NeuroImage 66 (2013) 457–468

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Cortex-based inter-subject analysis of iEEG and fMRI data sets: Application tosustained task-related BOLD and gamma responses

Fabrizio Esposito a,⁎,1, Neomi Singer b,c,1, Ilana Podlipsky c, Itzhak Fried d,e,f,Talma Hendler b,c,e, Rainer Goebel a,g

a Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlandsb Psychology Department, Tel Aviv University, Tel Aviv, Israelc Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israeld Functional Neurosurgery Unit, Tel Aviv Medical Center, Tel Aviv, Israele Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israelf Division of Neurosurgery, David Geffen School of Medicine and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, USAg Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences (KNAW), Amsterdam, The Netherlands

⁎ Corresponding author at: Department of Cognitive Nversity, P.O. Box 616, 6200 MD Maastricht, Maastrich433884125.

E-mail address: fabrizio.esposito@maastrichtunivers1 Equal contribution.

1053-8119/$ – see front matter © 2012 Elsevier Inc. Allhttp://dx.doi.org/10.1016/j.neuroimage.2012.10.080

a b s t r a c t

a r t i c l e i n f o

Article history:Accepted 29 October 2012Available online 6 November 2012

Keywords:Intra-cranial EEGfMRICombined intra-cranial EEG-fMRIMulti-modal imagingDistributed source modelingBOLDGamma

Linking regional metabolic changes with fluctuations in the local electromagnetic fields directly on thesurface of the human cerebral cortex is of tremendous importance for a better understanding of detailedbrain processes. Functional magnetic resonance imaging (fMRI) and intra-cranial electro-encephalography(iEEG) measure two technically unrelated but spatially and temporally complementary sets of functionaldescriptions of human brain activity. In order to allow fine-grained spatio-temporal human brain mappingat the population-level, an effective comparative framework for the cortex-based inter-subject analysis ofiEEG and fMRI data sets is needed.We combined fMRI and iEEG recordings of the same patients with epilepsy during alternated intervals of pas-sive movie viewing and music listening to explore the degree of local spatial correspondence and temporalcoupling between blood oxygen level dependent (BOLD) fMRI changes and iEEG spectral power modulationsacross the cortical surface after cortex-based inter-subject alignment. To this purpose, we applied a simplemodel of the iEEG activity spread around each electrode location and the cortex-based inter-subject align-ment procedure to transform discrete iEEG measurements into cortically distributed group patterns byestablishing a fine anatomic correspondence of many iEEG cortical sites across multiple subjects.Our results demonstrate the feasibility of a multi-modal inter-subject cortex-based distributed analysis forcombining iEEG and fMRI data sets acquired from multiple subjects with the same experimental paradigmbut with different iEEG electrode coverage. The proposed iEEG–fMRI framework allows for improved groupstatistics in a common anatomical space and preserves the dynamic link between the temporal features ofthe two modalities.

© 2012 Elsevier Inc. All rights reserved.

Introduction

Functional magnetic resonance imaging (fMRI) and intra-cranialelectro-encephalography (iEEG) are highly complementary neuromagingmodalities. In fact, while fMRI allows to continuously map metabolic(hemodynamic) changes of neural origin across the entire brain withspatial resolution in the order of a few millimeters, iEEG allows re-cording electrical signals with similar or higher spatial resolutionbut only from a limited number of discrete sites (Lachaux et al.,

euroscience, Maastricht Uni-t, The Netherlands. Fax: +31

ity.nl (F. Esposito).

rights reserved.

2003). On the contrary, while fMRI signals have time constants inthe order of a few seconds (Bandettini et al., 1992), iEEG can berecorded with high temporal resolution (from 200 Hz up to30 kHz) and can therefore reflect any complex organization in timeand frequency of the underlying oscillatory neural signals (Lachauxet al., 2003). Thereby, combining and optimally fusing the two tech-niques into one brain mapping procedure is an exciting challenge formany neuroscientists and clinical researchers.

Compared to scalp EEG, the combination of iEEG with fMRI posesdifferent problems and presents both advantages and disadvantages.IEEG data can be collected from electrodes placed directly on the corti-cal surface of surgically implanted patients. This allows reaching themesoscale resolution of a few millimeters that renders this modalityspatially comparable with fMRI. Scalp EEG does not achieve the samespatial resolution of fMRI due to the finite number of electrodes that

458 F. Esposito et al. / NeuroImage 66 (2013) 457–468

can be placed on the head of a subject and to other theoretical andphysical issues related to electromagnetic field propagation and volumeconduction (Acar et al., 2008; Mosher et al., 1999). On the other hand,scalp EEG can be safely, routinely and simultaneously measured withfMRI in healthy individuals, with the additional possibility of fullyexploiting the temporal coupling between the two modalities(Esposito et al., 2009a, 2009b) whereas iEEG electrodes can only beimplanted in the brain of some exceptional patients (typically withepilepsy) and the selection of the electrode sites, aswell as the durationof the implantations, are solely determined on pure clinical groundswithout any reference to possible specific requirements of a given func-tional study or standard coverage. This aspect implies that pooling iEEGand fMRI data sets frommultiple patients preserving as much as possi-ble of the native spatial resolution is made particularly difficult andchallenging by the need to ensure similar functional coverage and com-parable anatomical correspondence of the measured brain activity.

