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Granger Causality Analysis of Interictal iEEG Predicts Seizure Focus and Ultimate Resection


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Background: A critical conceptual step in epilepsy surgery is to locate the causal region of seizures. In practice, the causal region may be inferred from the set of electrodes showing early ictal activity. There would be advantages in deriving information about causal regions from interictal data as well. We applied Granger's statistical approach to baseline interictal data to calculate causal interactions. We hypothesized that maps of the Granger causality network (or GC maps) from interictal data might inform about the seizure network, and set out to see if "causality" in the Granger sense correlated with surgical targets. Objective: To determine whether interictal baseline data could produce GC maps, and whether the regions of high GC would statistically resemble the topography of the ictally active electrode (IAE) set and resection. Methods: Twenty-minute interictal baselines obtained from 25 consecutive patients were analyzed. The "GC maps" were quantitatively compared to conventionally constructed surgical plans, by using rank order and Cartesian distance statistics. Results: In 16 of 25 cases, the interictal GC rankings of the electrodes in the IAE set were lower than predicted by chance ( P < .05). The aggregate probability of such a match by chance alone is very small ( P < 10 -20 ) suggesting that interictal GC maps correlated with ictal networks. The distance of the highest GC electrode to the IAE set and to the resection averaged 4 and 6 mm (Wilcoxon P < .001). Conclusion: GC analysis has the potential to help localize ictal networks from interictal data.
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Granger Causality Analysis of Interictal iEEG
Predicts Seizure Focus and Ultimate Resection
Eun-Hyoung Park, PhD
Joseph R. Madsen, MD
Department of Neurosurgery, Boston
Children’s Hospital, Harvard Medical
School, Boston, Massachusetts
This work was presented as an oral
presentation as “Visualizing
epileptogenic networks: causality
analysis to optimize epilepsy surgery” at
the 75th annual meeting of the American
Academy of Neurological Surgery,
September 25 to 28, 2013 in Newport
Beach, California; as “Visualizing
Epileptogenic Networks in Surgical
Planning: Granger Causality in Interictal
iEEG Predicts Seizure Focus”at the
American Society of Pediatric
Neurosurgeons (ASPN) Annual Meeting,
January 26 to 31, 2014 in Peninsula
Papagayo, Costa Rica (the abstract of this
presentation was published in Journal of
Neurosurgery: Pediatrics,vol.13,March
2014, paper 38), and as a poster
presentation entitled “Visualization of
Epileptogenic Networks from Interictal
iEEG Using Granger Causality ” at the 68th
American Epilepsy Society Annual
Meeting, December 5 to 9, 2014 in Seattle,
Washi ngton.
Joseph R. Madsen, MD,
Department of Neurosurgery,
Boston Children’s Hospital,
Harvard Medical School,
Hunnewell 244,
300 Longwood Avenue,
Boston, MA 02115.
Received, June 10, 2016.
Accepted, March 27, 2017.
Published Online, May 2, 2017.
Congress of Neurological
Surgeons 2017.
This is an Open Access article distributed
under the terms of the Creative
Commons Attribution Non-Commercial
License (
licenses/by-nc/4.0/), which permits
non-commercial re-use, distribution, and
reproduction in any medium, provided
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BACKGROUND: A critical conceptual step in epilepsy surgery is to locate the causal region
of seizures. In practice, the causal region may be inferred from the set of electrodes
showing early ictal activity. There would be advantages in deriving information about
causal regions from interictal data as well. We applied Granger’s statistical approach to
baseline interictal data to calculate causal interactions. We hypothesized that maps of the
Granger causality network (or GC maps) from interictal data might inform about the seizure
network, and set out to see if “causality” in the Granger sense correlated with surgical
OBJECTIVE: To determine whether interictal baseline data could produce GC maps, and
whether the regions of high GC would statistically resemble the topography of the ictally
active electrode (IAE) set and resection.
METHODS: Twenty-minute interictal baselines obtained from 25 consecutive patients
were analyzed. The “GC maps” were quantitatively compared to conventionally
constructed surgical plans, by using rank order and Cartesian distance statistics.
RESULTS: In 16 of 25 cases, the interictal GC rankings of the electrodes in the IAE set were
lower than predicted by chance (P<.05). The aggregate probability of such a match by
chance alone is very small (P<1020) suggesting that interictal GC maps correlated with
ictal networks. The distance of the highest GC electrode to the IAE set and to the resection
averaged 4 and 6 mm (Wilcoxon P<.001).
CONCLUSION: GC analysis has the potential to help localize ictal networks from interictal
KEY WORDS: Causal connectivity, Epilepsy surgery, Seizure networks, Intracranial EEG, Surgical planning
Neurosurgery 82:99–109, 2018 DOI:10.1093/neuros/nyx195
Though surgical resection offers patients
the possibility of becoming seizure-free,
it is significantly underused in epilepsy
therapy.1-3One major challenge of resective
surgery is to locate the anatomic site of seizure
origin and remove the causal region. Often,
ictal intracranial electroencephalography (iEEG)
is used to specify the seizure origin, which is
assumed to be the causal part of the seizure
network. In practice, the anatomic site of seizure
ABBREVIATIONS: GC, Granger causality; iEEG,
intracranial electroencephalography; IAE, ictally
active electrode(s); ECoG, electrocorticography;
MEG, magnetoencephalography; fMRI, functional
magnetic resonance imaging; LTM , Long-Term
Monitoring; RZ, resection zone; HFO, high-
frequency oscillation
Supplemental digital content is available for this article at
origin is indicated by the set of ictally active
electrodes (IAE). The IAE set is typically deter-
mined by epileptologists from ictal iEEG and
communicated to the surgeon when planning
resection strategy. In order to obtain ictal data,
it is necessary to wait for 1 or more seizure
during the invasive monitoring period, typically
requiring a week in our institution (Figure 1). If
interictal data could be mined to reveal aspects
of the seizure network, which currently drives
the practice of waiting for seizures, it is possible
that some invasive monitoring cases could be
managed in one stage.
British economist Sir Clive Granger developed
a computational approach to identify what
he called “causal” influences among several
variables sampled over time. This approach
defined causality as a tendency for the past
values of one variable to improve the accuracy
of a prediction for the future value of another
FIGURE 1. IAE set, determined conventionally, and the causal nodes obtained from the technique of GC for patient 1 are shown. During a week of invasive
monitoring (upper left panel), the IAE (including “seizure onset” indicated by dark purple stars and “early spread” indicated by light purple dots) was identified
for 12 seizures and the area around the IAE including superior depth (SD) and inferior depth (ID) electrodes was then removed (upper right panel). The causal
connectivity map (spatial information) was determined from interictal iEEG data (temporal information) obtained on the night of surgery, more than 9hbefore
the first seizure recordings (bottom left panel). Each causal link (blue line) connects the causal node (red teardrop shaped symbol) and a node influenced by the
causal node (blue solid circle). Each node was evaluated based on the causal strength, rank-ordered, and color-coded from the most influential to the least influential
node based on the ranks (bottom right panel). The red stars denote very high-ranked causal nodes, which are within the top 10 ranks with higher than the causal
strength threshold value of 0.5. The causal nodes map simplifies and makes the complex causal network interpretable. In patient 1, the most influential causal
nodes were identified along the SD electrodes consistent with the IAE (compare the circle and arrow from the upper right panel and those from the bottom right
variable. In his Nobel address on the work,4he emphasized that
such statistical inferences could point to meaningful interactions
in a network, even when knowledge about the hidden mecha-
nisms responsible is lacking. We sought to apply this approach
to interictal electrocorticography (ECoG) data, where it seemed
possible that there could be subtle clues to the seizure focus not
immediately visible to the eye.
