Ictal HFOs identify seizure territories
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Supplemental Data
Figure S1: Action potentials are phase locked to high gamma oscillations. Representative
traces of high gamma (red) and corresponding multi-unit activity (black) recorded from the
penumbra (top) and the ictal core during (middle) and after (bottom) passage of the ictal
wavefront. The phase angle distribution demonstrates particularly robust phase locking in the
ictal core (right). Despite the stable amplitude of the multi-unit activity the amplitude of the
high gamma oscillations was increased in the core relative to the penumbra. Note that in the
ictal core, the high gamma oscillation is larger amplitude and leads the multiunit firing burst,
suggesting that firing outside the listening sphere of the electrode is contributing. This indicates
that high gamma currents have a larger driving force than action potentials, which would be
necessary for detection of high gamma oscillations at the cortical surface.
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Ictal HFOs identify seizure territories
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Figure S2: Ictal multi-unit activity correlates with stronger high gamma oscillations in the ictal
core than in the penumbra (a) Scatter plot of spike rate and high gamma amplitude during the
preictal period (blue, r=0.52, slope=8.80, p<0.001), and during the subsequent full ictal
activation (red, r=0.55, slope=43.96 p<0.001) in one seizure recorded from patient A. (b) Bar
plot comparing the slope of preictal (blue) and ictal (purple, red) correlation between spike rate
and high gamma power in both patients in which the seizure fully invaded the microelectrode
array territory (red, n=4) and for seizures that did not invade the microelectrode array (purple,
“penumbral activity”; n=6).
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Ictal HFOs identify seizure territories
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Figure S3: Examples of ictal high frequency oscillations (HFOs) recorded from the ictal core in
subdural and micro-electrode recordings. Wideband tracings are shown for a segment of the
ongoing ictal rhythm for each patient (top traces), paired with Morlet wavelet power-frequency
spectrograms (bottom plots). The frequency and power scales were chosen in each case to
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Ictal HFOs identify seizure territories
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demonstrate the presence or absence of the signature characteristics of HFOs.
(a) Ictal
discharges recorded from a subdural electrode in the presumptive ictal core from patient C with
superimposed HFOs visible in the raw signal. Note that superimposed HFOs are much smaller in
amplitude than the high-amplitude low-frequency waveform, making them difficult to
appreciate in the unfiltered signal.
However, they are more easily appreciated in the
spectrogram plots, which demonstrate “islands” with peak activity in the 80-150 Hz band. (b)
Ictal discharges recorded from a subdural electrode in the presumptive ictal core from patient D
(above). The HFOs are again difficult to discriminate visually, yet the corresponding Morlet
wavelet transform demonstrates clear “islands”.
(c) Ictal core discharges recorded from
subdural electrodes in patient A (above). The discharges exhibit sharp transients, with visible
HFOs and corresponding spectrogram “islands” evident during the discharges marked with a
single asterisk (*).
These contrast with high frequency activity lacking “islands” during
discharges indicated by the double asterisk (**). Instead, these discharges produce a tapered
cone appearance on the spectrogram, without a clear power peak in the high gamma band. (d)
Ictal discharges recorded from a subdural electrode in the ictal core from patient B (above), also
shown in Fig 2d. Again, both high frequency transients and spectrogram “islands” are evident.
(e) Microelectrode recording from patient A of the same ictal discharges shown in panel C. The
spectrogram islands of high gamma power during each discharge are now more clearly
discerned. Note that the frequencies of these islands are higher than they are in subdural
recordings. Simultaneously, increased power in the multi-unit activity band (300-3000 Hz) is
evident. (f) A simulation consisting of a train of Gaussian waveforms with a half width similar to
the ictal discharges recorded by subdural electrodes from patient A. Corresponding Morlet
wavelet transform demonstrates energy spread over the entire frequency range, resulting in a
tapered cone appearance, with no clear activity islands.
