METHODS
published: 20 May 2021
doi: 10.3389/fnhum.2021.642819

Clinical Validation of the Champagne
Algorithm for Epilepsy Spike
Localization
Chang Cai 1,2*, Jessie Chen 2 , Anne M. Findlay 2 , Danielle Mizuiri 2 , Kensuke Sekihara 3,4 ,Heidi E. Kirsch 2,5* and Srikantan S. Nagarajan 1*
1

National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China, 2 Department ofRadiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, 3 Departmentof Advanced Technology in Medicine, Tokyo Medical and Dental University, Tokyo, Japan, 4 Signal Analysis Inc., Hachioji,
Japan, 5 Department of Neurology, University of California, San Francisco, San Francisco, CA, United States

Edited by:
Stefan Rampp,
University Hospital Erlangen, Germany
Reviewed by:
Evelien Carrette,
Ghent University, Belgium
Giovanni Pellegrino,
McGill University, Canada*Correspondence:
Chang Cai
cchangenergy@gmail.com
Heidi E. Kirsch
heidi.kirsch@ucsf.edu
Srikantan S. Nagarajan
sri@ucsf.edu
Specialty section:
This article was submitted toBrain Imaging and Stimulation,a section of the journal
Frontiers in Human Neuroscience
Received: 16 December 2020
Accepted: 22 March 2021
Published: 20 May 2021
Citation:
Cai C, Chen J, Findlay AM, Mizuiri D,
Sekihara K, Kirsch HE andNagarajan SS (2021) Clinical Validationof the Champagne Algorithm for
Epilepsy Spike Localization.
Front. Hum. Neurosci. 15:642819.
doi: 10.3389/fnhum.2021.642819

Magnetoencephalography (MEG) is increasingly used for presurgical planning in people
with medically refractory focal epilepsy. Localization of interictal epileptiform activity, asurrogate for the seizure onset zone whose removal may prevent seizures, is challenging
and depends on the use of multiple complementary techniques. Accurate and reliablelocalization of epileptiform activity from spontaneous MEG data has been an elusive goal.
One approach toward this goal is to use a novel Bayesian inference algorithm—theChampagne algorithm with noise learning—which has shown tremendous success insource reconstruction, especially for focal brain sources. In this study, we localized
sources of manually identified MEG spikes using the Champagne algorithm in acohort of 16 patients with medically refractory epilepsy collected in two consecutive
series. To evaluate the reliability of this approach, we compared the performance toequivalent current dipole (ECD) modeling, a conventional source localization techniquethat is commonly used in clinical practice. Results suggest that Champagne may
be a robust, automated, alternative to manual parametric dipole fitting methods for
localization of interictal MEG spikes, in addition to its previously described clinical andresearch applications.
Keywords: source localization, epilepsy, source imaging analysis, magnetoencephalography, spike analysis, brain
source imaging, brain source localization

1. INTRODUCTION
Epilepsy is a neurological disorder that poses an important challenge due to its prevalence, as
it affects about 0.8–1% of the world population (Organization et al., 2005; Moshé et al., 2015).
While typical pharmacological treatments work to control seizures for the majority of people withepilepsy, roughly one-third suffer from drug-resistant epilepsy (Lamberink et al., 2017), leaving
surgery as a possible option for treatment (Ryvlin et al., 2014) for those in whom the seizure onset
zone (SOZ), where seizures arise, can be identified (Zijlmans et al., 2019). Magnetoencephalography(MEG) is increasingly used for presurgical planning in people with focal onset epilepsy refractory
to pharmacotherapy (Koster et al., 2020). MEG uses recordings of minute extra-cranial magnetic
fields produced by cortical activity, and is thereby able to display changes in brain state with atime resolution below 1 ms. Sources of such activity can be localized with an accuracy of several
millimeters (Wheless et al., 2004). Compared to other existing techniques, MEG has the advantage