Other investigators have looked at algorithms aimed at finding
directional influences of one region on another, rather than simple
correlation to reveal networks. Epstein et al5demonstrated that
100 | VOLUME 82 | NUMBER 1 | JANUARY 2018
a related method using Granger algorithms on data resolved into
frequency spectra can reveal network alterations in the immediate
pre-ictal period. With a different frequency-based algorithm,
Wilke et al6showed that the causal network characteristics in
the gamma band (30-50 Hz) observed during interictal periods
are qualitatively similar to those observed during ictal events.
But, neither of these studies focused on testing whether interictal
baseline can be used to predict which nodes are statistically corre-
lated with the location of the seizure focus.
In this study, we asked a utilitarian question: Can interictal
baseline iEEG predict the seizure network (practically represented
by the IAE set of electrodes) and ultimate resection? The interictal
data segments of 25 epilepsy patients were randomly selected from
the iEEG recordings early in the invasive monitoring process.
Conditional Granger causality (GC) formulated in the time
domain7-9was used to reveal causal connectivity from multi-
channel iEEG data (Figure 1).Ourchoicetousethetimedomain
method had the advantage of simplifying data processing steps
and reducing calculation time at the potential cost of revealing
less about specific frequency bands compared with other methods
such as directed transfer function and spectral GC, and high-
frequency oscillations (HFOs).5,6,10,11 For this study, we sought
a method that could be implemented as simply and practically as
possible. The time domain-based GC method could achieve this
by revealing causal connectivity with fewer processing steps while
making minimal assumptions about the data.9,12 It can be applied
directly to a variety of types of data such as EEG, MEG (magne-
toencephalography), fMRI (functional MRI), iEEG used for two
stage resections, or ECoG used for one stage resections.9,12
After performing GC calculations, which graphically display
high-causality regions of the brain (in the Granger sense), we were
struck with the similarity of the GC maps and the set of electrodes
tagged by neurologists in ictal data as active at or near the very
beginning of a seizure. We denote this group of electrodes as the
IAE, and compared GC maps with this set in 2 different ways.
First, do the IAE electrodes rank higher in causality than would
be expected from at random? Second, do the highest causality
electrodes turn out to be closer to the IAE set (or the surgical
resection, for that matter), than predicted by chance?
Any new approach to analyzing interictal iEEG data might be
used to supplement the status quo procedures, such as localization
of spikes or other interictal signatures. If the GC data exactly
duplicated spike data (which would be the case, for example,
if the algorithm functioned as an automated spike detector),
it might add less supplementary value than if it contributed
nonoverlapping information, so we analyzed this as well. A
potential advantage of the GC algorithm would be that it could
be employed irrespective of whether spikes appeared to be present
or not.
Our findings may one day enable a practical approach for
extracting data from intraoperative or extraoperative corticog-
raphy, aiding in the identification and visualization of causal
We retrospectively examined interictal iEEG recordings from 25
consecutive patients with medically refractory epilepsy who underwent
long-term invasive monitoring for planning surgical resection. Before
surgical implantation of electrodes and subsequent resective surgery,
patients’ informed consent for surgical treatment was always obtained.
Our retrospective review of intracranial EEG signals, clinical information
of each patient, and surgical outcomes was approved by our Institutional
Review Board as a post hoc review not requiring consent.
Invasive monitoring was recommended for each of these patients by
consensus of the multidisciplinary Epilepsy Surgery Conference, on the
basis of perceived need for iEEG data to permit optimal planning of a
surgical resection. Typically, better definition of seizure onset zone or
functional anatomic regions, or both was determined after consideration
of all data less invasively obtained. Resections were performed in 24 out
of 25 patients at the time of grid removal. The demographic information
and 1-yr Engel class13 follow-up outcomes are summarized in Table 1.
The patient series was consecutive and not selected by outcome or other
clinical factors.
Data Collection and Preprocessing
The iEEG data were recorded with subdural and/or depth electrodes
(Ad-Tech, Racine, Wisconsin) to accurately identify IAE. The total
number of electrodes for each patient ranged from 64 to 154 (103 ±
26, mean ±standard deviation). We analyzed the first 20 min interictal
baseline segment without technical disruptions or clinical events noted
on EEG annotations. The segments were considered baseline recordings
for each patient, and no effort was made to include or exclude other
electrical features such as spikes. For most of the cases (21 out of 25),
the segment analyzed preceded any clinical or electrographic seizure.
The details of the data collection and preprocessing are described in the
Supplemental Digital Content.
GC Analysis
Causality analysis among the data streams from pairs of electrodes
was calculated by applying Granger’s statistical approach to baseline
interictal data. The Granger method concludes that one time series
causes (or “Granger-causes”) another time series if the past values of
the first data improve the prediction of the future movement of the
other. The GC algorithm is based on linear regression modeling, with
additional details (called model order estimation and model validation)
determined from the data. This was accomplished using the Granger
causal connectivity analysis (GCCA) toolbox,9which allows determi-
nation of causal inference among each possible pair of electrode-specific
iEEG data streams. Graphical visualization of the causal relations among
the components of the epileptogenic network was projected on imaging-
based diagrams. The details have been discussed in the Supplemental
Digital Content.
