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Epilepsia. 2021;00:1–14.
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wileyonlinelibrary.com/journal/epi
Received: 8 July 2021
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Revised: 30 November 2021
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Accepted: 30 November 2021
DOI: 10.1111/epi.17145
FULL- LENGTH ORIGINAL RESEARCH
Correcting for physiological ripples improves epileptic focus
identification and outcome prediction
Willemiek J. E. M.Zweiphenning1,2
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Nicolásvon Ellenrieder1
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FrançoisDubeau1
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LaurenceMartineau3
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LorellaMinotti3
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Jeffery A.Hall1
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StephanChabardes4
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RoyDudley1
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PhilippeKahane3
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JeanGotman1
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BirgitFrauscher1
This is an open access article under the terms of the Creat ive Commo ns Attri bution-NonCo mmercial License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited and is not used for commercial purposes.
© 2021 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy
1Montreal Neurological Institute and
Hospital, McGill University, Montreal,
Quebec, Canada
2University Medical Center Utrecht,
Utrecht University, Utrecht, the
Netherlands
3Department of Neurology, Grenoble-
Alpes University Hospital and
Grenoble- Alpes University, Grenoble,
France
4Department of Neurosurgery,
Grenoble- Alpes University Hospital
and Grenoble- Alpes University,
Grenoble, France
Correspondence
Willemiek Zweiphenning, Department
of Neurology and Neurosurgery,
University Medical Center Utrecht, HP
C03.1.31 Heidelberglaan 100, Utrecht
3584 CX, the Netherlands.
Email: W.J.E.Zweiphenning@
umcutrecht.nl
Birgit Frauscher, Analytical
Neurophysiology Lab, Montreal
Neurological Instutue and Hospital,
McGill University, 3801 University
Street, Montreal H3A 2B4, QC, Canada.
Email: birgit.frauscher@mcgill.ca
Funding information
Canadian Institute of Health
Research, Grant/Award Number:
FDN- 143208; Neurodis Foundation;
Fonds de Recherche du Québec -
Santé; Foundation De Drie Lichten;
Foundation Jo Kolk Studiefonds
Abstract
Objective: The integration of high- frequency oscillations (HFOs; ripples [80–
250Hz], fast ripples [250– 500Hz]) in epilepsy evaluation is hampered by physi-
ological HFOs, which cannot be reliably differentiated from pathological HFOs.
We evaluated whether defining abnormal HFO rates by statistical comparison to
region- specific physiological HFO rates observed in the healthy brain improves
identification of the epileptic focus and surgical outcome prediction.
Methods: We detected HFOs in 151 consecutive patients who underwent stereo-
electroencephalography and subsequent resective epilepsy surgery at two tertiary
epilepsy centers. We compared how HFOs identified the resection cavity and pre-
dicted seizure- free outcome using two thresholds from the literature (HFO rate >
1/min; 50% of the total number of a patient's HFOs) and three thresholds based
on normative rates from the Montreal Neurological Institute Open iEEG Atlas
(https://mni- open- ieega tlas.resea rch.mcgill.ca/): global Atlas threshold, regional
Atlas threshold, and regional + 10% threshold after regional Atlas correction.
Results: Using ripples, the regional + 10% threshold performed best for focus iden-
tification (77.3% accuracy, 27% sensitivity, 97.1% specificity, 80.6% positive predictive
value [PPV], 78.2% negative predictive value [NPV]) and outcome prediction (69.5%
accuracy, 58.6% sensitivity, 76.3% specificity, 60.7% PPV, 74.7% NPV). This was an
improvement for focus identification (+1.1% accuracy, +17.0% PPV; p<.001) and
outcome prediction (+12.0% sensitivity, +1.0% PPV; p=.05) compared to the 50%
threshold. The improvement was particularly marked for foci in cortex, where phys-
iological ripples are frequent (outcome: +35.3% sensitivity, +5.3% PPV; p=.014).
In these cases, the regional + 10% threshold outperformed fast ripple rate > 1/min
(+3.6% accuracy, +26.5% sensitivity, +21.6% PPV; p<.001) and seizure onset zone
(+13.5% accuracy, +29.4% sensitivity, +17.0% PPV; p<.05– .01) for outcome predic-
tion. Normalization did not improve the performance of fast ripples.
Significance: Defining abnormal HFO rates by statistical comparison to rates
in healthy tissue overcomes an important weakness in the clinical use of ripples.
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ZWEIPHENNING et al.
1
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INTRODUCTION
High- frequency oscillations (HFOs), subdivided into rip-
ples (80– 250Hz) and fast ripples (FRs; 250– 500Hz), are a
promising biomarker for epileptogenic tissue. Most stud-
ies have reported results at the group level, with higher
HFO rates in epileptogenic than nonepileptogenic tissue,
and a better correlation between favorable outcome and
removal of tissue generating HFOs than removal of tissue
generating interictal spikes or that is part of the seizure
onset zone (SOZ).1– 3 FRs recorded after resection have
been linked to seizure recurrence.4– 7
Prospective studies reported conflicting results on
the performance of HFOs to identify the SOZ and pre-
dict good outcome at the individual patient level.8– 10
They showed patient examples where the performance
of ripples was hindered by the detection of presum-
ably physiological ripples.8,10 That HFOs occur under
physiological conditions is a challenge when assessing
their validity as a biomarker for epilepsy. Physiological
HFOs occur predominantly in the ripple range, at rest
and linked to cognitive processes or evoked by tasks or
stimuli.11– 19 Pathological and physiological HFOs largely
overlap in their signal properties, and there is no reliable
way to separate them.17,20,21 The Montreal Neurological
Institute (MNI) Open iEEG Atlas project (https://mni-
open- ieega tlas.resea rch.mcgill.ca/)22,23studied HFOs in
carefully selected stereoelectroencephalographic (SEEG)
channels with normal electroencephalographic (EEG)
activity.24 The rate of physiological ripples varied sub-
stantially across different regions, with the highest val-
ues in the occipital, sensorimotor, and mesiotemporal
regions. Physiological FRs were rare, even in eloquent
cortical areas.