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2. MATERIALS AND METHODS

of being completely non-invasive, and a wide range of highresolution analysis techniques may be applied to broadband
MEG datasets.
Like EEG, MEG can record both ictal and interictal data
but because patients are commonly studied in the MEG lab as
outpatients for only a limited amount of time, it is relatively rare
to capture a seizure, and most commonly, interictal epileptiformdischarges (spikes and sharp waves) are captured and source
modeling is carried out. The most widely used technique for
localizing interictal epileptiform discharges is a two-step process:(1) visual detection and identification of spikes in the raw
MEG. followed by (2) calculation of the location and orientationof equivalent current dipoles (ECD) that best account for theobserved magnetic field and review of the resulting fits (Vrbaand Robinson, 2001; Wheless et al., 2004). This method has
been proven to provide reliable and useful information forpresurgical planning by several studies (RamachandranNair et al.,2007) that also concluded that invasive intracranial recording
techniques might eventually be replaced by MEG (Knowlton
et al., 2006). Such procedures require manual review of ECD
results, which can be time-consuming, requires trained andexperienced scorers, and contains a considerable amount ofsubjectivity in the results. Additionally, users of ECD methods
typically attempt to represent activity at only one time point
associated with a spike, and so such models are inherently limitedin their ability to capture activity that represents the entire timecourse of such activity.
As a complement to ECD methods, our lab developed and uses
a robust brain source imaging algorithm, Champagne, derived
based on an empirical Bayesian inference and incorporating
deep theoretical ideas about sparse-source recovery fromnoisy, constrained measurements. Champagne improves sourcereconstruction performance evaluated using reconstruction
accuracy, robustness, and computational efficiency measures(Wipf et al., 2010). Experiments with simulated and real data
have shown that Champagne is robust for multiple complex
brain sources even under high interference from correlated
sources or with noisy data (Owen et al., 2012). The latest
extension of the Champagne algorithm incorporating noise
learning is able to reconstruct complex focal source activity
when corrupted by high levels of noise and interference,
while maintaining its typical high performance features (Caiet al., 2020), without the need for baseline or control data.
Champagne with noise learning is thus especially suitablefor uses like the localization of interictal epileptiform spikes,
where there is a focal brain source and high levels of noise
may co-exist.
Here, we evaluate a source localization pipeline foranalysis of manually identified epileptiform discharges
using the Champagne algorithm with noise learning.
Once selected, this pipeline provides a fully automatedreconstruction of the source of these interictal events.
Our study demonstrates the validity of this approachin a cohort of 16 patients with focal epilepsy andcompares it with an established clinical source localization
technique—parametric dipole fitting—to assess its practicality
and utility.

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2.1. Patients
In this paper, we chose 16 patients with medically refractory focal
epilepsy, one consecutive group from 2015 and one consecutive
group from 2019, who had MEG at the UCSF Biomagnetic
Imaging Laboratory and met the following inclusion criteria: allpatients were referred for MEG at UCSF as part of pre-surgicalplanning. UCSF Committee on Human Research approved allprocedures. All study procedures were conducted based on the
Declaration of Helsinki. Since many patients were either externalreferrals or recently scanned, therefore long-term outcome data
was not available for them. Participants gave their written
informed consent to participate in the study, or in some cases
the need for consent was waived for the purpose of retrospective
analysis by the UCSF Committee on Human Research.

2.2. Structural Images
All patients had magnetic resonance imaging (MRI) performed
at 1.5 or 3 T that was used for visualization of source localization
results. This study used the protocol including the following
sequences: (1) a T1-weighted, 3D spoiled gradient-recalled echo
in a steady state sequence with TR 34 ms, TE 3–8 ms, flip angle
30◦ ; (2) a T2-weighted 3D fast-spin echo sequence with TR 3000
ms, TE 105 ms. Both sequences had the following detail features:
slice thickness of 1.5 mm, matrix 256 × 256 × (108 − 140), and afield view of 260 × 260 mm with skin-to-skin coverage including
the nasion and preauricular points.