Statistical Analysis
Statistical Validation Using Rank Order Sum
We compared the results of the GC calculation described above to 2
relevant regional subsets of the electrodes, culled from the Long-Term
Monitoring (LTM) report for each patient. Each of these reports was
TAB LE 1. Demographic and Clinical Information Including Etiology, Pathology, Resection Procedure, and 1-yr Follow-up Outcomefor 25 Patients
gender Etiology Pathology Resection procedure
1 8/F Cortical dysplasia of the right inferior
frontal gyrus
Gliosis and dysplastic neurons Right frontal lesionectomy Ia
2 2.25/M Left frontal lesion FCD Type IIB, Low grade glial
Resection of tumor and small additional
resection of cortex adjacent to the cavity
318/F Unknown Gliosis Left mesial temporal nonlesional resection Ia
4 18/M Unknown Gliosis Left anterior and mesial temporal resection Ia
518/F Extensive bilateral heterotopia FCD Type IA, Hippocampal gliosis Right temporal resection Ia
6 19/F Lesion in left superior temporal gyrus WHO Grade I-II Ganglioglioma Resection of tumor in the superior
temporal sulcus
711/M Unknown FCD Type IIIA, Gliosis Left anterior temporal resection and
additional resection of lateral and basal
seizure focus
8 10/F Right mesial frontal DNET Recurrent/residual low-grade
Resection of parasagittal brain tumor Ia
910/M Focal cortical dysplasia; Prior year left
frontal resection
FCD Type IIA, Gliosis Extension of prior resection, four years
earlier, of a left frontal cortical dysplasia
10 16/M Unknown MTS, Gliosis Left anterior mesiotemporal resection Ia
11 2/F Unknown Microglia activation, Gliosis,
Neuronal injury
Left parietal and frontal cortex resection Ia
12 6/M Left frontal focal cortical dysplasia FCD Type IIA Functional disconnection of left frontal
lobe anterior to motor strip
13 20/F Right parietal oligodendroglioma Cerebral gray and white matter with
extensive reactive changes
(oligodendroglioma 11 years earlier,
then gliosis 4 years earlier)
No resection was done due to concern over
motor decit
14 10/M Unknown (focal seizures, epileptic
Cortex and white matter with
reactive changes
Right frontal resection Ia
15 16/F Left temporo-occiptal subcortical
FCD Type IIA, Vascular malformation Resection of seizure focus on the left side Ia
16 17/M Left mesial temporal lesion FCD Type IIA, HS Left temporal lobe and medial structures
17 12/M Left mesial temporal sclerosis, Left
temporal dysplasia
FCD Type IIA, HS, Gliosis Left mesial temporal lobe resection Ia
18 13/F Unclear: MRI scans suggest some
possible cortical dysplasia in the
anterior-superior temporal gyrus
FCD Type IIA, Gliosis Partial left temporal lobectomy Ib
19 18/M Presumed cortical dysplasia of left
para-hippocampal gyrus (MTLE)
MTS, Gliosis Left temporal tip and mesial temporal
20 10/M Right MCA in utero stroke aecting
inferior frontal temporal and parietal
areas with intraparenchmyal cyst and
FCD Type IA, Gliosis Extension of resection of right frontal
cortical dysplasia (prior surgery 5 years
21 10/F Right Frontal lobe lesion (medial
FCD Type IIB Resection of right mesial frontal lesion in
the vicinity of the motor strip
22 18/M Nonlesional, left frontal Irregularities of cortical
Left mesial frontal resection Ia
23 8.5/F Suspected right parasagittal cortical
Irregularities of cortical
development, Gliosis
Resection of large cortical dysplasia,
24 2/M TSC due to a de novo TSC2 mutation Severe dysplasia and abnormal
glioneuronal cells
Right frontal resection for multiple
subcortical tubers
25 18/F Presumed left frontal cortical dysplasia FCD Type IIB Left frontal cortical resection II
DNET: Dysembryoplastic neuroepithelial tumors, FCD: Focal cortical dysplasia, HS: Hippocampal Sclerosis, MCA: Middle cerebral artery, MTLE: Mesial temporal lobe epilepsy, MTS:
Mesial Temporal Sclerosis, TSC: Tuberous sclerosis complex, WHO: World Health Organization.
aClass I: Free of disabling seizures, Class II: Rare disabling seizures, Class III: Worthwhile improvement, Class IV: No worthwhile improvement.11
102 | VOLUME 82 | NUMBER 1 | JANUARY 2018
generated by one of 10 individual Board-certified neurologists (listed in
the Acknowledgements). For purposes of this paper, the “ictally active
electrode” set, was defined as the collection of electrodes identified in
the LTM report as either “seizure onset zone” or “early spread.” The
subset of electrodes defining the brain volume actually resected (based
on the operative note) defined the “Resection Zone”, or RZ. The subset
of electrodes for interictal spiking (spikes set) was also identified from
LTM report where neurologists summarized their EEG readings over the
entire week of monitoring. Calculation of causality values and clinical
interpretation of iEEG were performed blinded to each other.
To quantify similarity between the IAE set and the set of high-causality
electrodes, we rank-ordered the magnitude of causal influence of each
electrode and summed up the rank order value of the IAE set (Figure 2).
This rank order sum was used as a statistic to test the null hypothesis that
the estimated rank order sum for each patient is not different from what
is expected by chance. A Monte Carlo simulation technique was used
to create the sample distribution of the rank order sum as follows.14,15
The expected random distribution of the sum of possible rank orders
from NIAE electrodes (the number of the IAE taken from a total set of
Ntelectrodes) was determined by randomly selecting NIAE integers from
the set {1,2…,Nt}, summing them, and repeating this process a large
number of times (105replications were used in this study to ensure the
convergence of the distribution).16 If the GC results yielded a sum of rank
orders well out on the tail of this Monte Carlo determined distribution,
the probability of such an event happening by chance could be deter-
mined directly from the percentage area under the distribution curve to
the left of the observed GC value. By this means, individual Pvalues
could be computed for each case. To determine whether the results of
the 25 individual cases combined are significant under the same null
hypothesis, Fisher’s method was applied to compute overall Pvalue.17-19
The exact Pvalue was computed using the method of Kaever and his
Statistical Comparison With Spikes Set
To probe whether interictal spikes per se are either necessary or suffi-
cient to yield strong GC ratings of individual electrodes, we compared
the set of electrodes determined to have spiking in the final LTM report
to the set with the same number of electrodes with the highest GC calcu-
lated from our data. If GC analysis acted in effect as a spike detector,
these sets would be expected to agree in most cases.
Statistical Validation Using Average Minimum Pairwise
As an alternative statistical test, and based on coordinates obtained
from the CT scan, we calculated the distance from the highest ranked
electrode, and the averaged distance from the top 5 electrodes in each
patient to the actual IAE set and the RZ. We selected these 2 arbitrary
examples of highest causality regions to the RZ to allow the calculation
to be made agnostic to the actual size or number of electrodes in contact
with the RZ, which would not necessarily be known at the time of
calculation of the GC maps in future applications of this technique.
It should be also noted that the distance is calculated from one set of
electrodes to another set of electrodes. The Wilcoxon paired signed-
rank test was applied to compare the GC-determined distances and the
distances expected by chance. This approach yields a more physically
meaningful result (giving a measure in millimeters), and has been used
by others.6,21 -24 Because the Pvalue for proximity might depend on the
number of electrodes included as “highest causality” nodes, we repeated
FIGURE 2. Visual depiction of steps for the rank order approach used for the
statistical comparison between the IAE set and the causal nodes. For clear presen-
tation, the IAE nodes (shown in step 1) and the causal nodes (shown in step 2) are
marked on the schematic diagram of grids used for patient 1. The grid electrodes
are placed on the brain in 4 different locations (anterior frontal [AF], subfrontal
[SF], lateral temporal [LT], and mesial temporal [MT]) and depth electrodes are
placed in the brain in 2 locations (SD 10 contacts and ID 10 contacts). The null
distribution was simulated to test significance of the rank order sum indicated by
the arrow shown in step 3 (P value =.00006).
the calculation for all multiples of 5 from 10 to 60. A schematic view of
the distance calculation is shown in Figure 3.