This study evaluated whether "correcting" for physio-
logical HFOs improves identification of the epileptic focus
and prediction of surgical outcome. We hypothesized that
using the statistical distribution of normative physiologi-
cal HFO rates for each region to define rates that are too
high for the physiological range, and therefore most likely
to be pathological, would increase the performance of this
marker. We expected the improvement to be most pro-
nounced in patients with a focus in or close to brain areas
generating high rates of physiological ripples. Because
physiological FRs are rare, we expected no improvement
for FRs.
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MATERIALS AND METHODS
2.1
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Patient selection and data
acquisition
We screened 202 consecutive patients undergoing SEEG
investigation followed by resective open epilepsy surgery
at Grenoble- Alpes University Hospital (CHUGA) or the
MNI between January 2009 and January 2019. For selec-
tion criteria and flowchart of patients' inclusion see Figure
1. This study was approved by the MNI Institutional
Review Board. All patients signed written informed
consent.
The MNI recordings were acquired with Harmonie
(Stellate) or Nihon- Kohden EEG amplifiers at a sampling
rate of 2000Hz, using homemade MNI or commercial
DIXI electrodes. The CHUGA recordings were acquired
with Micromed EEG amplifiers at sampling rates of 512,
1024, or 2048Hz, using DIXI or, in a few occasions, ALCIS
electrodes.
2.2
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Data selection and HFO analysis
Analogous to the MNI Open iEEG Atlas, we automati-
cally detected HFOs in visually selected 20- min sections
from non- rapid eye movement (NREM) sleep stages N2/
It improves focus identification and outcome prediction compared to standard
HFO measures, increasing their clinical applicability.
KEYWORDS
biomarker, epilepsy surgery, high- frequency oscillations, interictal, normative values
Key Points
• Normalization significantly improved the abil-
ity of ripples to identify the epileptic focus and
to predict seizure freedom
• The regional + 10% threshold exhibited the best
performance
• Normalization is particularly useful in patients
with a focus in cortex with high physiological
ripple rates
• Ripple normalization outperformed fast ripple
rate > 1/min and seizure onset zone in patients
with a focus in ripple- rich cortex
• Normalization did not improve the perfor-
mance of fast ripples in identifying the epileptic
focus or predicting seizure freedom
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ZWEIPHENNING et al.
N3,24 as this state shows the highest HFO rates25– 27 and
best identifies the epileptic focus in the interictal EEG.28 If
possible, we chose epochs in the first sleep cycle, because
it was shown to contain higher pathological HFO rates.29
We selected epochs ≥2h away from focal or 6h from gen-
eralized seizures. Ripples (80– 250Hz) were analyzed in all
subjects. FRs (>250Hz) were analyzed in subjects whose
recordings had a sampling frequency greater than 1000Hz.
The detector is available at https://mni- open- ieega tlas.
resea rch.mcgill.ca/.12,29,30 It identifies increases in power
with respect to the background in narrow frequency bands,
with a duration longer than four oscillations plus the effec-
tive response time of the filters (equiripple finite impulse
response filters of order 508). Results of HFO analysis were
not used for clinical decision- making.
2.3
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Image coregistration and
localization of electrode contacts
Registration to stereotaxic space and anatomical localiza-
tion of electrode contacts and channels were performed as
done previously.22– 24 Using MINC tools (http://www.bic.
mni.mcgill.ca/Servi cesSo ftwar e/MINC) and the IBIS plat-
form, patient- specific peri- implantation and postsurgical
images were linearly registered to the preimplantation
magnetic resonance imaging (MRI), and electrode posi-
tions were marked in the coregistered images. A nonlin-
ear transformation from the preimplantation image to the
ICBM152 2009c template was obtained and applied to the
coordinates to represent them in the common MNI ster-
eotaxic space. We used the same 17 regions as the MNI
Open iEEG Atlas to correct for region- specific physiologi-
cal HFO rates.24
2.4
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Classification of channels
A bipolar channel was classified as resected if both contacts
were resected on the coregistered postresection image. To
account for sagging, coregistration error, and partial con-
tact resection, contacts in or in the close vicinity (<5mm)
of the cavity were considered resected.28,31 SOZ channels
were identified by consensus of two neurophysiologists
FIGURE Flowchart of the patient selection process. The reasons for exclusion merged in others are no report available (n=3), no
visible interictal electroencephalographic changes at seizure onset (n=1), and premature termination of stereoelectroencephalogram
(SEEG) due to self- removal of electrodes (n=1). In nine of the 151 included patients, we could only select 20min of non- rapid eye
movement (NREM) sleep containing one or more electrographic seizure. In five, we could only select 20min of NREM sleep <2h away
from a focal seizure. In one other patient, we could only select 10min of NREM sleep. We decided to include these patients to be as
generalizable as possible. The segments with seizures themselves were excluded. CHUGA, Grenoble- Alpes University Hospital; MNI,
Montreal Neurological Institute
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ZWEIPHENNING et al.
based on the first unequivocal visible signal changes at
seizure onset independent of frequency content.32
To determine where each bipolar channel was record-
ing from, we modeled each contact as a sphere of 10- mm
radius and computed the percentage of each Atlas' gray
matter region within this volume, assigning weights that
decreased with the square of the distance from the cen-
ter. We then averaged the percentages over the two con-
tacts, and used up to three regions showing the highest
percentages.