2.3. Recordings
All MEG recordings were collected when the participants werelying with their eyes closed in a magnetically shielded room.
Between 20 and 60 min of continuous, interictal, resting state
MEG were recorded with a 275 channel whole-head CTF Omega
2000 system (VSM MedTech, Coquitlam, BC, Canada), usinga passband of DC to 70 Hz and a sampling rate of 1,200 Hz,then downsampling to 600 Hz. In addition, concurrent scalp
electroencephalographic (EEG) data were recorded using 19
leads placed according to the international 10–20 system, alongwith an electrocardiogram channel and eye channels to assist in
the identification of artifacts. Some patients were sleep deprived
and reached various stages of sleep during the recordings.

2.4. Signal Analyses
A single-sphere (for ECD) and a multi-sphere (for Champagne
algorithm) head model were created for each patient, basedon the structural MRI images. Co-alignment of structural andfunctional images was achieved by marking three prominent
anatomical points (nasion and preauricular points) of thesubject’s head on the MRI images and localizing three magnetic
fiducials attached to the same points. All recordings werereviewed by experienced MEG technologists (MM, AF, DM)
and clinical neurophysiologists and epileptologists (HEK, HC),and the peak of all epileptiform spikes was marked manuallybased on both the MEG and EEG recordings. In addition to
the continuous recordings, spike-locked datasets were createdstarting 1 s before and ending 1 s after each spike and corrected
for baseline-offset using the first 700 ms. Spikes <1 s after2

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Validation of Automated Spike Localizationa previous spike were excluded. An equivalent current dipole
model was fitted to the magnetic field recorded with the entire
MEG sensor array during each single spike (ECDss). For each
spike, the sampling point was used that yielded the model withthe smallest residual variance. As a general rule, spikes with
dipoles having > 10% residual variance (aka error) were rejected.
Spike topography, moment strength, and orientation were also
considered for selection of ECDss. All source localizations weredone using software provided by VSM MedTech (Coquitlam, BC,
Canada). For the pipeline based on the Champagne algorithm
proposed here, we first remove the DC offset and band-pass filter
the time course for each subject with at 1–45 Hz, then we set
the time window for each spike as 25 ms before and 75 ms after
the spike peak. To run more complex spike localizations, we
also concatenate all spikes then reconstruct the brain sources byrunning Champagne directly.

FIGURE 1 | A representative “straightforward” case comparing ECDss andChampagne algorithms for spike localization (Subject 13). The top row shows
ECDss fits with errors <10% (yellow dots); the bottom row shows Champagne
localization for the same spikes (blue dots).

2.5. Performance Validation
First, we evaluate the performance of Champagne for 14“straightforward” cases, defined as those having spikes with′localization error < 10% using ECDss. Hit rate and A values arederived using FROC analysis as previously described (Owen et al.,2012; Sekihara, 2016; Cai et al., 2018, 2019).
Since the Champagne algorithm is a focal and sparse
distributed reconstruction algorithm that estimates source
amplitude for all voxels in the brain, to evaluate algorithm
performance we use robust thresholding methods as found inprior simulations (Cai et al., 2020). We define a successful
localization as one that meets the following criteria: we identify
active sources estimated as those brain sources whose amplitude
exceeds 1% of the largest amplitude of the estimation. This
threshold ensures that only significant estimated sources wereselected. Amongst all estimated sources, success of a localization
requires that there should be at least one estimated source
location within 2 cm of the limits of the source location estimatedby ECDss (with localization error < 10%).
Second, we evaluate the performance of Champagne
algorithm for spikes where data were “not straightforward.” For
this, we looked at two patients who had some spikes that could
be modeled with ECDss with acceptably low error (< 10%)
to a consistent cluster of plausible sources that also matchedclinical data (eg seizure semiology, ictal EEG), but ALSO had
other spikes with similar waveforms in sensor space (and on
simultaneous EEG) but with errors > 25% when modeled using
ECDss. We defined “ground truth” in this scenario as the averageof the coordinates of the ECDss localization that had errors< 10%. For the spikes with errors > 25% by ECDss, we applied
the Champagne algorithm to generate source models. Then wecompared the Euclidean distance between the ground truth andthe location of these “high-error spikes” (i.e., those with ECDss
localization error > 25%) as estimated by ECDss and thenby Champagne.
Finally, we evaluated the performance of the Champagne
algorithm for the localization of more complex spikes. Here, we
ran Champagne on data from one patient with spikes localizing
to two clusters of ECDss locations. We first concatenated