Small neural populations that become activated during time win-dows of a few seconds may cause blood oxygen level dependent(BOLD) signals measurable with fMRI and electrical field perturba-tions measurable with iEEG over the same cortical patch of a few mil-limeters (Logothetis and Pfeuffer, 2004). Like for EEG, locallymeasured electrophysiological responses, typically called local fieldpotentials (LFP) can be either described as event-related potentialsor as (de)synchronizations in predefined frequency bands, typicallyincluding delta (0–3 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta(15–30 Hz) and gamma (>30 Hz) ranges (Crone et al., 1998). Manyanimal and human iEEG studies have previously explored the relationof these iEEG components to the BOLD signal and all have shown aclose correspondence between the BOLD signal and the gammaband component of the LFP in the cerebral cortex (Conner et al.,2011; Hermes et al., 2012; Khursheed et al., 2011; Lachaux et al.,2007; Logothetis and Pfeuffer, 2004; Mukamel et al., 2005; Nir et al.,2007; Ojemann et al., 2010; Privman et al., 2007). In humans, bothperceptual, motor and cognitive neural processes have been shownto be accompanied by power increases in the gamma band in brainsites matching quite precisely with the anatomical organization ofthe functional networks as visible with fMRI during the same (or sim-ilar) paradigms (Niessing et al., 2005), whereas the anatomical local-ization of other types of iEEG responses such as desynchronizations inalpha and beta bands did not match as neatly as the gamma responseswith the cortical functional anatomy (Crone et al., 1998). Thereby, aclose anatomical correspondence exists between functional corticalnetworks revealed by fMRI and iEEG gamma band responses. SinceiEEG offers the unique opportunity to measure electrophysiologicalactivity from small cortical regions with a spatial precision equivalentto or higher than that of fMRI, studying the same patients with bothtechniques in the same days using the same experimental paradigmsgathers the attractive possibility of comparing the two modalities inhumans (Lachaux et al., 2007).

In the present study, we collected iEEG and fMRI data sets fromeight patients with chronically intractable epilepsy using the sameblocked experimental design and focused on the question of wheth-er BOLD signal variations and iEEG gamma spectral perturbationsinduced by simple passive movie viewing and music listeningpresented compatible anatomical distributions in a common corti-cally aligned brain space already used for fMRI population studies(Goebel et al., 2006).

In fMRI studies the use of a common brain space potentially offersa much more powerful group-level functional data analysis due to asubstantially improved anatomical alignment, which also improvesthe alignment of homologous functional regions (Frost and Goebel,2012). However, since gyri and sulci are not well aligned after stan-dard Talairach or Montreal Neurological Institute (MNI) normaliza-tion procedures, suboptimal iEEG–fMRI fusion results may beobtained if iEEG electrode positions precisely located in active voxelsinside the native space of individual subjects will correspond to

non-active voxels of other subjects due to pure alignment issues. Inthis work, after initial registration between individual iEEG electrodesand MRI brain volumes and after extracting the cortical surfaces, acortex-based alignment scheme (Fischl et al., 1999) was applied toexplicitly align corresponding gyri and sulci across subjects andallow fitting with maximal precision all iEEG electrodes from all sub-jects to the same target cortical space. Moreover, in order to take intoaccount the actual spread of LFP cortical activity in both an anatomi-cally and physiologically principled way, a simple distributed fieldforward model was applied; the developed field forward model con-verts the spatially discrete iEEG signals to continuously distributedsignals across the cerebral cortex. According to this model, all pointsof electrical contact between each iEEG electrode and the surround-ing brain tissue were assumed to record a weighted sum of activitiesfrom sources of electric field present in the entire brain volume withthe weight of each source decreasing with the square of the mutualdistance (Morris and Luders, 1985). Thereby, a close anatomical rela-tionship between iEEG and fMRI modalities would imply that corticalpatches exhibiting strong gamma band energy increases should spa-tially overlap with cortical patches with fMRI activation based onthe contrast comparing stimulated and resting experimental condi-tions (see, e. g., Mukamel et al., 2005). We tested precisely this pre-diction in a cortex-based inter-subject analysis of the visual andauditory iEEG gamma and BOLD fMRI responses.

Materials and methods

Participants

Recordings were obtained from ten neurosurgical patients sufferingfrom medically intractable epilepsy who were evaluated for possiblesurgery (Mage 31±7 years, 4 males; see Table 1 for more demographicand clinical details). The patients were recruited from the neurosurgerydepartment at the Tel-Aviv Sourasky medical center following theirclinical assignment for subdural electrode implantation (for approxi-mately 1 week) as part of their pre-surgical evaluation (Dewar et al.,1996). All patients provided written informed consent according tothe Tel Aviv Sourasky Medical Center institutional review board (IRB)committee guidelines prior to the experiment. Data of two patientswere excluded from analysis due to evidence of a vast rightparieto-temporo-occipital dysplasia and heterotrophy in one patient(P03, age 25 y, female, 47 iEEG channels) and due to equipment failureduring data acquisition in the second patient (P05, age 38, male). Afterthese elimination processes, the final study cohort was comprised ofeight patients, implanted with a total of 443 iEEG channels (Mage 31±7 years, 3 males).

iEEG acquisition

Each patient was implanted with subdural electrode arrayscontaining between 32 and 74 contact electrodes (Ad-tech, Racine,WI, USA). Electrodes were arranged in one-dimensional strips or intwo dimensional grids placed directly on the cortical surface. Eachelectrode was 2 mm in diameter, with 8 mm spacing between adja-cent electrodes. IEEG recordings in patients 01–08 were sampled at200 Hz and electronically filtered between 1 and 70 Hz using a 32channel Telefactor EEG system (Grass Technologies, West Warwick,RI, USA). In patients 09 and 10 recordings were sampled at 256 Hzusing a 64 channels Nicolet system (CareFusion, Middelton, WI,USA) but the same filter of 70 Hz was applied. Referential recordingswere referenced to an extra-cranial electrode. The choice of implantsites was based on purely clinical considerations about the suspectedlocations of seizure origin with no reference to the present experi-mental protocol.

Table1

M—

male,

F—

female,

R—

Righ

t;L—

left;T—

tempo

ral;Fr

—fron

tal,O

—oc

cipital,P—

parietal,M

TS—

multiplesclerosis,med

.—med

ial,mes.—

mesial.Ch

an.—

chan

nels.

Patien

tAge

Gen

der

Han

dedn

ess

#ch

an.

contacts

Chan

.loc

ation

(hem

isph

ere)

distribu

tion

ofch

anne

lloc

ation

perlobe

(%)

#ch

an.inan

alysis

#sessionus

edfM

RIscan

SeizureOns

etRe

section

MRI

Eviden

ceforLe

sion

Gen

eral

cogn

itive

func

tion

ing

TFr

OP

0123

MR

62R

400

4713

301

VR.