2.5
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Thresholds
We compared two thresholds for automatic HFO detec-
tion frequently used in the literature with three thresh-
olds based on the normative values of the MNI Open iEEG
Atlas.24 Studies in the literature frequently use (1) HFO
rate > 1/min5– 7,10 or (2) a majority threshold relative to
the total or maximum number of HFOs in a patient.8,9,33– 42
We used HFO rate > 1/min and above a threshold of 50%
relative to the total number of HFOs in a patient to com-
pare to our new HFO Atlas- based thresholds.
We computed three thresholds using the statistical
distribution of normative physiological HFO rates24: (1) a
global Atlas threshold, (2) a regional Atlas threshold, and
(3) a regional Atlas correction followed by a 10% thresh-
old (regional +10%). The global Atlas threshold was de-
fined as the 90th percentile value of the distribution of
normative HFO rates of all regions combined. This cutoff
is commonly used in biomedical statistics to objectively
set a threshold; using the 85th or 95th percentile did not
change our results (data not shown). We subtracted this
value from the automatically detected HFO rates. If the
subtraction resulted in a negative value, it was set to zero.
The regional Atlas threshold was defined as the region-
specific 90th percentile values of the normative rates. If a
bipolar channel was recording from more than one region,
the threshold was obtained by a weighted average of the
region- specific thresholds, the weight of each region being
determined by the percentage of each region contributing
to that channel. The regional+10% threshold was calcu-
lated by removing the channels that had ≤10% of the total
number of HFOs after regional correction. This was done
to eliminate channels only marginally above physiological
HFO levels; using a 5% or 15% cutoff did not change our
results (data not shown).
Six hundred seven bipolar channels (5% total ripple
channels) of 49 patients for ripples, and 299 bipolar chan-
nels (5% total FR channels) of 23 patients for FRs were
also part of the MNI Open iEEG Atlas. For these patients,
we recalculated the regional Atlas thresholds excluding
the values from that patient, and used these corrected
values for further calculation. The regional Atlas thresh-
old for every patient included in the Atlas is therefore in-
dependent of that patient's normative values.
2.6
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Surgical outcome
Outcome was determined according to the Engel classifi-
cation43 from the most recent follow- up ≥1year after sur-
gery, and dichotomized into seizure- free (Engel IA) and
non- seizure- free (Engel ≥IB).
2.7
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Statistical analyses
We tested for differences in demographic information and
group- level differences in event rates between resected and
nonresected channels of seizure- free and non- seizure- free
patients using Mann– Whitney U (MWU), chi- squared, or
Fisher exact tests depending on the type and distribution
of the variable.
To evaluate whether physiological HFO correction im-
proved identification of the epileptic focus, we compared
the HFO region to the resection cavity. We defined chan-
nels with HFOs above threshold that were or were not
resected as true positive (TP) or false positive (FP), and
channels without HFOs or with HFOs below threshold
that were or were not resected as false negative (FN) or
true negative (TN). We assessed the performance of ripples
and FRs above threshold to identify the resection cavity by
computing accuracy ([TP+TN]/[TP+TN+FP+FN]),
sensitivity (TP/[TP + FN]), specificity (TN/[TN+FP]),
positive predictive value (PPV; TP/[TP+FP]), and nega-
tive predictive value (NPV; TN/[TN+FN]). Because only
in Engel IA outcome is the epileptic focus entirely inside
the resection cavity, we separatly analyzed seizure- free
and non- seizure- free patients. We tested for differences in
performance measures between thresholds, by applying a
Wilcoxon signed- rank test to the performance measures
of all pairs of different thresholds within the two outcome
groups and corrected for multiple comparisons (false dis-
covery rate [FDR]<0.05 corrected for 50 comparisons:
10 threshold- pairs times five performance measures). We
tested for differences in performance measures between
seizure- free and non- seizure- free patients using an MWU
test (FDR < 0.05, five comparisons: five performance
measures). We expected that correcting for region- specific
rates of physiological ripples would increase specificity
and PPV for identification of the focus. We expected a
larger increase in PPV in seizure- free patients.
To evaluate whether physiological HFO correction im-
proved prediction of seizure- free outcome, we allowed
residual HFOs in a maximum of 5% of the nonresected
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ZWEIPHENNING et al.
channels. We used definitions analogous to the ones de-
scribed above, but now defined them at the patient level.
Patients without HFOs above threshold and good or poor
outcome were considered FN or FP, respectively. To test
for differences in performance measures between thresh-
olds, we applied a Cochran Q test to the proportions of
true and false predictions with different thresholds. If the
Cochran Q test was significant after correcting for multi-
ple comparisons (p<.05, five comparisons: five perfor-
mance measures), we performed post hoc McNemar tests
to identify which pairs of thresholds were significantly
different. We expected that correcting for region- specific
rates of physiological ripples would increase sensitivity
and NPV at the outcome level.