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all spikes, then localized this interictal epileptiform activity
using Champagne.
Please see Supplementary Table 1 for clinical information
including MRI, PET, SPECT, and video-EEG results.

3. RESULTS
To evaluate the performance of Champagne for “straightforward”
cases, we chose spikes with ECDss localization errors <10% from
14 people with intractable epilepsy. Figure 1 shows these spike
localization using ECDss, and localizations for the same spikes
using the Champagne algorithm, for a representative case. As isshown, Champagne with noise learning yields similar results.
Table 1 lists number of spikes recorded for each of the
14 subjects in the “straightforward” group, with the ability ofChampagne as compared to ECDss to localize the same spikes′in 14 subjects, including hit rate and A metric calculated as
described previously (Owen et al., 2012; Sekihara, 2016; Cai et al.,2018, 2019). Figure 2 illustrates this performance of Champagne
across this group of 14 people with intractable epilepsy. Note that′the mean A metric across all 14 subjects is 0.9, suggesting thatthe localization provided by the Champagne algorithm is similar′
to that of ECDss (The A metric is > 0.75% in one subject because
we recorded only five spikes during this study).
To evaluate the performance of our newer pipeline for spikes
that are “not straightforward,” we ran the Champagne algorithm
on spikes where ECDss fitting had yielded errors > 25% indata from two patients (NB one of these two patients had
several distinct spike populations/morphologies, one of which
was described, analyzed and included in the previous groupof 14 “straightforward”). Note that these were spikes where
the morphology on EEG and MEG suggested that all spikes(those that modeled with higher vs. lower error using ECDss)might arise from the same source. Here, we defined as groundtruth the set of source localizations with low error (< 10%)
using ECDss on the majority of spikes from the same patients.

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TABLE 1 | Overview of source localizations of interictal epileptiform activity recorded from 14 patients.
ID

1

2

3

4

5

6

7

8

9

10

11

12

13

14

Num of spikes

36

18

21

5

6

27

9

25

16

10

15

11

21

22

Hit ratio

0.61110.72220.8571

0.4

0.66670.88890.77780.84

0.93751

10.9091

10.8636A′ metric

0.78550.84160.90720.68130.81420.93260.86750.89870.93170.9746

0.96940.93830.98340.9032

FIGURE 2 | Performance of Champagne for 14 subjects with spikes where′
ECDss error <10%. The violin plot shows the distribution of A metric acrossall subjects; each dot represents data from one person. Note that the white
point represents the mean, the box subtends the middle quartiles and thewhiskers the outer deciles.

Figure 3 shows the localization result of more than 49 “notstraightforward” spikes using ECDss and Champagne algorithms(A). The localization results of ECDss with error > 25% are
distributed over whole brain (B), while the localization resultsof Champagne are more focal (C). Distance between the groundtruth and the ECDss result is 4 cm, much higher than that ofChampagne 3 cm. Figure 4 shows similar data for another patientwith 38 spikes (errors > 25%).
Figure 5 shows the localization results of one more complex
scenario, when spikes localize to clusters of dipoles. Here, we firstran ECDss in an additional patient. We show the localization
results in the first row of Figure 5. We then concatenated all
spikes and localized them using Champagne. As shown from thefirst row of Figure 5, with ECDss localizations are classified into
two clusters. The second and third rows present the Champagne
localization results. We can see that Champagne is able to localize
these two clusters of interictal epileptiform activity.