OP

R.OP

–Normal

0235

MR

32L

5041

09

182

VLFr.T

–LT

Normal

0325

FR

47R+

L26

3423

17–

––

Diffus

ive;

R+L

–RPT

OBe

llow

averag

e04

18F

L74

R54

1120

1551

2V

ROP

R.OP

RTLmed

.TNormal

0538

MR

48R+

L75

170

8–

––

Diffus

ive;

RFr.T

––

Bello

wav

erag

e06

39F

R44

R70

140

1633

2V

RT

R.T

–Normal

0727

FR

56R+

L66

921

440

2V

RT

––

Normal–low

0835

FL

47R+

L13

790

940

1V

Lmes.Fr

Lmes.Fr

–Normal

0933

FR

60R+

L55

2020

547

1V

Rmed

.TRmed

.TRMTS

Normal

1036

MR

68R

5610

925

512

VRFr.T

––

Normal

Ave

rage

30.9

53.8

5124

1412

38.75

S.E

2.26

3.97

0.06

0.07

0.05

0.02

4.03

Total

4M

538

274

112

8567

310

6F

459F. Esposito et al. / NeuroImage 66 (2013) 457–468

Localization of iEEG channels

The electrode contacts were identified on each individual stereo-taxic scheme. In addition, computer-assisted co-registration ofpost-implantation CT-scan with a pre-implantation 3-DMRI provideda direct visualization of electrode contacts with respect to eachpatient's brain anatomy. Using BrainVoyager QX software package(Brain Innovation, Maastricht, The Netherlands), the individual pa-tient CT volume was co-registered to the T1-weighted MRI that wasthen used to reconstruct the pial cortical surface. Specifically, thepost-operative individual patient 3D-CT images were first resampledto the same iso-voxel 1×1×1 mm resolution of 3D-T1 MRI imagesusing a cubic spline interpolation. Then, the three image dimensionswere re-oriented to match the native sagittal orientation of the3D-T1 MRI images. Finally, a manual co-registration between theMRI and CT was performed by interactively trimming the six param-eters of a rigid body transformation. The center of each electrode wasidentified from its high intensity levels on the CT volume and ulti-mately projected to the reconstructed cortical surface. To correct forpossible deformation of electrode position due to brain shift, eachelectrode was projected to the closest point on the mesh.

The spatial coverage of the electrodes was different in each patientand is summarized in Table 1. In average, 51% of the electrodes werelocated in the temporal lobe, 24% in the frontal, 14% in the occipitaland 12% in the parietal lobes.

MRI acquisition

Prior to the implantation of iEEG channels, all participantsunderwent an fMRI scan, which was performed on a 3-T GE scanner(GE, Milwaukee, WI, USA) equipped with echo-planar imaging (EPI)acquisition. Images were acquired in an interleaved fashion using an8 channel birdcage head coil and a single-shot echo-planarT2*-weighted sequence. The main MR acquisition parameters ofthis sequence were: 128×128 matrix; field of view of 20×20 cm;39 slices with 3 mm thickness and no gap. TR/TE 3000/35; flipangle 90°; acquisition time per slice 76 ms; acquisition orientationwas of the AC–PC plane. Functional images of patients 01, 02 and03 were acquired using a standard four-channel head coil andusing the following parameters: 64×64 matrix; field of view of20×20 cm; 44–45 slices with 3 mm thickness and no gap. TR/TE3000/35; flip angle 90°; acquisition time per slice 68 ms; acquisitionorientation was of the AC–PC plane. Positioning of the image planeswas performed on scout images acquired in the sagittal plane.

Subsequent to the functional scan, a high resolution T1-weighted 3Danatomical MR scan was obtained using the following parameters:256×256 matrix; field of view of 25×25 cm. In patient 09 the fieldof view was of 25.6×25.6 cm and in patient 07, of 24×24 cm; all ofthe 3D-T1 MRI images were resampled to the same iso-voxel1×1×1 mm resolution using a cubic spline interpolation. All fMRIsessions were conducted between 0 and 13 days before the iEEG ses-sions. One patient has undergone the fMRI session three monthsprior to the iEEG session.

Experimental design and procedure

The experimental design that was developed by Eldar et al. (2007)was used; each subject was presented with 12 film clips, 12 music clipsand 12 combinations of music and film clips (the combinations werenot used in the present paper). The stimuli were presented as 12 sepochs separated by 9 s blank epochs (gray screen). All conditionswere order-balanced and pseudo-randomly distributed both withinand between subjects (for further details, see Eldar et al., 2007). Toallow for exact synchronization between the stimuli and iEEG data,electrical pulses were given along with the stimuli and were used astriggers of condition onset. All iEEG sessions were conducted at least

460 F. Esposito et al. / NeuroImage 66 (2013) 457–468

two days after implantation and at least 6 h after a seizure at thepatient's quiet bedside while the patient was sitting upright. Duringthe iEEG sessions, stimuli were presented using Presentation software(Neurobehavioral systems, Inc.) via a standard laptop screen andspeakers in one or two runs of 13 min. During the fMRI scans thesame stimuli were presented in one run of 13 min using Presentationsoftware running on a standard desktop computer. The stimuli wereprojected to a screen located at the back of the scanner. Subjectsviewed the stimuli through a mirror that was placed on the upperpart of the head coil in front of the subjects' eyes and listened to thestimuli using MR compatible headsets. Participants were instructed tofocus on the presented stimuli and to naturally experience it.

IEEG data analysis

Channel-level iEEG data preparation and preprocessing wasperformed in EEGLAB (http://sccn.ucsd.edu/eeglab/), running onMatlab 7 or later (www.mathworks.com). Recording sites showingsigns of ictal epileptiform activities (as decided by professional neu-rologist) were excluded from the analysis. Preprocessing included:1) Removal of electrodes that were either disconnected or presentedepileptiform activity as identified by a visual inspection. This proce-dure resulted in the removal of a total of 133 iEEG channels from theanalysis, leaving data from 310 channels gathered from eight pa-tients. 2) Application of global noise filtering by subtraction of themean signal across all electrodes from each electrode. This procedurediscards non-neuronal contributions related to the extra-cranial ref-erence electrode, which may have affected all intracranial channels,including the 50 Hz peak. Since the iEEG recordings of patients 09and 10 were sampled at 256 Hz, we have initially down sampledthese data sets to 200 Hz.

