Introduction Electroencephalography correlated functional Magnetic

Resonance Imaging (EEG-fMRI) is a multi-modal imaging technique with growing application in the clinical evaluation of epilepsy. [Mulert, Lemieux (Eds.), EEG-fMRI 2010]

A new approach to simultaneous EEG-fMRI data integration in epilepsy is introduced. Independent component analysis (ICA) is applied to EEG data and spectral based metrics are extracted in order to predict and localise fMRI BOLD signal changes related to seizure events.

Improved EEG-fMRI integration in epilepsy

RecPad2010 - 16th edition of the Portuguese Conference on Pattern Recognition, UTAD University, Vila Real city, October 29th

Marco Leite1, Alberto Leal2, João Sanches1, Patrícia Figueiredo1

1Instituto Superior Técnico / Instituto de Sistemas e Robótica, Lisboa, Portugal2Department of Neurophysiology, Hospital Júlio de Matos, Lisboa, Portugal

MethodsPatient description: The patient was undergoing an EEG-fMRI study

as part of pre-surgical evaluation. The patient suffered from gelastic epilepsy, having a giant hamartoma (focal malformation) [Leal et al., Epilepsia 2008].

EEG processing: EEG data were acquired using an MR-compatible 37-channel Neuroscan system and were pre-corrected for fMRI slice gradient and balistocardiographic artefacts, band-pass filtered 2~45 Hz and downsampled to 100Hz, using the EEGLAB FMRIB plugin (sccn.ucsd.edu/eeglab).

ICA was performed on the data and followed by time spectral analysis using Morlet wavelet decomposition.

Five metrics were applied to the resulting spectra [Rosa et al., NeuroImage 2010; Kilner et al., NeuroImage 2005]:

Total power:

Un-normalised mean frequency:

Un-normalised root-mean-square frequency:

Normalised mean-frequency:

Normalised root-mean-square frequency:

fMRI processing: fMRI data were collected on a 1.5T GE system and were analysed using FSL (www.fmrib.ox.ac.uk/fsl).

Pre-processing: motion corrected, slice time corrected, temporal high pass filtered rejecting periods above 100 s and spatially filtered with a Gaussian kernel with FWHM = 8 mm.

All EEG metrics were convolved with a canonical haemodynamic response function and included individually in fMRI data GLM analysis, along with their time derivative and the movement parameters as confounds.

T tests were performed voxelwise and cluster corrected for multiple comparisons using voxel Z>2.3 and a cluster p<0.05.

ResultsThe independent component that yielded the largest fMRI activation map (IC 10) is detailed below:

The activation maps and BOLD predictions obtained by the methodology proposed and by the boxcar regressor defined by the neurophysiologist are shown below:

ConclusionThe methodology presented here represents a novel approach to epilepsy EEG-fMRI data integration, by using direct metrics of EEG data in an attempt to include more information about the seizure dynamics in BOLD prediction. The results obtained show improved statistical power in the detection of seizure-related brain networks, which is of critical importance in EEG-fMRI studies in epilepsy.

Neurophysiologist’s regressor

IC 10 MF regressor

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Subject RR, Sequence 3

IC 10 MF regressor: 2.3 6.7 Neurophysiologist’s regressor: 2.3 4.9

Figure 4: Activation Z statistic maps (bottom) and corresponding average time courses, partial and full model fits (arbitrary units), plotted as a function of the volume number (top).

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Figure 1: Scalp map and spectrogram for the independent component yielding the largest fMRI activation map. The red boxes indicate the periods marked as seizures by the neurophysiologist.

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