We expected greater improvement in performance in
patients with a focus in areas generating high rates of
physiological ripples according to the MNI Open iEEG
Atlas (namely occipital, sensorimotor, and mesiotemporal
regions, referred to as ripple- rich cortex) than in patients
with a focus in areas generating low rates of physiologi-
cal ripples (ripple- poor cortex).24 Therefore, we compared
tissue and outcome- level predictions between patients
with a focus in ripple- rich and ripple- poor cortex using
Cochran Q combined with McNemar and Fisher exact
tests corrected for multiple comparisons.
The current gold standard to define the area to resect is
the SOZ. As a last step, we compared the performance of
the best corrected HFO measure to the performance of the
SOZ on tissue and outcome level.
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RESULTS
The study sample consisted of 151 patients (Table S1).
Figure 2 displays the different thresholds examined and
the group- level results for ripples. It shows the ripple
rates in resected and nonresected channels of seizure-
free and non- seizure- free patients, and indicates the data
used when evaluating the different thresholds. In both
seizure- free and non- seizure- free patients, the major-
ity of channels with high ripple rates were resected. In
non- seizure- free patients there were more nonresected
channels with ripples above the 50% and regional+10%
threshold than in seizure- free patients. Table S2 provides
the median values and p- values.
3.1
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Ripples
3.1.1
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Identification of the resected tissue
The application of any threshold significantly improved
accuracy, specificity, and PPV to identify the resected
tissue compared to ripple rate > 1/min (Figure 3A, Table
1A). Using the regional+ 10% threshold resulted in the
highest accuracy, specificity, and PPV in both outcome
groups (seizure- free group: accuracy = 77.3%, specificity
= 97.1%, PPV = 80.6%; non- seizure- free group: accuracy
= 75.4%, specificity = 93.3%, PPV = 42.9%). In seizure- free
patients, this was a 1.1% increase in accuracy (p<.001),
and a 17.0% increase in PPV (p<.001) compared to the 50%
threshold (Table 1A). In non- seizure- free patients, this
corresponded to a 4.6% increase in specificity (p=.047),
and no difference in accuracy and PPV compared to the
50% threshold. Accuracy using ripple rate > 1/min, speci-
ficity using the 50% and regional+10% thresholds, and
PPV using all thresholds were higher in seizure- free com-
pared to non- seizure- free patients (Figure 3A).
There was a larger increase in PPV when moving from
the standard thresholds to the regional+ 10% threshold
in seizure- free patients, with a focus in ripple- rich (re-
gional+10% vs. >1/min: +62.2%, p=.005; regional+10%
vs. 50%: +40%, p = .015) rather than ripple- poor cortex
(regional + 10% vs. >1/min: +38.3%, p< .001; and re-
gional+10% vs. 50%: +8.3%, p=.014).
3.1.2
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Predicting Engel IA outcome
The application of any threshold significantly increased
the sensitivity, PPV, and NPV, and decreased the specific-
ity of ripples to predict seizure freedom compared to using
ripple rate > 1/min (Figure 3B1, Table S3A). Of the three
normalization thresholds, the regional + 10% thresh-
old resulted in the highest accuracy (69.5%), sensitivity
(58.6%), and NPV (74.7%), and the regional Atlas thresh-
old resulted in the highest specificity (84.9%) and PPV
(63.2%). Comparing the regional+10% to the 50% thresh-
old showed a trend toward higher sensitivity (+12.0%,
p=.05) and PPV (+1.0%, p=.05).
There was an upward trend in accuracy, sensitivity,
PPV, and NPV, and a downward trend in specificity when
moving from the standard to the regional+10% thresh-
old in patients with a focus in ripple- rich, but not in pa-
tients with a focus in ripple- poor cortex (Figure 3B2). In
patients with a focus in ripple- rich cortex, the sensitivity
and PPV using the regional+10% threshold were signifi-
cantly higher than with the 50% threshold (sensitivity =
+35.3%, p=.014; PPV = +5.3%, p=.014). In patients with
a focus in ripple- poor cortex, the 50% and regional+10%
thresholds resulted in comparable performance. Using the
regional+10% threshold, there was a trend toward higher
sensitivity and NPV to predict seizure freedom in patients
with a focus in ripple- rich than ripple- poor cortex (sensi-
tivity = 76.5% vs. 51.2%, p=.089; NPV = 86.7% vs. 69.2%,
p = .080). Figure 4shows an example of a seizure- free
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ZWEIPHENNING et al.
patient with an epileptic focus in ripple- rich cortex (A),
where normalization improves prediction, and a seizure-
free patient with an epileptic focus in ripple- poor cortex
(B), where the 50% and all normalization thresholds work.
3.1.3
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Patient level classification of
seizure outcome
Thirty- four of the 58 (58.6%) seizure- free patients were
correctly classified using the regional + 10% threshold.
Twelve of the 24 incorrectly classified seizure- free patients
had residual ripples in 6%– 10% of the nonresected chan-
nels, which was slightly more than the allowed 5% cutoff.
In all 12 patients, >50% of the channels identified by the
regional + 10% threshold were included in the resection.