Matlab scripts were used to export iEEG data sets and stimulustrigger information to the EEG-MEG module of BrainVoyager QXwhere channel time courses were segmented in single trialsextending from 5 s before to 15 s after the onset of each interval ofstimulation (lasting 12 s) and, for each trial, time-frequency data(TFD) representations were obtained with the short-time fast fouriertransform (ST-FFT) approach (time resolution 1 s, time window 1 sand maximum frequency 70 Hz). In the context of both single pa-tient and group analyses, the baseline-normalized magnitudes ofTFD data were displayed as maximum-scaled time-frequencyimage plots. For spatial mapping of the iEEG gamma responses, thesurface-projected (see below) TFD were analyzed in the gamma band(40–70 Hz). To this purpose, the single-trial magnitudes of theprojected TFD values were averaged across all time-frequency bins ofthis band, within both the post-stimulus (target) and the pre-stimulus(baseline) interval, and a trial-averaged gamma response for each ex-perimental condition was calculated as the t value resulting from thea two-sample unpaired t test between the target and baseline intervals.This was done for all subjects and both experimental conditions. For thegroup-level analysis, the individual t-values were first converted toz-scores (to correct for the different number of trials across subjectsor conditions) and then entered a random-effects analysis on the com-mon cortical space (see below). In this space, the effective number ofsubjects contributing to each vertex was also varying and thereforewe computed the statistical tests with a spatially varying number of de-grees of freedom at each vertex. To account for the different degrees offreedom in space, the resulting t-maps expressing the iEEG gamma ef-fects for each condition versus baseline were finally converted toz-score maps using the correct degrees of freedom at each vertex. Dueto the low number of subjects and the multi-modal descriptive andcomparative nature of this analysis, thresholding of the resulting corti-cal maps was performed at p=0.05 without correction for multiplecomparisons.

In order to perform a correlation analysis between iEEG and fMRIdata (coupling) in selected regions of interest, additional TFDs were

computed from each individual iEEG data set after segmenting thecontinuous iEEG time-series into short epochs (corresponding toeach fMRI TR). The new epochs were placed along the same virtualtime line of the fMRI paradigm, with the beginning of the epoch cor-responding to the beginning of the fMRI slab acquisition, and thetotal number of epochs being equal to the number of fMRI time points.In this case, the surface-projected (see below) TFDwere analyzed into 5separate bands: delta (0–4 Hz), theta (4–8 Hz), alpha (8–15 Hz), beta(15–30 Hz), and gamma (40–70 Hz). For each band and each epoch(time point), the single-trial magnitudes of the projected TFD valueswere averaged across all time-frequency bins and applied a standardhemodynamic correction (see below), thereby band-specific iEEGtime-courses were produced for iEEG-fMRI correlation analysis.

fMRI data analysis

The functional image time-series from each patient werepreprocessed using the standard sequence of fMRI preprocessingsteps. Slice scan time correctionwas performed using sinc interpolation,head 3-D motion correction was performed by spatial aligning all vol-umes to the first volume via rigid body transformations using sinc inter-polation, and linear trend removal and temporal high-pass filteringwere applied to each voxel time-course to remove linear and nonlinearlow-frequency drifts of 3 or fewer cycles per time course (high passcut-off~0.004 Hz). No low-pass temporal and spatial filters were usedbefore the statistical analysis, which was based on a standard generallinear model (GLM) approach (Friston et al., 1995).

The GLM design matrix was prepared with one predictor per con-dition type (movie viewing, music listening, combined stimulation);in order to account for hemodynamic delay and dispersion, each ofthese predictors was derived by convolution of an appropriatebox-car waveform with a double-gamma hemodynamic responsefunction (Friston et al., 1998). Fitting of the GLM was performed byaccounting for the effects of temporal serial correlations (Bullmoreet al., 1996). Based on these fits, group-level random-effect t-mapsfor the movie and music conditions were generated using a standardtwo-level (hierarchical) ordinary least squares (OLS) fit procedure(Mumford and Nichols, 2009). Due to the low number of subjectsand the multi-modal descriptive and comparative nature of this anal-ysis, thresholding of the resulting cortical maps was performed at p=0.05 without correction for multiple comparisons.

Finally, in order to assess the temporal (dynamic) correspondencebetween the two imaging modalities beyond the spatial correspon-dence, the regional iEEG-fMRI correlations (coupling)were also estimat-ed. To this purpose, for selected cortical patches, another GLM analysiswas performed, using five iEEG-derived band-specific time-courses(see above) as fMRI predictors. As the fMRI time-courses are stored asarbitrary values, before entering the GLM, both the fMRI and theiEEG-derived time-courses were scaled to their temporal z-scores(i. e. demeaned and divided by the standard deviation).

Cortex-based inter-subject iEEG–fMRI distributed analysis

In order to perform the cortex-based data analysis, the white/gray matter boundary was segmented from the normalized MRI T1images using automatic segmentation routines (Kriegeskorte andGoebel, 2001), with additional manual correction of any possible re-sidual topological errors wherever needed. Before extracting theimage boundary, the segmented white matter images were dilatedto reach the outer gray matter profile. A high resolution surfacemesh was finally reconstructed based on this profile and used inboth the surface-based analysis of fMRI data and the cortex-basedalignment (CBA) procedure (Goebel et al., 2006). For the surface-based analysis of fMRI data, in order to preserve as much as possiblethe original lay-out of the fMRI activity, and exploiting the fact CBAmakes smoothing not strictly necessary in cortex-based group fMRI

461F. Esposito et al. / NeuroImage 66 (2013) 457–468

analyses (Frost and Goebel, 2012, but see also other CBA-fMRI appli-cations), we did not apply any smoothing along the cortical surface.Thereby, the coregistered functional data were simply interpolatedon the positions corresponding to the nodes of the cortex meshesat the white/grey matter boundary plus an outward shift along thesurface normal direction to account for cortical thickness (Andradeet al., 2001).

CBA requires several steps which have been all described in de-tail elsewhere (Goebel et al., 2006). Ultimately, the CBA output isrepresented by an “average” cortex mesh (also called “target”mesh) that contains almost the same level of anatomical detail aseach of the individual cortex meshes but with all gyri and sulci max-imally corresponding as evaluated by landmark labeling (Frost andGoebel, 2012). The established spatial correspondence betweenmeshes allows mapping each location on the individual cortex toone or more locations of the target mesh and is used to align iEEGelectrode and fMRI time courses.

