG data for
 the presence of interictal spikes, we implemented the spike-detection
 algorithm included with this dataset. Briefly, EDF files were imported
 into MATLAB using the edfread function. EDF files were filtered with a
 1-35 Hz fourth-order Butterworth bandpass filter. Each electrode was
 analyzed for the presence of spikes; spikes were defined as high-amplitude
 events with negative polarity, shorter than 200 ms in duration, with a
 maximum voltage greater than one standard deviation above the median
 background signal. Both spikes (&lt; 70 ms) and sharp waves (70-200
 ms) were included in the overall spike count. Spikes were then divided
 into two half waves (Fig. 2), for which amplitude, duration, and slope
 were calculated. The estimation of these metrics depends on the accurate
 identification of the start and end points (edges) of each spike. These
 can be identified visually as the locations of the first trough points
 (local minima) that occur just before (start) and after (end) the point of
 maximum voltage. After identifying the spike location (point of maximum
 voltage), edge detection was performed in three essential steps. First,
 the raw signals were filtered using a fourth-order Butterworth highpass
 filter with a 7 Hz cutoff frequency to eliminate the confounding effect of
 low-frequency background or prominent slow waves. Next, the filtered
 signals were smoothed using a 12-point moving average window. Finally, the
 raw, filtered, and smoothed signals were inverted, and potential edge
 points were identified using the findpeaks function. "True"
 edges were designated as those that were detected consistently across the
 raw, filtered, and smoothed signals. Moreover, the maximum amplitude and
 total duration for each spike was determined. For each recording day,
 spike counts were normalized using the total recording length (mean
 spikes/h).
description: EDF browser was used for data visualization and
 analysis. R or RStudio can be used to open the RDA
 file.
description: # Spike-induced cytoarchitectonic changes in the epileptic human cortex
 are reduced via MAP2K inhibition --- ## Description of the data and file
 structure Variables in R Dataset (SpikeData_Cleaned_withRecTime.Rda): *
 SpikeTimeSec: Identifies the time that a spike was observed on the EDF
 file, relative to the file start time. * OspikeV: Maximum spike voltage
 (uV) * Ldur2: Duration of the left half-spike (ms) * Rdur2: Duration of
 the right half-spike (ms) * Tdur2: Total spike duration (ms) * OLslope2:
 Slope of the left half-spike * ORslope2: Slope of the right half-spike *
 OLamp2: Amplitude of the left half-spike (uV) * ORamp2: Amplitude of the
 right half-spike (uV) * CH: Corresponds to the electrode recording site.
 1: right anterior electrode; 2: right middle electrode; 3: right posterior
 electrode; 4: left anterior electrode; 5: left middle electrode (site of
 TeNT or saline injection); 6: left posterior electrode * Rat: Individual
 rat identification number * Group: Corresponds to the rat treatment group.
 SND: sham, no drug; SED: sham, early drug group; SDD: sham, delayed drug
 group; TND: TeNT, no drug; TED: TeNT, early drug group; TDD: TeNT, delayed
 drug group * Day: Post-operative recording day * MaxAmp: Maximum half-wave
 amplitude of each spike; i.e. for each spike, MaxAmp = max(OLamp2,ORamp2)
 * delAmp: the difference between half-wave amplitudes of each spike; i.e.
 for each spike, delAmp = MaxAmp min(OLamp2,ORamp2) * TTx: Did the rat
 receive tetanus toxin? 0 = no (sham rat), 1 = yes (TeNT rat) * Area1:
 Calculated area under each spike (uV x ms) * RecTime: Total EEG recording
 length by day (hours); used to calculate mean spikes/h for each recording
 day ## Sharing/Access information This data is not publicly accessible at
 any other location and is not derived from any additional sources. ##
 Code/Software Matlab Scripts to Detect Spike Events and Extract Morphology
 Parameters NOTE: .m files do NOT require Matlab to be opened/read. Any
 text editor/reader (e.g. Notepad++ for Windows, TextEdit for MacOS) will
 do. Even IDEs for other languages (e.g. R) will work. 1. edfread()
 Essential Function(s): reads EEG European Data Format (EDF) files
 (extension: .rec/.edf) into Matlab arrays Input(s): name of raw EEG
 recording as .rec/.edf file Output(s): header information and raw signal
 information (as a Matlab array) Developer(s): Brett Shoelson, PhD
 ([brett.shoelson@mathworks.com](mailto:brett.shoelson@mathworks.com))
 [Copyright 2009 - 2012 MathWorks, Inc] Note(s): see detailed description
 in the code file Dependent Function(s): mRatSpikeBatch() 2.
 edfreadUntilDone() Essential Function(s): reads EEG European Data Format
 (EDF) files (extension: .rec/.edf) into Matlab arrays Input(s): raw EEG
 recording as .rec/.edf file Output(s): header information and raw signal
 information (as a Matlab array) Developer(s): Brett Shoelson, PhD
 ([brett.shoelson@mathworks.com](mailto:brett.shoelson@mathworks.com))
 [Copyright 2009 - 2012 MathWorks, Inc] Note(s): see detailed description
 in code file; called within edfread() under certain conditions if
 necessary based on the read of the file Dependent Function(s): edfread()
 3. mRatSpikebatch() Essential Function(s): detects (probable/possible)
 spike events, records locations (in time, with respect to start of file)
 and preliminary morphological parameters for specific animals on specific
 recording days Input(s): raw EEG recording as .rec/.edf file; list/range
 of electrode channels to analyze; combined string containing name of
 animal + corresponding recording day to be analyzed (e.g.
 "TT101DAY5") Output(s): CSV file with locations and morphologic
 parameters of all detected spike events (name = "
 PeaksProperties.csv"); CSV file detailing annotations for (possible)
 seizure-like events (NOT used) Developer(s): Biswajit Maharathi, PhD
 Note(s): see detailed description in code file; the morphological
 parameters computed with this script are NOT the final ones we use in the
 analysis; those are computed with the mRatSpike_NewEdgeDetector_LFF()
 script 4. mRatSpike_NewEdgeDetector_LFF() Essential Function(s): uses
 previously detected events from specific animals on specific recording
 days (i.e. raw signal data + output from mRatSpikeBatch() function) +
 stores NEW (i.e. adjusted/corrected) morphologic parameters Input(s): same
 as mRatSpikeBatch() conveniently :) Output(s): CSV file with locations and
 morphologic parameters of all detected spike events (name = "
 PeaksProperties_V2.csv") Developer(s): Mitchell Butler
 ([mbutle28@uic.edu](mailto:mbutle28@uic.edu)), Biswajit Maharathi, PhD
 University of Illinois at Chicago Note(s): adjusted/corrected parameters
 are based on an expanded definition of allowed duration (up to 200ms) +
 use a multi-layer check to verify the true locations of the
 "edges" (i.e. leftmost and rightmost points) of each spike
 event; combines low-frequency filtering + moving average smoothing to
 ascertain these points; see description and annotations in code file for
 further details Matlab Code Instructions (per individual recording) Step
 0: ensure all functions are stored locally / on an accessible drive and on
 the Matlab path Step 1: Run mRatSpikeBatch() Step 2: Run
 mRatSpike_NewEdgeDetector_LFF()