Visual analysis of the network graphs of the causal interactions
identified by the GC method appeared to show a concentration of
“causal nodes” in and around the IAE and the RZ (see Figures 1-3
FIGURE 3. Comparison between the RZ set of electrodes and the highest causality (HC) set of electrodes and between
the HC set and the IAE set using average minimum pairwise distance calculation (patient 1). In the upper panel, a
schematic diagram shows HC set (stars labeled as “e1” to “e4”) and IAE set or RZ set (solid circles labeled as “a” to
“d”). The distances between each element of HC set and each element of IAE set (or RZ set) are denoted by “s” and
“k.” For example, the shortest distance between “e1” (one of the elements from HC set) and each element of IAE (or
RZ) set is indicated as “s2.” Similarly, the shortest distance between “e4” and each element of IAE (or RZ) set is 0 since
“e4” and “a” (one of the elements of IAE (or RZ) set) coincide each other. The distances were used to compare HC set
with IAE set and RZ set as illustrated in the lower panel.
for the results of patient 1). The desire to quantify this similarity
led us to simplify the network maps by depicting diagrams of
high- and low-causality regions. Using the simpler depiction, we
were then able to assess whether the clinically identified zones
had particularly high presence of causality. The graphical images
continued to show a striking similarity to the IAE and the RZ,
prompting us to determine the probability for each case that the
concentration of high GC in the clinically identified zones could
be achieved by chance alone. The Monte Carlo methods described
above allowed calculation of actual probabilities.
Some cases, exemplified in Figure 4, showed extremely high
similarity to the IAE and the RZ. In the cases of patient 3 and
patient 6, both yielded statistically significant results in the rank
order approach (P=.009 for patient 3 and P=.003 for patient 6)
and also showed their close proximity to the IAE and to the RZ (0
mm from the highest electrode for both cases; <10 and <5mm
from top 5 electrodes for patient 3 and patient 6, respectively).
This is what prompted us to look at distance from the highest
causality nodes (top 1 and top 5 electrodes) to the ultimate IAE
and RZ, in addition to rank-order similarities in the first place.
The complete set of Pvalues is shown in Table 2.Giventhat
some cases appeared to support the concept that GC maps from
interictal iEEG predict the IAE, it is reasonable to ask whether
the results from all 25 cases could represent a result based on
chance alone. The answer to this question can be calculated using
Fisher’s method of combining multiple independent tests of the
same hypothesis, which gives a Pvalue of less than 10–20 .
The particular cases of poor similarity between the GC maps
and the IAE are instructive to study. Two cases (patient 4 and
patient 7) are shown in Figure 5. Neither attained statistical signif-
icance in the rank order calculation (P=.06 and P=.7, respec-
tively), but in both cases and some other P>.05 cases, high-
causality electrodes seem close to the actual resections. In almost
all cases, visual inspection shows that the causal electrodes cluster
within or near the IAE and RZ, and the statistics bear out the
clear statistical correlation of the interictal GC analysis with ictal
analysis. Thus, in 12 of 25 cases the top GC electrode was later
identified as one of the IAE, and the mean distance from the
104 | VOLUME 82 | NUMBER 1 | JANUARY 2018
FIGURE 4. Some cases showed remarkable similarity between the high-causality
electrodes and the IAE set. Two of those patients (patient 3 and patient 6) who
showed excellent concordance between high causal nodes and the IAE (P =.009
and P =.003 by rank order, respectively) are shown. The volume-rendered CT
images and the causal connectivity maps of the patients are shown. The rank-
ordered and color-coded causal nodes obtained from GC analysis can be visually
as well as statistically compared (by calculating rank order sum and Cartesian
distance) with the IAE set and with the RZ.
nearest IAE electrode averaged 6 mm (median, 5 mm) over all
cases. In 20 of 24 cases, the top GC electrode was actually in the
RZ, with a mean distance from that zone of 4 mm considering all
cases. These distances are small, considering that spatial resolution
is limited to about 10 mm, the distance between electrodes.
Looking at the 5 electrodes, for example, the Wilcoxon signed-
rank test is also highly significant as shown in Table 2, but the
same calculation yields statistical significance at the P<.05 level
for any sample set size computed, up to 60. At present, our
computational methods do not yield an estimate of the size of
the IAE set or RZ set. Such comparisons require arbitrary cutoffs.
Are interictal spikes necessary or sufficient to result in high-
causality ranking? Comparing the set of electrodes recorded as
showing spikes with a set of the same number of elements with
the highest GC ratings, there was only 33 ±4% (mean ±
standard error) overlap. This suggests that spikes are neither
necessary nor sufficient to yield high GC. One particular example
(patient 6) illustrated the dissociation between GC and interictal
spiking. Invasive monitoring focused on distinguishing 2 radio-
graphic targets: a small left hippocampus vs a 10 mm left superior
temporal gyrus lesion. Grids were placed over both areas to distin-
guish true onset of seizure and to allow functional mapping. We
were able to identify and analyze episodes where interictal spikes
were seen in the mesial temporal strip but not over the lateral
temporal grids. In these same epochs, strong causal influence
concentrated on the nonspiking superior temporal gyrus lesion.
The superior temporal gyrus lesion proved to be the ictal origin
and its removal resulted in seizure freedom. These observations
suggest that GC is not a simple spike detector, meaning that GC
may supplement rather than duplicate conventional iEEG inter-
The motivation of this study was to search for signatures of
putative ictal networks in interictal recordings, which could one
day improve the efficiency of analysis of intracranial data in
epilepsy surgery. By viewing interictal iEEG data through the
computational lens of GC, we generated statistical connectivity
maps, and found that these diagrams often bore resemblance to
the expert readings of the ictal iEEG information which had
not been recorded when the interictal data were sampled. In this
paper, we compared the regions of high GC against the IAE set
and RZ set found clinically, and found that GC could predict,
far better than expected by chance, these regions specified by
our current practice. For practical utility, we were interested in
whether GC analysis in the time domain could give clues to
the anatomic location and extent of the epileptogenic cortex
without the need for observing a seizure. Time domain analysis
was chosen, because it avoids several time-consuming compu-
tational steps: it does not need frequency decomposition and
integration over all frequency ranges to obtain GC results. GC
maps based on interictal iEEG proved to correlate statistically
with the ultimate ictal EEG interpretations, and the maps were
“predictive” in the sense that the ictal data require typically days
of waiting for seizures. It should be noted that the IAE set,
a practical judgment of the electroencephalography team, may
or may not represent the more abstract concept of “epilepto-
genic zone” or “ictogenic zone.”25,26 However, since the outcomes
of these operations, using this approach to reading and catego-
rizing the ictal EEG, yielded good results (92% Engel I at 1
year), readings of the neurologists usually yielded excellent advice
regarding the resection target.
Future Directions to Achieve Further Development of
Causal Network Analysis for Use in Surgical Planning
This statistical correlation is only the first step in the quest to
develop a clinically useful method. Several additional necessary
or highly desirable steps are evident, and suggest further research
directions. The first of these might be a more intuitive under-
standing of what high GC results mean in terms of traditional
TAB LE 2. Statistical Comparison (by Rank Order and Distance) of Electrodes Highlighted by the GC Map (Based on Interictal Baseline Data) Compared With the Ictally Active
Electrode (IAE) Set Identied From Ictal Data and Ultimate Resection. Individual Patients are Shown in Order of Rank Order Correspondence, with Best Correspondence
(Lowest Pvalue) at the Top of the List
Comparison of the causality ranking of the IAE set
with the causality rankings of a set of the same
number of electrodes selected by chance Comparison of the distances of the 1 or 5 highest causality electrodes to the IAE set or RZ set
(by sum of the rank orders) compared with the corresponding expected distance of electrodes selected at random.