After registration to the individual MRI volumes, all iEEG elec-trodes were further registered to the individual cortical anatomyby fitting each electrode to its closest point on the cortex mesh.Then, given the discrete nature of iEEG, a simple model of the neuralsource activity spread around each electrode site was employedwhere each vertex of this mesh represented a new virtual intracrani-al electrode for probing the neural source responses.

To implement this model, the simple formula was used:

xv t; fð Þ ¼XM

i¼1

wiv⋅yi t; fð Þ

where:

xv t; fð Þ→iEEG activity at mesh vertex v time bin t and frequency bin fð Þyi t; fð Þ→iEEG activity at electrode i time bin t and frequency bin fð Þwiv→Weighting function of electrode� to� vertex

distance→ 1−div=dmaxð Þk div≤dmax0 div>dmax

n

According to this model, the iEEG time-frequency activity wasspread to the entire mesh by generating distributed activity estimatesin the form of iEEG surface maps which could be further pooled acrosssubjects on the target mesh in the CBA framework. Particularly, thismathematical formulation was chosen to flexibly increase theweighting of the surface vertices that are closer to the electrode posi-tions while ensuring both a “compact” support (i. e. the weight is ex-actly zero a distance equal or higher than dmax) and a spatialcontinuity at the boundary (i. e. the weight approaches zero whenthe distance approaches dmax) of the spread area, as shown by the com-parison of the function profiles for the quadratic case (i. e. k=2) with atypical Gaussian kernel (see, e. g., Miller et al., 2007b) of equivalentfull-width-at-half-maximum (FWHM) presented in SupplementaryFig. S1.

In our model, the parameters dmax and k allow to respectivelycontrol the radius and the attenuation of the activity spreadaround each electrode. In fact, the weight w for any given vertexand iEEG electrode is 1 only if that electrode is fitted to exactlythat vertex, otherwise is less than 1 and vanishes at a distance far-ther than dmax. Fig. 1 illustrates this model of iEEG activity on oneindividual subject for the quadratic kernel. For a selected electrodein the occipital cortex, different choices (0.5, 1 and 2 cm) for dmax

are shown (Fig. 1a–c). The quadratic spread of many electrodes(using different colors for the different electrodes) throughoutthe right hemisphere is shown in Fig. 1(d) to provide an illustra-tion of the actual continuous coverage achieved for one subject as-suming dmax=1 cm. For any given choice of these parameters, acontinuous representation of the electrode density of the whole

sample of patients throughout the target mesh was obtained asthe “equivalent number of iEEG electrode” at each vertex:

ED vð Þ ¼ ∑patients

XM patientð Þ

i¼1

w patientð Þiv

For all subjects in the present analysis we used dmax=1 cm andk=2 that allow for an optimal coverage of the cortex with miminalinter-electrode overlap and correspond very well with the empiricalvalues reported in the literature (Lachaux et al., 2003; Menon et al.,1996; Morris and Luders, 1985). To empirically verify this choice,we considered two representative case subjects for which morethan one electrode was located within 15 mm of an area of significantfMRI activity (Khursheed et al., 2011) and evaluated the overlap be-tween this area and the area of significant gamma iEEG activityresulting from the cortical spread, at multiple values of dmax (0.5,1.0, 1.5 and 2.0 cm) for iEEG and multiple p-values for fMRI (p=0.001, 0.01, 0.05). The overlap was calculated as the (percent) ratiobetween the vertex counts of the intersection and the vertex countsof the union, of the two patches of significant gamma iEEG (p=0.05) and significant fMRI activity (p=0.05, 0.01 and 0.001).

Results

Effect of the activity spread radius and kernel type on iEEG-fMRI spatialcorrespondence

Before applying the electrode spread model, we empirically veri-fied the effect of the spread model parameter dmax in two subjects(P01 and P04), for which we had more than one electrode showingsignificant (music-related) gamma iEEG activity in close proximitywith significant (music-related) fMRI activity. We verified that thechoice dmax=1 cm produced the best overlap between the fMRIand gamma iEEG activity maps (Supplementary Figs. S2 and S3 re-spectively for patients P01 and P04). We additionally wished to com-pare the quadratic kernel to a standard Gaussian kernel (Miller et al.,2007b), by estimating the overlap between the fMRI and gamma iEEGactivity maps with the FWHM of the Gaussian spread function set tothe same value of dmax. As can be seen, in one case (P01) the quadrat-ic model outperforms the Gaussian model when the size is set to10 mm, whereas in the other case (P04), the performances are equiv-alent when the size is set to 10 mm or 15 mm. Hence, the quadratickernel can be as good as (if not better than) the Gaussian kernel.

Condition-specific gamma activity at the individual level

We first wished to examine the spatial distribution of music orfilm-induced gamma activity at the individual level. Two patients(P03, P05) were excluded from the analysis and thus were not con-sidered in the following analyses. In the remaining eight subjects, cor-tical sites with statistically significant (individual t-tests, pb0.05)differences between target and baseline intervals in the gammaband (Crone et al., 2006) were observed for at least one of the two ex-perimental conditions. All these cases are shown in Figs. 2 and 3. Fig. 2shows the iEEG electrodes on the individual cortical meshes, the cor-tical patches that presented significant sustained gamma activity dur-ing music listening and the normalized time-frequency plots for bothexperimental conditions in the most responsive iEEG channel. In fourpatients (P01, P04, P06, P10) significant gamma power increase wasobserved during periods of music listening and this effect was accom-panied by power suppression in the low frequency bands. The gammaactivity was mostly confined in patches along the right superior tem-poral gyrus (STG) in the right auditory cortex. Contrariwise, no signif-icant gamma activity patches were detected in the left temporalcortex during music listening.

Fig. 1. Illustration of the iEEG (cortically constrained) distributed model on an individual subject. (a–c) Effect of changing dmax for one electrode: (a) dmax=0.5, (b) dmax=1.0 cm, (c) dmax=2.0 cm. (d) Result of mapping activity of all electrodes on the cortex using the simple activity spreading model (dmax=1.0 cm).