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Ictal HFOs identify seizure territories
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Figure S4: Examples of subdural and micro-electrode recordings lacking ictal high frequency
oscillations. Unfiltered traces and corresponding Morlet wavelet spectrograms are shown as in
Fig. S3. (a) Subdural recording from an electrode overlying the microelectrode array in patient C,
a penumbral site (top). The spectrogram demonstrates scarce power in the high gamma band
and activity resembling the tapered cone appearance described in Fig S3. (b) A similar subdural
ictal recording from an electrode adjacent to the microelectrode array, again demonstrating a
lack of distinct activity in the high gamma band. (c,d) Subdural recordings from electrodes 2 cm
away from the microelectrode array in patients A and B, respectively, prior to incorporation into
the ictal core based on the microelectrode-recorded multi-unit firing pattern and PLHG
measure. Similar to the examples in panels A and B, the spectrogram demonstrates a lack of
high gamma band activity, with a tapered-cone appearance showing energy spread over lower
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Ictal HFOs identify seizure territories
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frequencies. This is again consistent with effects of sharp transients, rather than distinct high
frequency oscillations at the discharge peaks, which are less evident in the unfiltered traces. (e)
Microelectrode recording from patient C, when multi-unit activity demonstrated heterogeneous
firing characteristic of the penumbra. There are no islands of high gamma activity evident in the
spectrogram, but there is sporadic power in the (300-3000 Hz) band corresponding to neural
firing.
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Ictal HFOs identify seizure territories
Pre-recruitment
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Recruitment
Post-recruitment
(n=203)
ISI
Penumbra
(n=220)
42.29 +/- 3.13
12.72 +/- 0.53
31.69 +/- 1.11
333.27 +/- 36.40
ms
ms
ms
ISI CV
2.60 +/- 0.06
1.56 +/- 0.02
1.51 +/- 0.02
1.55+/-0.33
% Electrodes
69.53 +/- 16.12%
72.20 +/- 11.1%
88.07 +/- 3.7 %
29.79 +/- 5.1%
Z statistic
12.0 +/- 0.567
9.13 +/- 0.37
21.05 +/- 1.32
4.560+/-0.26
Mean phase
145.48+/-1.64
161.53+/-1.79
147.24 +/- 1.69
172.74+/-6.15,
with MUA phaselocked to HG
angle
Supplementary Table S1: Action potentials are phase locked to the trough of the high gamma
oscillation during the seizure. (a) Inter-spike interval (ISI), coefficient of variation (CV) of the
interspike interval, and results of Rayleigh’s test (significance at p < 0.05, Z statistic) for circular
non-uniformity for action potentials during each seizure stage and in the ictal penumbra.
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Ictal HFOs identify seizure territories
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R2
F
p
n
A seizure
0.2631
31.4174
0.00001
90
B seizure 1
0.3813
56.1970
0.00001
93
B seizure 2
0.0309
3.789
0.0539
121
B seizure 3
0.0702
8.984
0.0033
121
(spike cross-correlation vs. subdural electrode
PLV)
Supplementary Table S2: High gamma cross frequency coupling in the subdural EEG overlying
the multi-electrode array correlates with the multi-unit spiking cross correlation during the
seizure. Results of linear regression analysis between multi-unit cross correlation (bottom) and
subdural phase locking value for all 4 seizures captured by the microelectrode array in the ictal
core.
Supplementary Movies S1-S4: Mapping seizure activity using high frequency oscillations.
Movies demonstrating dynamic changes in phase locked high gamma and line length during the
initial time period of seizures in the four patients from whom seizures were recorded with the
microelectrode array (small square) positioned below the subdural electrode grid, depicted
schematically by circles. All grid electrodes were 3mm diameter, spaced 1 cm center to center.
Ictal wavefront passage and subsequent core activity was detected by the microelectrode array
in patients A (Movie S1) and B (Movie S2). The array recorded only penumbral activity in
patients D (Movie S3) and C (Movie S4). The left panels in each movie depicts a line length
measure used to approximate the arrival of the seizure at each electrode as judged by the
visible range of EEG. Subthreshold values are indicated by the copper color scale, and channels
meeting “recruitment” criteria (> 2.5 SD over preictal baseline x 2 seconds) are marked in solid
blue. Similarly, PLHG values are shown in the right panels, with channels meeting threshold
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Ictal HFOs identify seizure territories
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marked in solid red. The movies show the dramatic contrast in the apparent seizure progression
as indicated by the two criteria, with PLHG progression corresponding well with the timing (or
failure) and direction of wavefront propagation as detected by the microelectrode array. At any
given time, the extent of the seizure core is far smaller by PLHG criteria than it is by linelength
criteria, an indication of the large extent of the penumbra and the degree to which it influences
seizure localization.
Supplementary Methods
Subject enrollment and surgical procedures: Participants consisted of adults with
pharmacoresistant focal epilepsy who underwent chronic invasive EEG studies to help identify
the epileptogenic zone for subsequent removal. Enrollment was restricted to patients for whom
the presurgical evaluation indicated good localization, and invasive recordings were required to
refine the resection boundaries. The study was approved by the Institutional Review Board of
the Columbia University Medical Center and informed consent was obtained by each patient
prior to implantation. A 96 channel microelectrode, 4 mm×4 mm array recording from cortical
layers III-V (Neuroport, Blackrock Microsystems Inc., Salt Lake City, UT) (House et al., 2006),
was implanted along with subdural electrodes (Ad-Tech Medical Instrument Corp., Racine WI or
PMT Corp., Chanhassen, MN). The individual microelectrodes were platinum coated silicon,
protruding 1 mm from the array base, electrically insulated except for the terminal 70 m.