Rank order
sum by
Probability of
observed value
by chance
Distance from
electrode to
IAE set (mm)
Mean distance
from top ve
electrodes to
IAE set (mm)
distance of
random set of
electrodes to
IAE set (mm)
Distance from
electrode to
RZ set (mm)
Mean distance
from top ve
electrodes to
RZ set (mm)
distance of
random set of
electrodes to
RZ set (mm)
22 2566 3595 <.001 012 13 035 42
14 646 1013 <.001 0 4 15 0 5 27
1719 1199 <.001 5 1 12 0 0 11
12 1447 1936 .002 0 8 12 0 2 28
13 1317 1807 .002 053 37 N/AaN/A N/A
6 829 1210 .003 0 4 15 0 4 24
21 389 676 .005 830 39 031 39
23 912 1212 .007 0 2 18 0 1 18
2558 769 .008 014 21 11 19 25
3 817 1067 .009 0 8 17 0 9 27
25 345 620 .01 620 27 021 27
24 1050 1356 .02 0 9 16 0 22 25
5901 1177 .02 11 10 15 0 2 12
10 479 647 .02 18 8 14 44 9 20
15 1063 1284 .05 18 23 20 18 28 23
8 2696 3099 .05 0 7 11 0 20 28
4437 548 .06 10 14 12 0 2 13
11 203 260 .13 0 14 17 0 12 16
20 713 838 .16 21 21 30 010 21
18 593 657 .20 15 14 11 0 7 10
19 286 325 .24 10 15 18 010 14
17 914 977 .28 0 14 20 0 5 17
16 456 475 .39 16 19 18 10 616
9 611 595 .57 10 25 19 0 34 21
71810 1732 .70 10 817 0 2 33
Combined Pvalue: <1020 Mean 6 14 19 4 12 22
Median 514 17 0 9 22
PairedbP<.0001 P=.003 P<.001 P<.001
aNot applicable: no resection was done.
bWilcoxon signed rank test.
106 | VOLUME 82 | NUMBER 1 | JANUARY 2018
FIGURE 5. Several cases showed poor similarity to the IAE set. Two of such cases
(patient 4 and patient 7) with weaker statistical correlation between high causal
nodes and the IAE (P =.06 and P =.70, respectively) are shown, arranged as in
Figure 4. The volume-rendered CT images and the causal connectivity maps of the
patients are shown. The rank-ordered and color-coded causal nodes obtained from
GC analysis can be visually as well as statistically compared (by calculating rank
order sum and Cartesian distance) with the IAE and with the RZ. In patient 4,
the most causal nodes clearly suggest the temporal lobe, though with less emphasis
on the small focus of onset observed in the ictal iEEG. The high-causality nodes
virtually all resided within the resection. In patient 7, where monitoring was done
around a previous resection cavity, the causality map pointed to a broader region,
including much of the lateral frontal lobe. The resection in this particular case
included only temporal tissue.
EEG interpretation. This could be gleaned from comparison of
other methods of analysis of interictal data with the computa-
tional analysis by GC method. Most obviously, the reading of
individual samples of iEEG by human experts could be systemat-
ically compared with the GC analysis of the same samples. Such
comparison was not yet included in our study design, but could
inform us about the relationship between the GC map and various
interictal features of iEEG. Similarly, interictal features which
may be detected by other digital techniques, such as HFOs27-29
could also be correlated with the time-domain GC map. It should
be noted that the method implemented in this study actually
filters-out HFOs before making its calculations. Since HFOs are
not visible to this algorithm, additional data from HFOs, or a
modified GC method including higher frequency data, might
significantly improve the method outlined here. Similarly, an
algorithm to identify and include conventional features such as
spikes, which may also occur independently of the GC calcu-
lation, might enhance the utility of an automated method for
interictal data display.
A second need is technical: optimal computational speed. The
present studies were done retrospectively, but incorporation into
active surgical decision making will be progressively more feasible
as the computational speed increases. The actual calculations
for this paper were not optimized for speed, but recently, using
parallel computing, we have been able to reduce hours to a few
minutes for the analysis of 20 s of 100 channels.
With progress on the steps stated above, the investigation
of GC analysis performed intraoperatively may begin. Initial
applications might focus on one stage resections with planned
ECoG. We foresee progressing by supplementing conventional
interpretation by neurologists with automated visualization of
networks. If this approach yields information useful to surgeons,
appropriately validated at some point, it might be included
in making intraoperative decisions about whether a long-term
invasive monitoring is necessary to see a seizure or whether GC
with other modalities (such as a spike localization and an HFO
detection) could in some cases obviate the need for a long-term
invasive monitoring with a second craniotomy.
This early investigation has limitations. One is that the study
population may be not typical for all epilepsy surgery practices.
In this study, the series of patients happened to include a large
number of lesional cases and also turned out to have generally
good surgical outcomes. This perhaps helped illustrate how the
graphical models correspond with the conventional advice given
by epileptologists, but testing in more varied populations will be
useful. A broader mix of outcome results might help determine
whether resections including the most causal electrode zones
improve outcome. Another limitation is that there is still the
need to determine how large the resection set should be. This
will be one of several computational improvements that we might
imagine in the future. We can, however, now conclude that useful
information can be mined from interictal corticography.
Our findings from the application of time domain GC analysis
to interictal iEEG suggest that the value of interictal data as a
surgical planning tool can be enhanced computationally. The
technique described in this paper can produce graphical depic-
tions of interesting networks without actually recording seizures.
Evaluation in the operating room will be needed to assess the
potential impact of GC analysis on surgical decision making.
The authors have no personal, financial, or institutional interest in any of the
drugs, materials, or devices described in this article.
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Supplemental digital content is available for this article at www.neurosurgery-
We would like to thank Dr. David Zurakowski for helpful discussion
of methods and statistical analysis, Laurel Fleming for copy editing of the
manuscript, and Dr. Phillip Pearl for careful review and thoughtful comments
on this manuscript. We thank the Division of Epilepsy and Clinical Neurophysi-
ology at Boston Children’s Hospital and the Long Term Monitoring Unit staff for
their kind cooperation and counsel on our research. In particular, we would like to
thank the following 10 board-certified epileptologists who read each of the EEG
recordings for the 25 cases for the purpose of surgical planning: Drs. Ann Marie
Bergin, Jeffrey Bolton, Blaise Bourgeois, Chellamani Harini, Sanjeev Kothare,
Mark Libenson, Jurriaan Peters, Annapurna Poduri, Alexander Rotenberg, and
Masanori Takeoka.