All had the electrodes positioned relatively close together,
recording from brain tissue very close to the subsequently
resected tissue (Figure 5A). Ten of the 24 incorrectly classi-
fied seizure- free patients had a separate ripple focus distant
from the resection (Figure 5B). These secondary foci were
predominantly located in central, parietal, and occipital
areas. Eight of these 10 patients had an MRI abnormality
(n=6) or positron emission tomography (PET) hypome-
tabolism (n=2) in these regions. The remaining two incor-
rectly classified seizure- free patients had ripples above the
50% threshold in central or occipital areas that were not re-
sected; these ripples were below the region- specific normali-
zation threshold and therefore resulted in no HFOs using
the regional and regional+10% thresholds.
FIGURE Visualization of the different thresholds examined in this study. Violin plots show the raw ripple rates (A, C, E) and ripple
rates relative to a patient's total ripples (B, D) in resected (dark color) and nonresected (light color) tissue in seizure- free (SF; teal) and
non- seizure- free (nonSF; red) outcome patients. The colored blocks cover the data points used when evaluating the thresholds (indicated
with colored dashed lines) examined in this study: raw ripple rate > 1/min (A; dark blue), 50% of the patient's total ripple rate (B; light
blue), the global Atlas threshold (C; yellow), the regional Atlas threshold (E; green), and 10% of the patient's total ripple rate remaining after
regional Atlas correction (D; orange). The global Atlas threshold is calculated as the 90th percentile value of the normative rates of all 17
high- frequency oscillation (HFO) Atlas regions combined. The regional Atlas threshold is calculated as the weighted average of the region-
specific 90th percentile value of the normative rates obtained from the Montreal Neurological Institute Open iEEG Atlas Project from which
an electrode channel is recording. The 17 HFO Atlas regions are indicated on the x- axis in E. SOZ, seizure onset zone
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ZWEIPHENNING et al.
FIGURE Performance of (normalized) ripples, fast ripple (FR) rate of >1/min, and the seizure onset zone (SOZ) to identify the
resected tissue (A) or to predict seizure freedom (B). (A) Violin plots of the accuracy (1), specificity (2), and positive predictive value (PPV;
3) of ripples above different thresholds and the SOZ (x- axis) to delineate the resected tissue in seizure- free (teal) and non- seizure- free (red)
patients. The dots represent the performance values of the individual patients. The black dots in the thicker blue or red lines show the
median and interquartile range. Significant differences in performance values between the thresholds within the seizure- free or non- seizure-
free outcome groups are indicated with teal or red significance bars. Significant differences in performance values between seizure- free and
non- seizure- free patients are indicated with black significance bars. The number of filled circles indicates the level of significance: one = p<.05,
two = p<.01, and three = p<.001. A tilde indicates a trend: p=.05– .1. (B) Line plot indicating the change in accuracy (red), sensitivity
(blue), specificity (green), PPV (purple), and negative predictive value (NPV; orange) of ripples with increasingly sophisticated degrees of
thresholding and the SOZ (x- axis) to predict seizure- free outcome for all patients (1), and separately for patients with a focus in ripple- rich
(2A) and ripple- poor (2B) cortex. Significant differences in performance values between thresholds are indicated with colored significance
bars. The number of filled circles indicates the level of significance: one = p<.05, two = p<.01, and three = p<.001. A tilde indicates a
trend: p=.05– .1
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ZWEIPHENNING et al.
TABLE Median values of performance measures of ripples above the different thresholds, FR rate > 1/min (B), or SOZ (C) for predicting the resected tissue in seizure- free and non-
seizure- free patients, overall and grouped by location of the epileptogenic focus in ripple- rich versus ripple- poor cortex
Performance
measure Subgroup
Accuracy Sensitivity Specificity PPV NPV
SF nSF p, SF vs. nSF SF nSF p, SF vs. nSF SF nSF p, SF vs. nSF SF nSF p, SF vs. nSF SF nSF p, SF vs. nSF
Ripples above the different thresholds
Rate>1/min All 56.8 48.1 .043a85.2 83.3 .714 47.7 39.2 .077b37.3 25.8 .016a91.1 89.5 .958
RRC 54.0 47.8 .187 65.2 80.0 .907 52.6 43.0 .317 37.8 22.7 .007a78.6 90.5 .140
RPC 57.7 48.7 .042a90.0 87.7 .919 45.5 39.0 .138 36.7 27.9 .339 92.0 88.9 .561
50% All 76.2 74.3 .355 27.3 24.2 .682 94.3 88.7 .002a63.6 41.2 .002a77.5 81.0 .958
RRC 72.5 76.2 .067b20.0 25.0 .907 94.5 91.9 .083b60.0 40.0 .023a70.3 83.0 .012a
RPC 78.8 71.7 .042a29.4 23.7 .216 94.1 86.9 .008a66.7 42.0 .017a82.7 79.4 .622
Global All 77.0 75.5 .355 25.7 20.0 .099b95.5 92.1 .063b62.8 41.7 .004a78.9 81.8 .958
RRC 72.1 77.8 .129 21.4 20.0 .907 94.6 93.1 .317 66.7 46.7 .047a75.8 83.7 .012a
RPC 78.0 72.5 .042a29.4 19.1 .119 95.9 91.5 .087b60.0 33.3 .023a82.6 78.3 .484
Regional All 77.3 71.6 .287 36.1 27.8 .114 92.6 87.9 .063b66.7 40.0 <.001a80.6 82.0 .958
RRC 73.5 72.0 .242 16.7 20.0 .907 96.4 92.9 .275 66.7 43.8 .002a74.6 85.0 .021a