Electrode impedance at manufacture was 322  138 k. The microelectrode array was
implanted into neocortical gyri through the pia matter using a pneumatic insertion technique
(Waziri et al., 2009). The implant site was selected from presurgical EEG studies, and eloquent
cortical sites such as Broca’s area were avoided. In all four patients the microelectrode array
implantation site was within the seizure onset zone and was subsequently surgically treated.
Multi-unit activity analysis:
Calculations were performed using in-house software
(Matlab, Mathworks, Natick, MA) and FIELDTRIP (http://fieldtrip.fcdonders.nl/) (Oostenveld et
al., 2011). To compare the multi-unit firing rate from each channel with local field potential
power, the complete seizure and 10 second preictal epoch were divided into bins with a width
of 200 ms. Power in each bin was computed by applying a multitaper FFT for spectral
smoothing. The Spearman rank correlation coefficient and linear regression slope between spike
rate and LFP power was calculated across all time bins. These comparisons were repeated for
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Ictal HFOs identify seizure territories
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power in frequencies 1-500 Hz, divided into 5 Hz frequency bins. Correlations between firing
rate and high gamma power were computed by summating the power in the 80-150 Hz
frequency bins.
Normalized overall firing rate for each seizure was calculated from the
summation of spikes for each channel in each bin across all bins. Interspike interval and the
coefficient of variation of the interspike interval were calculated using the “spike train analysis
techniques
toolbox”
(http://glab.bcm.tmc.edu/
signal_processing_techniques/signal_proc.html).
To calculate the phase angle of neuronal firing with respect to the high gamma oscillation
we applied the Hilbert transform to the microelectrode recording after band pass filtering the
signal (80-150 Hz) and subsequently manually separating the seizure into penumbra, ictal
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∞ 𝑥[𝜏]
wavefront, and ictal core epochs. The Hilbert transform is defined as y[t]=H(x[t])=𝜋 ∫−∞ 𝑡−𝜏 𝑑𝜏
and results in the analytic signal z[n]=a[n]exp(i
is the instantaneous phase. We then used spike timing tspike to calculate the
spike]
of each action potential with respect to the high gamma
oscillation. We calculated the first trigonometric moment of these phase angles using the
equation m’=∑ 𝑒𝑥𝑝𝑖𝜙𝑠𝑝𝑖𝑘𝑒 = R𝑒𝑥𝑝𝑖𝜃 . Rayleigh’s Z-test for circular uniformity was calculated as
Z=nR2. The probability that the null hypothesis holds was estimated as 𝑝 = 𝑒𝑥𝑝−𝑍 .
Cross correlation of multi-unit activity was performed by calculating the spike rate in bin
sizes of five ms for each active channel across a 10 second interval two minutes prior to seizure
and during the entire seizure (D, C) or during seizure post-recruitment (B, A). Across all bins the
cross correlation value was calculated between all channel pairs. Each cross correlation
calculation included three bins before, and three bins after the bin of interest for the channel
pair. To create a time series of multi-unit cross correlation, the seizure was further divided into
333 ms bins and multi-unit cross correlation across all electrodes was calculated as described
above for each bin. Statistical significance of the cross correlation between channel pairs was
performed with a bootstrapping method by circularly shifting the bins by a random amount
(varying up to one bin) and calculating the 98th percentile of the Rayleigh Z statistic for 1,000
trials to determine the per-channel threshold.
References
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Ictal HFOs identify seizure territories
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House PA, MacDonald JD, Tresco PA, Normann RA. Acute microelectrode array implantation into
human neocortex: preliminary technique and histological considerations. Neurosurgical Focus
2006; 20: E4.
Oostenveld R, Fries P, Maris E. FieldTrip: Open source software for advanced analysis of MEG,
EEG, and invasive electrophysiological data. Comput Intell Neurosci 2011; 2011:156869.
Waziri A, Schevon CA, Cappell J, Emerson RG, McKhann GM II, Goodman RR. Initial surgical
experience with a dense cortical microarray in epileptic patients undergoing craniotomy for
subdural electrode implantation. Neurosurgery 2009; 64: 540–545.
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