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Fig. 3 shows the iEEG electrodes on the individual cortical meshes,the cortical patches that presented significant sustained gamma activityduring movie viewing and the normalized time-frequency plots forboth experimental conditions in the most responsive iEEG channel. Infour patients (P01, P02, P07, P09) significant gamma power increasewas observed during periods of movie viewing in the ventral occipitalcortex, in either one hemisphere only (right visual cortex in P01 andP07 and left visual cortex in P02) or bi-laterally (P09).

Hence, gamma power selectivity tofilm ormusic can be seen at sitesin visual or auditory regions, respectively, albeit not in all patients andat all expected sites. To rule out possible confounding effects ofinter-ictal activity, we subsequently verified, based on inspection of anexperienced neurologist that no signs of inter-ictal epileptiform activitywere evident during the task in the sites reported here.

iEEG and fMRI condition-specific effects at the group level: application ofCBA

Having highlighted region-restricted gamma band selectivity atthe individual level, we further wished to generalize these findingsand to enhance their statistical validity by demonstrating similar ef-fects at the group level. For that, we performed a group analysis ofthe condition dependent induced gamma activity. This was achievedby applying CBA to the eight patients that completed both iEEG andfMRI sessions and by re-analysing the iEEG data in the gamma bandfrom all subjects. For comparison, the same analysis was performedwithout applying the CBA procedure, thus keeping the original volu-metric alignment gathered by the standard Talairach space normali-zation. Group-level gamma activity is shown on the CBA targetcortex mesh in the left panel of Fig. 4, after individual fitting to the in-dividual cortex mesh and with or without subsequent group-level

adjustment based on the same CBA anatomical correspondence. Thedirect comparisons of each stimulus type relative to baseline revealeda condition specific and spatially restricted gamma activity increase.Specifically, enhanced gamma activity in response to music was re-stricted to the (right) auditory areas in temporal cortex, whereas en-hanced response to film was evident in ventral visual areas. None ofthe channels that were highlighted by this analysis showed signs ofinter-ictal epileptiform activity during the task, as determined by aprofessional neurologist.

The observed iEEG group effects obtained with and without CBAwere then compared with fMRI effects. Fig. 4 (right panel) showsthe group-level fMRI activity modulation by stimulation type(random-effects omnibus F-test of themain effects of music listeningandmovie viewing stimulus type, pb0.05 uncorrected) after CBA ap-plied to all eight subjects that completed both iEEG and fMRI ses-sions. Group-level fMRI activity is shown on the CBA target cortexmesh with andwithout iEEG electrodes (displayed in different colorsfor different patients) after individual fitting to the individual cortexmesh and subsequent group-level re-fitting based on the same CBAanatomical correspondence. Similarly to the gamma activity distri-bution, extended BOLD activity is observed in primary perceptionauditory and visual areas, respectively in the occipital and temporalcortex. With respect to the main group-level fMRI activity, the iEEGcoverage appears to be much better in the temporal (compared tooccipital) cortex and in the right (compared to the left) hemisphere(see also Fig. 5a and the text bellow for further details). This coverageasymmetry can explain the relatively small effect of film inducedgamma activation in the visual areas in the occipital lobe. Moreover,compared to the simple volumetric alignment without CBA, thegamma group activation effects obtained with the CBA applicationappear more compact and more similar to the corresponding fMRI

Fig. 3. IEEG gamma activity in four patients with statistically significant sustained gamma power change during movie viewing (p=0.05).

Fig. 2. IEEG gamma activity in four patients with statistically significant sustained gamma power change during music listening (p=0.05).

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Fig. 4. Left panel. Group-level random-effects z-maps of CBA-aligned and non-CBA-aligned distributed iEEG gamma activity (n=8) on the target cortex mesh (right view) showingthe regions where the activity significantly increases in response to film (a, a′) or music (b, b′) relative to baseline (p=0.05). Right panel. Group-level random-effects fMRI activity(F-map, p=0.05, main effect of stimulus presentation, uncorrected) on the target cortex mesh (left, right and bottom views) without (a, b, c) and with (a′, b′, c′) iEEG electrodesaligned on the target cortex mesh. Notice the resemblance between fMRI and iEEG activity maps in the region densely covered with channels (circled in red), particularly for theCBA-aligned map.

Fig. 5. Group-level representation of iEEG gamma activity (n=8 patients) on the target cortex mesh. (a) Electrode density distribution visualized from the more left and more rightlateralized views. (b) Group fMRI activity patch overlaid together with individual iEEG gamma activity patches from 4 patients. (c) Group-level random-effects distributed iEEGgamma activity (F-map, p=0.05, main effect of stimulus presentation, uncorrected), with the patch used for the iEEG–fMRI coupling analysis contour-marked in black.(d) Group-level random-effects iEEG–fMRI coupling in five canonical bands (GLM fits “beta” with standard errors) in the selected patch (the asterisk denotes a level of significancepb0.05).

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effects in the auditory cortex, which is the region where the densityof iEEG electrodes is maximal.

In order to quantify the iEEG coverage in a more continuous fash-ion with a surface map, the iEEG electrode density group map wascomputed on the CBA target cortex and displayed in Fig. 5a. In thissurface map the higher the values the better the studied populationis virtually covered by the iEEG measurement. As expected, highervalues are observed along the right superior temporal gyrus whereasthe group-level coverage is poorer in the occipital cortex (especiallyin the primary visual cortex) and in the left temporal cortex. Fig. 5ballows comparing individual aligned patches of iEEG gamma activitywith the fMRI activity after CBA application to each individual iEEGpatch with the patch of fMRI activity in the right temporal cortex.As can be seen in Fig. 5c, the density of electrodes in this region, cor-responds to the patch of statistically significant group-level main ef-fect of stimulus presentation type on iEEG gamma activity (random-effects omnibus F-test of the main effects of music listening andmovie viewing stimulus type, pb0.05 uncorrected).

We next wished to test, whether beyond the spatial co-localization,there is a temporal correspondence between the fMRI and iEEG region-al signals in all canonical bands. For that, we correlated these signals ina selected patch in the auditory cortex around the peak of the gammaiEEG activity (see the contour-marked patch in Fig. 5c). Fig. 5d showsthe iEEG-fMRI correlations in this spot (group-level GLM beta withstandard errors), confirming that the (condition-independent) cou-pling of cortical iEEG and fMRI continuous activity is positive and max-imally significant in the gamma band, even if positive and negativecorrelations are found respectively in the alpha and beta bands.