FIGURE 3 | Localization of high-error spikes by ECDss and Champagne
algorithms: Subject 1. (A) Ground truth as defined by localizations of spikes by
ECDss with errors <10%. (B) Localizations of spikes by ECDss with error>25% (C) Localization results when spikes with error >25% by ECDss werethen localized using the Champagne algorithm. (D) Distance between the
ground truth (A) and the localization result by ECDss (B) and Champagne (C)for those spikes with ECDss error >25%. For (D), whiskers indicate SEM.

oscillations may supplant its role (Frauscher et al., 2017), it is themost well-established, familiar, and widely used. The standard
method of source localization is the localization in individualinterictal epileptic spikes using dipole fitting (ECDss) (Bagicet al., 2011). However, this technique is operator-dependentand time-consuming. Dipole fitting procedures often do failunder both low and high SNR regimes, and when spikes do
not show a clear dipolar topography, and has shown to haveweak localization accuracy (Kobayashi et al., 2005; Fujiwara et al.,2012).
To explore the performance of Champagne algorithmwith noise learning for clinical use in the localization ofinterictal epileptiform discharges—“spikes”—we compared its
performance against ECDss in three common scenarios.
First, for “straightforward” cases, where ECDss solutions had
low error, Champagne with noise learning yields similar results′
and the mean A metric across all 14 subjects is around 0.9. Thisillustrates that Champagne can perform equivalently to ECDss inthis common use scenario.

4. DISCUSSION
Over the past three decades, whole-head MEG has continued toprove its value as an important tool for the evaluation of people
with medically refractory epilepsy. As a key part of the presurgicalworkup, it can assist in the identification of the SOZ by
providing corroboration of hypotheses and enabling resection or
implantation of electrodes for staged evaluations. MEG interictal
epileptiform activity is one of many potential surrogates for SOZ,
and though emerging MEG biomarkers including high frequency

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FIGURE 5 | The localization results for one patient (Subject 16) with spikes in
two clusters by ECDss and by Champagne algorithm.

FIGURE 4 | Localization of high-error spikes by ECDss and Champagne
algorithms: Subject 15. (A) Ground truth as defined by localizations of spikes
by ECDss with errors <10%. (B) Localizations of spikes by ECDss with error>25% (C) Localization results when spikes with error >25% by ECDss werethen localized using the Champagne algorithm. (D) Distance between the
ground truth (A) and the localization result by ECDss (B) and Champagne (C)for those spikes with ECDss error >25%. For (D), whiskers indicate SEM.

“SESAME” was evaluated by Luria et al. (2020) and wasconcordant with standard ECD fitting done by experienced staff.
Pellegrino et al. (2018) applied the coherent Maximum Entropyof the Mean (cMEM) to yield distributed magnetic source images(dMSI) that provided improved accuracy compared to ECD, also
having the advantage of user-independent results; as dMSI results
provide spatial and temporal voxelwise information, they canoffer information about anatomic extent of source beyond thatavailable from ECD. We note that our approach is comparable tothese similar approaches that others have taken, and performancecompared to standard ECD methods is likewise comparable.
Together, these experiences suggest clear and robust alternatives
to parametric dipole fitting procedures.