The authors have used sophsiticated analyses to demonstrate that
Granger Casuality can be used to identify seizure onset regions, using
interictal data, in a selected population. This is a nice addition to the
ongoing efforts to use timeseries information to identify seizure onset
zones from interictal data. The idea that the interictal state can replicate
the spread pattern of seizures is attractive. It will be very important to
determine whether this method works in non-lesional cases for several
reasons. First, in lesional cases (which characterizes the majority of the
cases in this report) there is much less doubt as to the seizure onset
zone and, in some centers, long-term invasive monitoring is avoided
completely. This is perhaps increasingly the case as imaging methods
improve. Second, it is the non-lesional cases that are most in need of
avoiding extensive invasive monitoring. Thirdly, non-lesional cases are
at higher risk of being multi-focal and it is not clear if this method will
hold in this situation. If invasive monitoring is ‘close’ but not exact to
the onset, will Granger Causality give an answer anyway? This will be
a false positive, but only if it would lead to an incorrect resection. This
line of work will be of great interest especially as increasingly sophisti-
cated analyses (Granger Causality as well as other directional analyses
are applied, such as Direct Transfer Function, Phase Slope Index, Phase
Amplitude Coupling, etc), higher frequency sampling, and machine
learning algorithms that can analyze all data (electrophysiology, imaging,
etc) are applied to the localization problem.
Jeffrey G. Ojemann
Seattle, Washington
108 | VOLUME 82 | NUMBER 1 | JANUARY 2018
This article describes a novel and sophisticated method for the
prediction of epileptogenic zones using interictal iEEG data. Tradi-
tionally, seizure onset zones are the primary source of information for
surgical decision making. The authors time domain Granger Causality
analysis provides an alternative data point that does not require the
patient to have a seizure and has the long-term potential to decrease the
duration of iEEG monitoring. Algorithms that visualize and quantify
network activity in the brain, as described in this article, are valuable
and may provide new insights on the dynamics of seizures and interictal
Sara Hanrahan
Adam O. Hebb
Englewood, Colorado

Supplementary resource (1)

... Perhaps the most developed is the use of interictal high-frequency oscillations (80-500 Hz; HFOs) derived from iEEG and scalp EEG recordings. HFOs have demonstrated potential in their ability to serve as a biomarker for the seizure onset zone [1][2][3][4]. Localized HFOs have also been detected from ictal sEEG recordings, supporting the case for the accuracy of HFOs in SOZ detection [5]. However, despite recent advances in algorithmic HFO detection, the standard protocol remains physician determination of HFO through visual inspection of EEG data [3,6]. ...
... However, despite recent advances in algorithmic HFO detection, the standard protocol remains physician determination of HFO through visual inspection of EEG data [3,6]. Furthermore, the HFO methods require processing data in the frequency domain, which is more computationally intensive than the more recent GC time-domain method [1]. Because of our motivations to cut down on the time needed for SOZ determination, eliminating computational steps and automating the entire process are important for the clinically viability of any SOZ determination methods. ...
... We build off the previous result that time domain Granger Causality (GC) analysis of baseline interictal iEEG data indicates which electrodes seem to be more influential for activity at a network of electrodes [1]. The retrospective study found that brain regions surrounding these interictally causal electrodes matched the regions chosen for resection through traditional analysis of ictal data with an aggregate probability much smaller than chance (p < 10 −20 ) [1]. ...
Current epilepsy surgery planning protocol determines the seizure onset zone (SOZ) through resource-intensive, invasive monitoring of ictal events. Recently, we have reported that Granger Causality (GC) maps produced from analysis of interictal iEEG recordings have potential in revealing SOZ. In this study, we investigate GC maps' network connectivity patterns to determine possible clinical correlation with patients' SOZ and resection zone (RZ). While building understanding of interictal network topography and its relationship to the RZ/SOZ, we identify algorithmic tools with potential applications in epilepsy surgery planning. These graph algorithms are retrospectively tested on data from 25 patients and compared to the neurologist-determined SOZ and surgical RZ, viewed as sources of truth. Centrality algorithms yielded statistically significant RZ rank order sums for 16 of 24 patients with RZ data, representing an improvement from prior algorithms. While SOZ results remained largely the same, this study validates the applicability of graph algorithms to RZ/SOZ detection, opening the door to further exploration of iEEG datasets. Furthermore, this study offers previously inaccessible insights into the relationship between interictal brain connectivity patterns and epileptic brain networks, utilizing the overall topology of the graphs as well as data on edge weights and quantity of edges contained in GC maps.
... Although this definition is pragmatic and close to clinical practice of epilepsy surgery, it has several disadvantages, including the following: exclusion of nonoperated patients (often more complex cases with a wider EZ and counting up to 50% of SEEG-explored patients), inclusion of nonepileptogenic regions within this zone (e.g., anterior temporal lateral neocortex in standard anterior temporal lobectomy/lateral structures on the surgical access to mesial EZ), and exclusion of some EZs (e.g., located in eloquent cortices). Following such methodology, Park and Madsen (2018) demonstrated that interictal connectivity (estimated from Granger causality on a mix of depth-electrodes and ECoG recordings in 25 patients) predicts significantly better than the chance location of EZ/RZ. Shah et al (2019b) have recently replicated this finding in a study on 27 patients recorded using a mix of ECoG and depth electrodes (with a majority of ECoG). ...
... Another explanation could be the impact of interictal spikes on connectivity directionality, because some authors included spikes in their analyses (Bettus et al, 2011;Lagarde et al, 2018;Varotto et al, 2012) and others did not (Narasimhan et al, 2020;Paulo et al, 2022). Therefore, despite the relatively low effect of ep-ileptic spikes on the overall connectivity value (Bartolomei et al, 2013;Bettus et al, 2008;Jiang et al, 2022;Park and Madsen, 2018), it is possible that spikes affect the directionality of connectivity (Karunakaran et al, 2018). Further studies looking at the effect of spikes and methods used for estimating the directionality of the connectivity of the EZ are still needed. ...
Full-text available
Focal epilepsies are diseases of neuronal excitability affecting macroscopic networks of cortical and subcortical neural structures. These networks ("epileptogenic networks") can generate pathological electrophysiological activities during seizures but also between seizures (interictal period). Many works attempt to describe these networks by using quantification methods, particularly based on the estimation of statistical relationships between signals produced by brain regions, namely Functional connectivity (FC). FC has been shown to be greatly altered during seizures and in the immediate peri-ictal period. An increasing number of studies have shown that FC is also altered during the interictal period depending on the degree of epileptogenicity of the structures. Furthermore, connectivity values could be correlated with other clinical variables including surgical outcome. This leads to a conceptual change and to consider epileptic areas as both hyperexcitable and abnormally connected. These data open the door to the use of interictal FC as a marker of epileptogenicity and as a complementary tool for predicting the effect of surgery. In this article, we will review the available data concerning interictal FC estimated from iEEG in focal epilepsies and discuss it in the light of data obtained from other modalities (EEG, MEG, MRI) and modelling studies.
... The net causal outflow also exceeded three standard deviations (SD) from the mean value. The study of 25 patients in [64] using the Granger causal connectivity analysis (GCCA) toolbox [65] determined the causal inference among each possible pair of electrode-specific iEEG data using 20 minutes of interictal data. The visual analysis of the obtained graphs showed a concentration of ''causal nodes'' in and around the ictally active electrodes. ...