We further investigated this link between signals by plotting the spec-tral profile of the auditory or visual regions, inwhich a significant fMRI ac-tivity was evident in response to music or film, respectively. Fig. 6demonstrates the normalized group averaged time-frequency plots thatwere obtained from either the fMRI selective visual or auditory regions.As can be seen, the different regions present condition dependent spectralprofiles; whereas in the visual area enhanced sustained gamma power(along with diminished low-frequency power) is selectively associatedwith the film condition, similar spectral profile is associated with the au-ditory area during the music stimulation.

Discussion

The objective of this work was to determine whether iEEG and fMRIdata sets from multiple subjects undergoing an iEEG and fMRI session

Fig. 6. Time frequency plots averaged across patients depicting the iEEG responses from thconditions (fixed effects, n=8, ntrials=153). The visual and auditory regions of interest for tthat their activity was significantly enhanced by music listening or movie viewing relative

with the same experimental paradigm can be suitably combined in agroup-level cortex-based distributed analysis framework. Dykstra etal. (2012) have recently proposed to combine the individualized locali-zation and the cortex-based registration of iEEG electrodes, demon-strating how this can improve the anatomical labeling of the activatedregions when displayed in single patient cortical maps. Differentlyfrom, and complementary to this work, here we explored the concreteapplication of CBA registration to a group-level cortical analysis, usingboth iEEG and fMRI data from the same patients performing the sameexperiment in bothmodalities. To achieve this goal, we first introduceda simple continuous forward field model of the iEEG activity spreadaround each electrode location to transform intrinsically discrete iEEGmeasurements into cortically distributed patterns, and, then, appliedCBA to obtain maximal anatomic correspondences between individualcortical sites of iEEG and fMRI activity across multiple subjects.

The electrophysiological model presented here for transformingiEEG spatial patterns from discrete to distributed representations didnot incorporate detailed aspects of geometry and field propagation asis normally done for scalp EEG forward modeling (Mosher et al.,1999). Nonetheless, we provided a simple parametrical formula thatallowed the rapid empirical exploration of the most practical aspectsof iEEG that are essential inmulti-modal comparative analysis involvingfMRI, namely the spread of activity and the profile of influence for eachsingle electrode in the surrounding cortical tissue. It should beremarked, however, that, like for similar models commonly used foriEEG interpolation (see, e. g., Miller et al., 2007a; Pei et al., 2011), thisprocedure is merely an aid for obtaining cortically distributed patternsfrom discrete iEEG recordings, and, therefore, adds no extra spatial in-formation with respect to what is physically recorded. In other words,the ultimate spatial resolution of the iEEG data is solely determined bythe size of the electrodes and the inter-electrode distance (2 mm and8 mm, respectively). Nevertheless, instead of applying a typicalsmoothing Gaussian Kernel as in previous studies (see, e. g., Miller etal., 2007a), we applied here a simple quadratic model that is at leastas anatomically and physiologically plausible as the Gaussian kerneland used an optimal kernel of 1 cm that was both in accord with previ-ous reports (Lachaux et al., 2003; Menon et al., 1996) and empiricallyverified (see Supplementary Figures).

Neural source modeling could be in principle used with iEEG inexactly the same way as with scalp EEG, i.e. by solving the electro-magnetic forward problem and performing the source localization in acortex-based source space (Acar et al., 2008). For instance, Acar et al.(Acar et al., 2008, 2009) have shown that volume-conducted distal

e visual (top panel) and auditory (bottom panel) areas to film (left) or music (right)he plots were selected using the group fMRI data from the same patients, as the regionsto baseline, respectively (random-effects, pb0.05 uncorrected).

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and near-field proximal portions of data recorded from the humancortex can be separated and used to localize and visualize corticaliEEG sources, including those located within sulcal folds (Acar et al.,2008, 2009). Since we did not intend to model here the coupling ofthe two modalities from two different regions, but only the localspatio-temporal correspondences between the neural effects detectablein bothmodalities taken in isolation in a group-level cortex-based anal-ysis, we did not test any classical forward and inverse EEG sourcemodeling schemes but outline that the possible incorporation of suchschemes would only pertain to the distributed modeling of the corticalsources of iEEG signals and will be thus possible in future iEEG studiesusing the same cortex-based analysis framework presented here.

Starting from available previous knowledge that gamma bandenergy modulations can impose local hemodynamic signal variations(Niessing et al., 2005), a logical prediction was that in the presence ofinduced neural activation, iEEG recording sites would show increasesin gammaband energymainly in the vicinity of fMRI activation clusters.At the population level, we also expected that a higher (resp. lower)density of iEEG electrodes in the true regions of activity would resultin an increased (resp. decreased) likelihood to observe consistent ef-fects in the iEEG gamma and fMRI BOLD responses under the same ex-perimental conditions. We tested this prediction by, first, identifyingiEEG sites and corresponding cortical patcheswith significantly inducedenergy modulation in the gamma band and, second, by assessing thespatial overlap existing between iEEG gamma and fMRI BOLD patchesof activity resulting from group-level random-effects analyses in thecommon CBA target cortical space.

At the individual level, a sustained iEEG gamma activity evoked bypassive auditory and visual perception was observed in half of the pa-tients, respectively in the right STG and in peripheral parts of the occip-ital cortex. In addition to the sustained gamma power augmentation,and in agreementwith previous findings, a similarly sustained suppres-sion of low frequency activity in the alpha/beta range was observed,possibly reflecting the known thalamo-cortical gating mechanism thatfacilitates cortical processing (Crone et al., 2001a, 2001b).

During music listening, four out of eight patients exhibited aug-mented gamma power in very close spots on the right STG, whereasduring movie viewing four patients exhibited augmented gammapower along the ventral stream of visual processing. In the volumespace all these patches with iEEG gamma activity were part of a moreextended fMRI cluster exhibiting significant fMRI BOLD activity, butnone of these overlapwith each otherwhen backprojecting the patchesfrom the individual cortical space to the standard Talairach space.

