This study investigates the concordance between resting-state functional MRI (rs-fMRI) and stereotactically-implanted EEG (sEEG) in measuring interactions between brain regions in epilepsy patients. Electrodes were implanted in 13 brain regions and rs-fMRI data was collected. Coherence between sEEG electrode pairs and correlation between corresponding rs-fMRI regions were calculated. Strong similarities were found between the sEEG coherence and rs-fMRI correlation matrices, though some regions differed. Certain region pairs, like between the precuneus and posterior cingulate cortex, showed particularly high interaction levels in both modalities. This preliminary analysis indicates potential for rs-fMRI to identify connectivity patterns seen in sEEG.

1. BA7, BA31 and BA40 are implicated in resting state
networks [4]. Figure 6 focuses on these three regions.
The correlation between BA31-BA7 show the strongest
interaction. Interestingly, BA7 and BA31 lie in the brain’s
DFM (default mode) network [5]. Here we have focused
on electrodes in the DFM as these are known to exhibit
strong spontaneous interactions.
Invasive electrical recordings and non-invasive imaging
techniques both play a central role for epileptic patients.
In this preliminary study we investigate sEEG
(stereotactically-implanted EEG) and rs-fMRI (resting
state functional MRI) data from epilepsy patients with the
goal of establishing concordance in measures of
interaction between electrode locations using the two
modalities. We compare low frequency coherence
analysis between multiple sEEG electrode pairs with the
resting state correlation between those same locations as
measured using rs-fMRI.
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[2] M. Jenkinson, et al. FSL. NeuroImage, 62:782-90, 2012.
[3] C Bhushan, et al. "INVERSION: A robust method for co-
registration of MPRAGE and diffusion MRI images", Joint
Annual Meeting ISMRM-ESMRMB, Milan, Italy, 2014, p. 2583.
[4] M De Luca et al. "fMRI resting state networks define distinct
modes of long-distance interactions in the human brain."
Neuroimage 29.4 (2006): 1359-1367.
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3. FMRI data processing1. Introduction
Functional Connectivity in Intractable Epilepsy:
Comparing Resting-state Functional MRI and sEEG
Kangwoo Lee1, Richard M Leahy1,2
1Department of Biomedical Engineering, USC, Los Angeles, CA
2Department of Electrical Engineering, USC, Los Angeles, CA
5. Conclusion
Acknowledgments: This work was supported by NIH grant number
R01-NS089212-01A1. Functional MRI and sEEG data were
provided by Cleveland Clinic.
6. Discussion and future work
Figure 1: The placement of electrodes. 13 depth electrodes with
a total of 130 contacts are implanted into the right hemisphere of
the brain.
2. SEEG processing
1 mm pore
Figure 2: Raw (dotted line) and pre-processed (solid line) sEEG
data of one specific channel B10. Preprocessing includes ARIMA
filtering and detrending, to remove drift, DC components, and
reduce non-stationarity.
Figure 3: Axial view of co-registration of fMRI and structural MRI data using
Brainsuite’s BDP addon.
4. Result
62 out of 130 channels are chosen after white matter contacts
and outlier removal, and those channels are located in prefrontal
cortex (PFC), insula, hippocampus, posterior cingulate cortex
(PCC), amygdala, diverse areas of medial/inferior/lateral parietal
lobe, and those of medial/inferior/lateral temporal lobe. There was
no significant negative correlation among selected channels (-0.2
< R- < 0). Figure 4 shows the coherence and correlation matrices
of sEEG and fMRI data. After applying thresholds, some regions
have stronger correlation/coherence than others, and those
regions are similar between sEEG and fMRI data, except region
(4) as indicated in Figure 4. The contacts in the strong coherence
region (4) are all in the same area (middle temporal gyrus).
Regions (1), (2), (3) display the relations between precuneus
(BA7) - posterior cingulate gyrus (BA31), precuneus -
supramarginal gyrus (BA40), and posterior cingulate gyrus –
supramarginal gyrus, respectively. Interestingly, Regions (1), (2)
and (3) in the correlation/coherence plots show comparatively
strong interaction in both sEEG and fMRI between widely
separated cortical areas.