RPC 81.2 70.0 .042a43.8 33.3 .147 90.9 84.8 .044a65.2 40.0 .002a86.7 80.6 .484
Regional+10% All 77.3 75.4 .355 27.0 17.1 .079b97.1 93.3 .004a80.6 42.9 <.001a78.2 80.0 .958
RRC 73.0 77.6 .129 13.0 16.7 .907 100 96.6 .083b100 33.3 <.001a73.2 83.3 .016a
RPC 80.0 73.4 .042a30.0 23.2 .109 96.6 92.4 .011a75.0 42.9 .002a84.3 78.4 .484
FRs>1/min All 79.6 77.9 .680 21.4 10.9 .154 97.4 95.4 .124 66.7 39.4 .02a81.1 82.8 .958
RRC 69.9 78.3 .587 16.6 16.0 .824 96.5 98.6 .868 50.0 64.6 .907 69.9 80.3 .682
RPC 79.4 79.4 .903 11.1 11.1 .756 96.4 94.4 .551 40.0 40.0 .891 81.8 85.7 .889
SOZ All 78.9 74.5 .091b51.5 58.3 .318 93.4 87.2 .003a77.8 53.8 <.001a83.2 87.9 .958
RRC 71.7 77.6 .822 30.0 40.0 .178 94.9 88.5 .167 80.0 56.3 .056b75.8 87.8 .008a
RPC 82.9 73.1 .017a59.1 69.8 .756 92.6 81.5 .029a77.8 50.0 .019a89.4 88.6 .889
Abbreviations: FDR, false discovery rate; FR, fast ripple; NPV, negative predictive value; nSF, non- seizure- free; PPV, positive predictive value; RPC, ripple- poor cortex; RRC, ripple- rich cortex; SF, seizure- free; SOZ,
seizure onset zone.
aSignificant difference between the SF and nSF groups: Benjamini– Hochberg FDR- corrected p- value <.05.
bTrend toward a significant difference between the SF and nSF groups: Benjamini– Hochberg FDR- corrected p- value between .05 and .1.
|
9
ZWEIPHENNING et al.
3.2
|
Fast ripples
There was no clinically relevant difference in any perfor-
mance measure of FRs to identify the epileptic focus or
predict seizure freedom using FR rate > 1/min, the 50%
threshold, or any of the three proposed normalization
thresholds (Figures S1 and S2).
3.3
|
Comparing the performance of the
best ripple threshold to FR rate > 1/
min and the SOZ
Four of the 151 (2.6%) patients analyzed for ripples did
not show ripples above the regional + 10% threshold;
FRs>1/min were not detected in eight of the 83 (9.6%)
patients analyzed for FRs.
Comparing the performance of the regional + 10%
threshold to that of FR rate > 1/min to identify the epi-
leptic focus, we found no significant differences (Figure
3A, Table 1B). Comparing the regional +10% threshold
to the SOZ, we found a higher PPV in non- seizure- free
patients (+13.8, p=.05) and lower specificity (seizure-
free, −3.7; non- seizure- free, −6.1; p< .001) of the SOZ.
This shows that the regional+10% threshold is a better
marker for the epileptic focus than the SOZ. The higher
sensitivity (seizure- free, +24.5; non- seizure- free, +41.2;
p<.001) and NPV (seizure- free, +5.0; non- seizure- free,
+7.9; p<.001) of the SOZ compared to the regional+10%
threshold is likely explained by the SOZ being used to tai-
lor the surgical resection (Table 1C).
The regional + 10% threshold outperformed FR rate
> 1/min and the SOZ for outcome prediction, especially
in patients with a focus in ripple- rich cortex (Figure 3B,
Table S3).
4
|
DISCUSSION
In this large bicentric study of 151 patients, we demon-
strated that HFO normalization based on region- specific
physiological HFO rates improves epileptic focus identi-
fication and prediction of surgical outcome. We showed
that (1) normalization significantly improved the ability of
FIGURE Patient examples of a case with an epileptic focus in ripple- rich cortex where normalization improves prediction (A), and
a case with an epileptic focus in ripple- poor cortex where the 50% and all normalization thresholds work (B). Both patients had Engel IA
outcome. We used Epitools50 to map the electrodes in the patient's brain surface as dark blue cylinders and indicated which channels were
above the cutoff threshold of 1 ripple/min with small dark blue spheres. The four columns indicate channels that had ripples above the
50% (light blue), global Atlas (yellow), regional Atlas (green), and regional+10% Atlas (orange) thresholds. The black dots indicate the
resection. (A) This patient benefitted from regional thresholding and shifted from false negative to true positive classification. The 50%
threshold identified four channels inside, and five channels outside the resection: one in the posterior hippocampus, and four in the medial
parieto- occipital areas. The global Atlas threshold identified three channels inside, and three channels outside the resection: three in the
medial parieto- occipital areas. The regional Atlas and regional+10% threshold identified two channels inside the resection. The residual
ripples using the 50% and global Atlas thresholds surpassed the allowed 5% residual high- frequency oscillations, hence the false negative
classification. (B) This patient was classified as false negative using ripple rate > 1/min and as true positive using all other thresholds:
channels with ripples above the 50%, global Atlas, regional Atlas, and regional+10% Atlas thresholds were all inside the right orbitofrontal
resection
10
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ZWEIPHENNING et al.
ripples to identify the resected tissue and predict seizure
freedom, with the regional+10% threshold exhibiting the
best performance; (2) normalization is particularly useful
in patients with a focus in cortex with high physiological
ripple rates; in this condition, ripple normalization even
outperformed FR rate > 1/min and the SOZ, which re-
quires many days of monitoring; and (3) normalization
did not improve the performance of FRs for focus identifi-
cation or oucome prediction.