No significant patches of iEEG activity were detected in the lefttemporal cortex during music listening and in the early visual cortexduring movie viewing, even if the patches of significant fMRI activitywidely encompassed these primary perception regions. This resultcould be explained by representing iEEG electrodes overlaid withfMRI activity on the same CBA space revealing that the iEEG coveragewas substantially worse in these regions in the left temporal cortexcompared to the highly dense coverage of the right STG, although,this could also be due to the known advantage of music processingin the right hemisphere (Herdener et al., 2010; Zatorre et al.,2007). This iEEG coverage was even more effectively represented interms of the iEEG electrode density group map computed on the CBAtarget cortex that quantified how the studied population representa-tive brain was reasonably well covered by iEEG measurements alongthe right STG but rather poorly covered in the occipital cortex (espe-cially in the primary visual cortex) and in the left temporal cortex(see Fig. 5a). Thereby, we could predict that pooling the individualpatches or even individual time-frequency data from all eight sub-jects on the CBA target cortex will produce the maximal overlapand the highest statistical significance in the right STG.

The novel application of the CBA framework to integrate iEEGdata into one cortically aligned distributed group analysis has provedeffective for the iEEG–fMRI comparative analysis by producing an

overlapping patch of statistically significant gamma activity afterpooling all eight subjects in one random-effects omnibus F-test,despite that one half of the subjects did not exhibit this effect on anindividual level and that another half of the subjects produced activ-ity in neighboring but not overlapping spots. Even if this frameworkneeds to be tested on a much higher number of patients, thepresented results clearly suggest that with adequate iEEG electrodecoverage, the spatial consistency of the underlying local fMRI andiEEG effects is preserved and can be improved in a common brainspace represented by the CBA-aligned group mesh, rather than (orin alternative to) in a template or atlas brain (see, e. g., David et al.,2011). This implies that, in future studies, fMRI could be well usedas a spatial predictor of iEEG signals by indicating those extendedcortical regions where iEEG sites have more chances to detectgamma band responses under stimulated conditions if a minimal“probabilistically” sensitive coverage is ensured. Of course, as theiEEG implantation is guided by clinical considerations, this spatialprediction can only be used “post hoc” to qualify a single patient ora given cohort of patients for a specific study.

Even if iEEG and fMRI data were not acquired simultaneously (see,e. g., Carmichael et al., 2008, 2010; Vulliemoz et al., 2011), the use ofan identical paradigm in the same patients, allowed us exploring theiEEG–fMRI dynamic coupling (correlations) in all canonical bands. Aswe found that the obtained positive correlations in the gamma, aswell as negative correlations in the beta band, were also consistentwith our expectations (see, e. g., Conner et al., 2011; He et al., 2008;Hermes et al., 2012), we could also verify that not only the spatialbut also the temporal relationship between the two modalities (i. e.electrophysiology and BOLD) remains preserved after applying theproposed approaches (see Fig. 6).

Besides neuro-physical and anatomical aspects, there are sometechnical factors that could negatively affect the accuracy of thismethodology. First of all, the precise localization of the electrodesis strongly affected by possible CT-MRI misregistration and topolog-ical errors on the cortical mesh. Moreover, even in the absence ofthese imaging problems, the spatial precision of the iEEG remainsdependent on how precisely the localization of the electrodes isperformedwith respect to the real (rather thanmodeled) brain anat-omy and on howmuch tissue deformation (brain shift) is induced bythe electrode implantation (Lachaux et al., 2003). Brain shift variesstrongly from patient to patient, depending on the location andsize of craniotomy, the number and placement of electrodes, andthe presence and severity of any post-surgical swelling and the dis-tortions of the tissue relative to the cortical surface reconstructionscan reach the order of one centimeter (Khursheed et al., 2011).Therefore, our approach for electrode projection, which is identicalto that presented by Hermes and colleagues (Hermes et al., 2010)for the special case of electrode strips, could be not as accurate asin previous works based on more advanced and precise methods(see, e. g., Dalal et al., 2008; Dykstra et al., 2012; Hermes et al.,2010; Swann et al., 2009; Tertel et al., 2011). Finally, while the corti-cal surface of the brain is the usual and ideal target space of fMRIanalyses (see, e. g., Gunduz et al., 2012; Miller et al., 2007b), andprojecting the iEEG electrodes on the cortical surface permits theCBA application to correct the channel positions on the group aver-age cortical space, it should be also remarked that whenever theelectrodes are not placed accurately along a sulcus, as could be thecases for electrode grids or arrays, the possible bias for the projectionon the cortical surface is potentially higher than for the projection onthe envelope surface of the brain, which can be seen as a less biasedsolution for electrode grids or arrays.

Conclusions

In summary, our results support that the functional networks asrevealed by fMRI are spatially and temporally congruent with the

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neuro-electrical gamma activity in humans as reveled by iEEG. Obvi-ously, higher numbers of patients and more diverse experimentalconditions would be needed to corroborate the anatomical and func-tional models presented here in a wide range of brain explorationsand cognitive situations. Nonetheless, we were able to illustrate aconcrete framework for the integration of the two imaging ap-proaches in a common brain space, which may turn out to be veryhelpful to exploit their complementary nature and encourage furtherresearch efforts to combine fMRI, with both iEEG and scalp EEG (andalso MEG) through source-reconstruction algorithms and neural ef-fect coupling models (Esposito et al., 2009a, 2009b).

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.neuroimage.2012.10.080.

Acknowledgments

The authors would like to thank the patients for their cooperationin participating in this study. We also thank Prof. Miriam Neufeldand Dr. Svetlana Kipervasser for their clinical assistance and support;Dr. Mordekhay Medvedovsky for his aid in data assessment; Dr. HadasOkon Singer, Dr. Keren Rosenberg, Dr. Irit Lichter-Shapira Dr. DonnaAbecasis and Gadi Gilam for their assistance in iEEG recordings;David Yossef, Sari Nagar, Rivi Cohen, and the technicians of the EEGlab at Sourasky Medical Center for technical assistance. This workwas supported by the Israeli Science Foundation converging technol-ogies grant (ISF-1747/07) to T.H and I.F, by the European Union AC-TIVE grant (FP7-ICT-2009-270460) to T.H and by scholarships fromthe Israeli Council for Higher Education (converging technologies)and Levie-Edersheim-Gitter Institute for Functional Brain Mappingto N.S.

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