We then address a second common scenario: where there
spikes where ECDss fails due to high error. To test this we looked
specifically at a group of people who had a population of spikes
that could be modeled using ECDss to yield a cluster of dipolesthat were then used as “ground truth.” They also had spikes
that were more difficult to model with ECDss (albeit having asimilar waveform in sensor space and similar EEG morphology
to those with lower error ECDss models). When these weremodeled, localization results using Champagne were more focal
than were results generated by ECDss. In addition, Champagne
localizations were much closer to the low error “ground truth”
ECDss localizations than were the high error ECDss fits. In thesecases, therefore, we show how we can use Champagne to salvage
spikes that could not be confidently modeled using ECDss due tohigh error.
We also address a third scenario, in one patient withtwo plausible clusters of ECDss sources corresponding tointerictal spikes. For this patient, the Champagne algorithm
is able to resolve these two clusters and to reconstructindependent sources.
The pipeline we describe here does require manual detection
and identification of interictal epileptiform activity at an
early analysis stage. Later stages of the process are relatively
automated, and can be used by operators with variable degrees
of neurophysiology expertise. In recent years, similar Bayesian
iterative approaches have been applied to this problem in order todevelop semi-automated algorithms for clinical use. For example,
an automated multidipole iterative Monte Carlo approach called
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5. CONCLUSION
The application of the Champagne algorithm with noise
learning allows the localization of underlying brain activity
in a precise and objective manner without the need for
additional “baseline” or “control” data to estimate contributions
to sensors from noise. Here, we demonstrate our use of theChampagne algorithm in some scenarios commonly encounteredduring source localization of interictal spikes in people withintractable epilepsy. By comparing the results of Champagne toa conventional source localization technique, ECDss, we haveshown its equivalency, and have also illustrated its use as a salvage
technique in some scenarios where ECDss may fail. The results
suggest that Champagne is a reliable alternative to manual ECDssdipole fitting for clinical application.

DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will bemade available by the authors, without undue reservation.
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Validation of Automated Spike Localization

ETHICS STATEMENTNIH grants R01EB022717, R01DC013979, R01NS100440,
R01DC176960, R01DC017091, R01AG062196 UCOP-MRP-17454755, and T32EB001631 from the NIBIB, and by the Major
Project for Technological Innovation of Hubei Province underGrant 2019AAA044.

The studies involving human participants were reviewed
and approved by UCSF Committee on Human Research.
Written informed consent to participate in this study wasprovided by the participants’ legal guardian/next of kin.
Written informed consent was obtained from the individual(s),and minor(s)’ legal guardian/next of kin, for the publicationof any potentially identifiable images or data included inthis article.

ACKNOWLEDGMENTS
The authors would like to thank all members and collaborators of
the Biomagnetic Imaging Laboratory for their support.

AUTHOR CONTRIBUTIONSSUPPLEMENTARY MATERIAL

CC, HK, JC, and SN contributed to the conception and design ofthe study. CC, JC, AF, DM, HK, and SN organized the database.
CC, JC, AF, KS, HK, and SN performed the statistical analysis.
CC, HK, and SN wrote the first draft of the manuscript. All
authors contributed to manuscript revisions, read, and approved
the submitted version.

The Supplementary Material for this article can be foundonline at: https://www.frontiersin.org/articles/10.3389/fnhum.
2021.642819/full#supplementary-material
Supplementary Table 1 | Clinical characteristics of the 16 subjects. The first 14
described here appear in Table 1. Note that some patients were referred fromoutside institutions and thus their information was limited to that available at thetime of the MEG scan. FCD = focal cortical dysplasia; Lt = Left; Rt = Right; temp
= temporal; occip - occipital; ATL = anterior temporal lobectomy; MTS = mesial
temporal sclerosis; ECoG = electrocorticography; ED = epileptiform discharge;
PQ = posterior quadrant; GFPA = generalized paroxysmal fast activity; SEEG =stereo-EEG; hypo = hypometabolism; PVNH = periventricular nodular
heterotopia; c/w = consistent with.

FUNDING
This work was supported by the National Natural Science
Foundation of China under Grant 61772380, 62007013 and

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Conflict of Interest: KS was employed by company Signal Analysis Inc.
The remaining authors declare that the research was conducted in the absence ofany commercial or financial relationships that could be construed as a potential
conflict of interest.
Copyright © 2021 Cai, Chen, Findlay, Mizuiri, Sekihara, Kirsch and Nagarajan.
This is an open-access article distributed under the terms of the Creative Commons
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May 2021 | Volume 15 | Article 642819