4.1
|
Normalization improves epileptic
focus identification
We found a higher accuracy, specificity, and PPV, but
a lower sensitivity and NPV compared to Lachner- Piza
and colleagues and to the tissue- level performance re-
sults from Fedele and colleagues.9,42 Normalization of
ripples not only performed better than standard thresh-
olds applied to ripples alone, but also outperformed
the combinations of ripples and spikes,42 or ripples and
FRs9 reported in the literature. These latter methods are
designed to differentiate pathological from physiologi-
cal ripples and increase the specificity of ripples for the
epileptic tissue.
Higher specificity and PPV confirmed our hypothesis
that correcting for region- specific rates of physiological
ripples improves localization of the epileptic focus com-
pared to standard thresholds applied in HFO research and
compared to the SOZ as current gold standard measure.
We found a larger increase in PPV in seizure- free than
non- seizure- free patients, and a larger increase in PPV in
seizure- free patients with a focus in ripple- rich compared
to ripple- poor cortex. This indicates that our method cor-
rected for physiological ripples and did not erroneously
remove ripples in the nonresected part of the presumed
epileptogenic region in non- seizure- free patients. Our re-
sults also confirm the co- occurrence of physiological and
pathological ripples in the same tissue.19 If diseased tis-
sue were not able to generate physiological ripples, our
subtraction method would result in poorer performance
in patients with a focus in ripple- rich cortex.
FIGURE Patient examples of a case where the 5% tolerance for residual high- frequency oscillations (HFOs) may be too strict (A), and
a case where the physiological ripple correction may be insufficient (B). Both patients had Engel IA outcome. We used Epitools50 to map
the electrodes in the patient's brain surface as dark blue cylinders and indicated which channels were above the cutoff of 1 ripple/min with
small dark blue spheres. The four columns indicate channels that had ripples above the 50% (light blue), global Atlas (yellow), regional Atlas
(green), and regional+10% Atlas (orange) thresholds. The black dots indicate the resection cavity. (A) All thresholds identify the same
brain area, but only using the 50% threshold, this patient was classified as true positive, because there were residual HFOs in >5% of the
70 nonresected channels using the other thresholds. These channels with residual HFOs were located in the close vicinity of the resection
cavity. (B) This patient was classified as false negative using all thresholds and had <50% of the channels above all thresholds resected. All
thresholds correctly identified the right hippocampus, but also showed a parietocentral and occipital ripple focus. This patient's magnetic
resonance imaging showed a right mesiotemporal sclerosis, a small area of gliosis and encephalomalacia/ulegyria in the inferior aspect of
the left occipital pole, and a T2signal abnormality, possibly gliosis or dysplasia, in the left centroparietal region. The parietocentral and
occipital ripple foci may be secondary foci that are responsive to antiseizure medication; alternatively, these may correspond to physiological
ripples, indicating an insufficient correction
|
11
ZWEIPHENNING et al.
The low sensitivity and NPV are likely explained by
the resected tissue also containing nonpathological tis-
sue. Reanalyzing our data using the overlap between re-
sected and SOZ channels, for a more strict definition of
the epileptogenic tissue, resulted in higher sensitivity and
NPV (sensitivity = 44.9% vs. 27%, NPV = 93.9% vs. 78.2%;
data not shown), values similar to those reported in the
literature.
4.2
|
Normalization improves
prediction of Engel IA outcome
We found higher accuracy, sensitivity, and NPV, but
lower PPV and lower or higher specificity comparing
our outcome level results to the results of the long- term
recording subgroup of Jacobs and colleagues, and the
ripple results of Fedele and colleagues.8,9 Higher sen-
sitivity and NPV confirmed our hypothesis that cor-
recting for region- specific rates of physiological ripples
improves prediction of seizure outcome compared to
standard thresholds. Our results showed that all patients
showed significant improvements in the performance
of ripples to predict seizure freedom using any thresh-
old compared to ripple rate > 1/min, but only patients
with a focus in ripple- rich cortex showed significant
improvements in sensitivity and PPV when compar-
ing the regional+10% and 50% thresholds. It was also
supported by the trend toward higher sensitivity and
NPV in patients with a focus in ripple- rich compared
to ripple- poor cortex when using the regional+ 10%
threshold. In patients with a focus in ripple- rich cortex,
the regional + 10% threshold even outperformed the
SOZ. Using a similar approach, Kuroda and colleagues
recently showed that the prediction of postoperative
seizure outcomes can be optimized with the considera-
tion of normalized HFOs.44
Apart from the difference in threshold to define an
HFO channel, other methodological differences were
the definition of good outcome (Engel I vs. IA), and the
threshold defining the number or fraction of HFOs need-
ing to be resected for a good outcome patient to be consid-
ered TP.