Full-text available
This paper discusses the various methods of identifying the seizure onset zone (SOZ) from the intracranial electroencephalography (iEEG) data. Epilepsy, also known as seizure disorder, is a neurological condition caused due to disruption in the regular electrical communication within the neuron network. With almost a third of epileptic conditions being drug-resistant and several cases with no known cause, there is a need to resort to alternative treatment methods such as neurostimulation or surgical resection. Both these methods require the identification of regions within the brain that need to be stimulated or resected. For most of the patients, this corresponds to the part that initiates the seizure. These are called seizure onset zone (SOZ) or epileptogenic zone (EZ). Epileptologists locate the SOZ by analyzing the iEEG data of patients suffering from seizures. This, however, is time-consuming and can be prone to human error. Thus, there has been significant research on the automatic detection of SOZ. High-frequency oscillations (HFOs), characterized by iEEG oscillations above 80 Hz, are one of the most promising candidates for identifying SOZ. Functional connectivity and graph theory measures have also distinguished SOZ and non-SOZ regions using different features. Newer works on phase-amplitude coupling have also shown promising results. With the increased data availability, it has also become possible to build supervised learning algorithms to improve the predictive power of anomaly detection algorithms used to localize SOZ.
... 28 Likewise, functional network analyses have demonstrated that patients with more functional abnormalities persisting after surgery are less likely to become seizure free. [29][30][31][32][33][34][35][36] Recent studies show that alterations in structural and functional brain networks spread from a traditionally defined epileptic focus to other connected brain regions, and these alterations increase with epilepsy severity and duration. 37,38 Overall, these studies suggest that various methodologies can localize network abnormalities present in surgical non-responders and could help us redefine the brain tissue that is targeted by surgical treatments. ...
Full-text available
Drug resistant epilepsy is a disorder involving widespread brain network alterations. Recently, many groups have reported neuroimaging and electrophysiology network analysis techniques to aid medical management, support presurgical planning, and understand postsurgical seizure persistence. While these approaches may supplement standard tests to improve care, they are not yet used clinically or influencing medical or surgical decisions. When will this change? Which approaches have shown the most promise? What are the barriers to translating them into clinical use? How do we facilitate this transition? In this review, we will discuss progress, barriers, and next steps regarding the integration of brain network analysis into the medical and presurgical pipeline.
... To answer the questions above, epileptic activity should be assessed in terms of functional connectivity and dynamics of neuronal networks (23). Among the many methods of functional connectivity, Granger causality (GC) is a reliable tool to estimate the interactions from time-series data during seizure onset and propagation (26,27) and has the potential to help localize the ictal network (28,29). In addition, if multiple neuronal groups have been recorded simultaneously, the conditional GC can distinguish the interaction relationship between direct vs. indirect interactions (30). ...
Full-text available
Responsive neural stimulation (RNS) is considered a promising neural modulation therapy for refractory epilepsy. Combined stimulation on different targets may hold great promise for improving the efficacy of seizure control since neural activity changed dynamically within associated brain targets in the epileptic network. Three major issues need to be further explored to achieve better efficacy of combined stimulation: (1) which nodes within the epileptogenic network should be chosen as stimulation targets? (2) What stimulus frequency should be delivered to different targets? and (3) Could the efficacy of RNS for seizure control be optimized by combined different stimulation targets together? In our current study, Granger causality (GC) method was applied to analyze epileptogenic networks for finding key targets of RNS. Single target stimulation (100 μA amplitude, 300 μs pulse width, 5s duration, biphasic, charge-balanced) with high frequency (130 Hz, HFS) or low frequency (5 Hz, LFS) was firstly delivered by our lab designed RNS systems to CA3, CA1, subiculum (SUB) of hippocampi, and anterior nucleus of thalamus (ANT). The efficacy of combined stimulation with different groups of frequencies was finally assessed to find out better combined key targets with optimal stimulus frequency. Our results showed that stimulation individually delivered to SUB and CA1 could shorten the average duration of seizures. Different stimulation frequencies impacted the efficacy of seizure control, as HFS delivered to CA1 and LFS delivered to SUB, respectively, were more effective for shortening the average duration of electrographic seizure in Sprague-Dawley rats ( n = 3). Moreover, the synchronous stimulation of HFS in CA1 combined with LFS in SUB reduced the duration of discharge significantly in rats ( n = 6). The combination of responsive stimulation at different targets may be an inspiration to optimize stimulation therapy for epilepsy.
Introduction The brain is a series of networks of functionally and anatomically connected, bilaterally represented structures; in epilepsy, activity of any part of the brain affects activity in the other parts. This is relevant for understanding the pathophysiology, diagnosis, and prognosis of the disease. Objectives In this study, we present a state-of-the-art review of the neurophysiological view of epilepsy as a disease affecting neural networks. Results We describe the basic and advanced principles of epilepsy as a disease affecting neural networks, based on the use of different clinical and mathematical techniques from a neurophysiological perspective, and signal the limitations of these findings in the clinical context. Conclusions Epilepsy is a disease affecting complex neural networks. Understanding these will enable better management and prognostic confidence.
Visual review of intracranial electroencephalography (iEEG) is often an essential component for defining the zone of resection for epilepsy surgery. Unsupervised approaches using machine and deep learning are being employed to identify seizure onset zones (SOZs). This prompts a more comprehensive understanding of the reliability of visual review as a reference standard. We sought to summarize existing evidence on the reliability of visual review of iEEG in defining the SOZ for patients undergoing surgical workup and understand its implications for algorithm accuracy for SOZ prediction. We performed a systematic literature review on the reliability of determining the SOZ by visual inspection of iEEG in accordance with best practices. Searches included MEDLINE, Embase, Cochrane Library, and Web of Science on May 8, 2022. We included studies with a quantitative reliability assessment within or between observers. Risk of bias assessment was performed with QUADAS-2. A model was developed to estimate the effect of Cohen kappa on the maximum possible accuracy for any algorithm detecting the SOZ. Two thousand three hundred thirty-eight articles were identified and evaluated, of which one met inclusion criteria. This study assessed reliability between two reviewers for 10 patients with temporal lobe epilepsy and found a kappa of .80. These limited data were used to model the maximum accuracy of automated methods. For a hypothetical algorithm that is 100% accurate to the ground truth, the maximum accuracy modeled with a Cohen kappa of .8 ranged from .60 to .85 (F-2). The reliability of reviewing iEEG to localize the SOZ has been evaluated only in a small sample of patients with methodologic limitations. The ability of any algorithm to estimate the SOZ is notably limited by the reliability of iEEG interpretation. We acknowledge practical limitations of rigorous reliability analysis, and we propose design characteristics and study questions to further investigate reliability.