That we seem strong similarities between sEEG and rs-
fMRI indicates the potential for rs-fMRI to identify
connectivity patterns currently identified cinically using
implanted electrodes. These results are preliminary and
show stronger correlations between some regions that we
would expect from the literature. We will explore these
issue in more depth as we investigate data from multiple
subjects. Our future objective is to perform partial
coherence and correlation analysis with more subjects to
remove the effects of third variables, so that direct
connectivity can be determined. We will also look beyond
the default mode network to investigate connectivity in
networks that contain epileptogenic seizure onset zones.
Figure 4: Cross-coherence and cross-correlation matrices. (a)(b) are sEEG
cross-coherence and (c)(d) are fMRI cross-correlation. (b)(d) focus on
targeted regions. Region in (1) left-top (2) left-bottom, (3) left-bottom next to
(2), and (4) center of (a)(c) are marked. (1), (2) and (3) indicate the relations
between precuneus and posterior cingulate gyrus , precuneus and
supramarginal gyrus, and posterior cingulate cortex and supramarginal
gyrus, respectively. Region (4) in the center of (a) shows interaction
between contacts all placed in middle temporal gyrus.
Figure 5: Boxplot of interactions between multiple brain areas and (a) posterior cingulate gyrus, (b) precuneus, and (c) supramarginal gyrus. These
boxplots are the average of sEEG coherence matrix and fMRI correlation matrix. Posterior cingulate gyrus (BA31) is highly correlated to angular gyrus
(BA39), precuneus (BA7), and supramarginal gyrus (BA40). BA7 is highly correlated to BA31. BA40 is highly correlated to BA31. Correlation values of
BA31-BA40 and BA31-BA7 show comparatively higher than other regions. The correlation between BA7 and BA40 show smaller value in boxplot, since
correlation in fMRI was not significant compare to other two strong correlation regions.
Resting-state sEEG recording were collected as follows:
contacts made of titanium and titanium oxide; 2mm long
by 0.8mm diameter; 1000Hz sampling rate; 13 depth
electrodes with a total of 130 contacts (10 per
electrode). All electrodes are implanted into the right
hemisphere of brain tissue (Fig. 1). For better
understanding of the data or prediction of future points in
the time-series, we apply an ARIMA filter to reduce non-
stationarity (Re-sampling: 200Hz). Also, the data is
detrended to remove first-order drift and DC
components. Sub-band coherence analysis of sEEG
was performed for the frequency band lower than 15Hz.
Beta band (15~30Hz) is not included since coherence
values were not significant (<0.2). Coherence is
calculated in each band, and four coherence matrices,
one per frequency band: Low delta (<2Hz), high
delta(2~4Hz) , theta (4~7Hz), and alpha band(7~15Hz) .
They are averaged to generate a sEEG low-frequency
band coherence matrix (threshold=0.2).
100 m
100 m
Cortical surface extraction of T1-weighted image is performed
using Brainsuite [1]. Skull-stripping, non-brain matter removal,
and motion correction of fMRI images are done in FSL [2]. The
fMRI images are co-registered to T1-weighted images using
Brainsuite BDP’s rigid registration algorithm (Fig. 3) [3]. Contacts
placed in white matter and cerebrospinal fluid (CSF) were
removed automatically using a tissue classification process. 3 x
3 voxel ROIs, centered on each electrode contact were used for
analysis of the resting state data. The rs-fMRI correlations
among ROIs are analyzed using a zero-lag correlation because
fMRI data are filtered by the hemodynamic response and do not
contain the higher frequency components seen in sEEG.
Figure 6: Boxplot of correlation values in BA7, BA31, and BA40.
BA31-BA7 show the highest correlation value than others.
(a) (b) (c)
BA7
BA7
BA40
BA40
BA31 BA31
0.5
-0.2