4.3
|
Normalization does not improve FR
performance
Physiological FRs are rare, even in eloquent cortical
areas.24 Also, FRs are usually recorded in only a subset
of patients,6,7,10,45 but when present they are very specific
for epileptogenic tissue, and residual FRs after resection
are tightly linked to seizure recurrence.5– 7,9,10 We found
a high percentage of nonresected channels without FRs
(specificity), a high percentage of channels with FRs that
were resected (PPV) at the tissue level, and a high per-
centage of patients with residual FRs who had recurrent
seizures (NPV) at the outcome level. As expected, because
physiological FRs are rare, we found no clinically relevant
changes in these performance measures upon application
of the different thresholds. Interestingly, ripples above
the regional+10% threshold performed similarly to FRs
in terms of focus identification, but outperformed FRs in
outcome prediction.
4.4
|
Limitations
As prior work showed that it is not necessary to remove
all channels with HFOs to achieve seizure freedom, we
allowed residual HFOs in 5% of the nonresected chan-
nels.8,40,46 We defined this threshold as a fraction of the
total number of nonresected channels instead of a rate
threshold to make it independent from the proportion
of electrodes covering the epileptogenic tissue. By doing
so, we also compensated for cases with large resec-
tions where the few identified HFO channels were re-
sected by chance. However, our results showed that this
5% threshold may be too strict in patients with tightly
grouped electrodes (Figure 5A). The nonresected elec-
trodes close to the cavity, which showed likely patho-
logical ripples in these seizure- free patients, may be
functionally deactivated, because they become discon-
nected even if not resected.4
In addition, 10 of the 24 incorrectly classified seizure-
free patients had a clear, separate ripple focus distant from
the resection cavity (Figure 5B). These secondary foci were
predominantly located in central, parietal, and occipital
areas, regions less densely sampled in the MNI Open iEEG
Atlas; hence, the 90th percentile value may be suboptimal.
However, eight of these 10 patients showed either an MRI
abnormality or a PET hypometabolism, so it may be that
our method is correct and seizures originating from these
foci are responsive to antiepileptic drugs. Whether an ex-
tension of the atlas will eventually solve this issue awaits
future research.
Lastly, we analyzed HFOs in 20- min NREM sleep
epochs. We chose this state of vigilance, and if possible
the first sleep cycle, as it was shown to have the highest
HFO rates,25– 27 with the optimal pathological to phys-
iological HFO ratio,29 and hence, best identifies the
epileptic focus in the interictal EEG.28 However, in con-
trast to what was previously suggested,25,47 some recent
studies demonstrated that in some patients HFO anal-
ysis from a short segment of NREM sleep might not be
representative of the total HFO distribution over longer
12
|
ZWEIPHENNING et al.
periods, and hence, analysis of prolonged durations is
warranted.28,48,49
5
|
CONCLUSIONS
This large bicentric study proposes a solution to one of
the key problems that hamper the integration of HFOs
into clinical practice: differentiating physiological from
pathological HFOs. Correcting for region- specific nor-
mative rates of physiological ripples improves epileptic
focus identification and prediction of seizure freedom
compared to using standard HFO measures alone.
Ripple normalization is particularly useful in patients
with an epileptic focus in ripple- rich cortex. In this con-
dition, it even outperformed FR rate > 1/min and the
SOZ, the traditional gold standard for defining the epi-
leptic focus. We found no relevant improvement using
HFO normalization for the performance of FRs, which
supports the general view that FRs are closely related
to epileptogenicity. Future research should compare
normalized ripples to other epilepsy markers in a mul-
timarker approach on the same dataset to improve our
definition of the area to be resected and ultimately epi-
lepsy outcome.
ACKNOWLEDGMENTS
The authors wish to express their gratitude to the staff
and technicians at the EEG Department of the Montreal
Neurological Institute and Hospital, Lorraine Allard,
Nicole Drouin, and Chantal Lessard. The authors wish
also to express their gratitude to the staff and techni-
cians at the Neurophysiopathology Laboratory of
Grenoble- Alpes University Hospital, Patricia Boschetti,
and Marie- Pierre Noto. This work was supported by
the Canadian Institute of Health Research (grant FDN-
143208 to J.G.) and a donation of the Hewitt Foundation
(to B.F.). B.F.'s salary was supported by a salary award
(Chercheur- boursier clinicien Junior 2) for 2018–
2021 from the Fonds de Recherche du Québec– Santé.
The research stay of B.F. at Dr. Kahane's laboratory
at CHUGA for project planning and local data collec-
tion was supported by a CIC Brain and Mental Health
Chair award from the Neurodis Foundation (to B.F.).
W.J.E.M.Z. received research scholarships from the
Foundations Jo Kolk Studiefonds and De Drie Lichten
in the Netherlands.
CONFLICT OF INTEREST
None of the authors has any conflict of interest to disclose.
We confirm that we have read the Journal's position on
issues involved in ethical publication and affirm that this
report is consistent with those guidelines.
ORCID
Willemiek J. E. M. Zweiphenning https://orcid.
org/0000-0002-0720-7878
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SUPPORTING INFORMATION
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How to cite this article: Zweiphenning WJEM,
von Ellenrieder N, Dubeau F, Martineau L, Minotti
L, Hall JA, et al. Correcting for physiological ripples
improves epileptic focus identification and outcome
prediction. Epilepsia. 2021;00:1– 14. https://doi.
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