Background: Stereoelectroencephalography (sEEG) facilitates electrical sampling and evaluation of complex deep-seated, dispersed, and multifocal locations. Granger causality (GC), previously used to study seizure networks using interictal data from subdural grids, may help identify the seizure-onset zone from interictal sEEG recordings. Objective: To examine whether statistical analysis of interictal sEEG helps identify surgical target sites and whether surgical resection of highly ranked nodes correspond to favorable outcomes. Methods: Ten minutes of extraoperative recordings from sequential patients who underwent sEEG evaluation were analyzed (n = 20). GC maps were compared with clinically defined surgical targets using rank order statistics. Outcomes of patients with focal resection/ablation with median follow-up of 3.6 years were classified as favorable (Engel 1, 2) or poor (Engel 3, 4) to assess their relationship with the removal of highly ranked nodes using the Wilcoxon rank-sum test. Results: In 12 of 20 cases, the rankings of contacts (based on the sum of outward connection weights) mapped to the seizure-onset zone showed higher causal node connectivity than predicted by chance (P ≤ .02). A very low aggregate probability (P < 10-18, n = 20) suggests that causal node connectivity predicts seizure networks. In 8 of 16 with outcome data, causal connectivity in the resection was significantly greater than in the remaining contacts (P ≤ .05). We found a significant association between favorable outcome and the presence of highly ranked nodes in the resection (P < .05). Conclusion: Granger analysis can identify seizure foci from interictal sEEG and correlates highly ranked nodes with favorable outcome, potentially informing surgical decision-making without reliance on ictal recordings.
The severe neurological disorder epilepsy affects almost 1% of the world population. For patients who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings are essential for the localization of the brain area where seizures start. Apart from the visual inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for this purpose. Among other features, regularity versus irregularity and phase coherence versus phase independence allowed characterizing brain dynamics from the measured EEG signals. Can phase irregularities also characterize brain dynamics? To address this question, we use the univariate coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean phase coherence to quantify the degree of phase locking. All phase-based measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. In the first part of our analysis, we use the Rössler model system to study our approach under controlled conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded from areas that are not involved in the seizure at its onset. Our results show that focal signals have less phase variability and more phase coherence than nonfocal signals. Once combined with surrogates, the mean phase velocity proved to have the highest discriminative power between focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based measures can help to detect features induced by epilepsy from EEG signals. This holds not only for the classical mean phase coherence but even more so for univariate measures of phase irregularity.
Objective: A 'Virtual resection' consists of computationally simulating the effect of an actual resection on the brain. We validated two functional connectivity based virtual resection methods with the actual connectivity measured using post-resection intraoperative recordings. Methods: A non-linear association index was applied to pre-resection recordings from 11 extra-temporal focal epilepsy patients. We computed two virtual resection strategies: first, a 'naive' one obtained by simply removing from the connectivity matrix the electrodes that were resected; second, a virtual resection with partialization accounting for the influence of resected electrodes on not-resected electrodes. We validated the virtual resections with two analysis: 1) We tested with a Kolmogorov-Smirnov test if the distributions of connectivity values after the virtual resections differed from the actual post-resection connectivity distribution; 2) we tested if the overall effect of the resection measured by contrasting pre-resection and post-resection connectivity values is detectable with the virtual resection approach using a Kolmogorv-Smirnov test. Results: The estimation of post-resection connectivity values did not succeed for both methods. In the second analysis, the naive method failed completely to detect the effect found between pre-resection and post-resection connectivity distributions, while the partialization method agreed with post-resection measurements in detecting a drop connectivity compared to pre-resection recordings. Conclusion: Our findings suggest that the partialization technique is superior to the naive method in detecting the overall effect after the resection. Significance: We pointed out how a realistic validation based on actual post-resection recordings reveals that virtual resection methods are not yet mature to inform the clinical decision-making.
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### Introduction A key challenge in neuroscience and, in particular, neuroimaging, is to move beyond identification of regional activations toward the characterization of functional circuits underpinning perception, cognition, behavior, and consciousness. Granger causality (G-causality) analysis
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Power modulations of oscillations in electro- and magnetoencephalographic (EEG/MEG) signals have been linked to a wide range of brain functions. To date, most of the evidence is obtained by correlating bandpower fluctuations to specific target variables such as reaction times or task ratings, while the causal links between oscillatory activity and behavior remain less clear. Here, we propose to identify causal relationships by the statistical concept of Granger causality, and we investigate which methods are bests suited to reveal Granger causal links between the power of brain oscillations and experimental variables.
Interictal high frequency oscillations (HFOs) in intracranial EEG are a potential biomarker of epilepsy, but current automated HFO detectors require human review to remove artifacts. Our objective is to automatically redact false HFO detections, facilitating clinical use of interictal HFOs. Intracranial EEG data from 23 patients were processed with automated detectors of HFOs and artifacts. HFOs not concurrent with artifacts were labeled quality HFOs (qHFOs). Methods were validated by human review on a subset of 2000 events. The correlation of qHFO rates with the seizure onset zone (SOZ) was assessed via (1) a retrospective asymmetry measure and (2) a novel quasi-prospective algorithm to identify SOZ. Human review estimated that less than 12% of qHFOs are artifacts, whereas 78.5% of redacted HFOs are artifacts. The qHFO rate was more correlated with SOZ (p=0.020, Wilcoxon signed rank test) and resected volume (p=0.0037) than baseline detections. Using qHFOs, our algorithm was able to determine SOZ in 60% of the ILAE Class I patients, with all algorithmically-determined SOZs fully within the resected volumes. The algorithm reduced false-positive HFO detections, improving the precision of the HFO-biomarker. These methods provide a feasible strategy for HFO detection in real-time, continuous EEG with minimal human monitoring of data quality. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
The prime object of this book is to put into the hands of research workers, and especially of biologists, the means of applying statistical tests accurately to numerical data accumulated in their own laboratories or available in the literature.
Objective In recent decades intracranial EEG (iEEG) recordings using increasing numbers of electrodes, higher sampling rates, and a variety of visual and quantitative analyses have indicated the presence of widespread, high frequency ictal and preictal oscillations (HFOs) associated with regions of seizure onset. Seizure freedom has been correlated with removal of brain regions generating pathologic HFOs. However, quantitative analysis of preictal HFOs has seldom been applied to the clinical problem of planning the surgical resection. We performed Granger causality (GC) analysis of iEEG recordings to analyze features of preictal seizure networks and to aid in surgical decision making.Methods Ten retrospective and two prospective patients were chosen on the basis of individually stereotyped seizure patterns by visual criteria. Prospective patients were selected, additionally, for failure of those criteria to resolve apparent multilobar ictal onsets. iEEG was recorded at 500 or 1,000 Hz, using up to 128 surface and depth electrodes. Preictal and early ictal GC from individual electrodes was characterized by the strength of causal outflow, spatial distribution, and hierarchical causal relationships.ResultsIn all patients we found significant, widespread preictal GC network activity at peak frequencies from 80 to 250 Hz, beginning 2–42 s before visible electrographic onset. In the two prospective patients, GC source/sink comparisons supported the exclusion of early ictal regions that were not the dominant causal sources, and contributed to planning of more limited surgical resections. Both patients have a class 1 outcome at 1 year.SignificanceGC analysis of iEEG has the potential to increase understanding of preictal network activity, and to help improve surgical outcomes in cases of otherwise ambiguous iEEG onset.