1. Strategy for EEG/fMRI fusion Thomas Vincent 1,2 thomas.vincent@cea.frwww.lnao.fr Neurospin 1: CEA/NeuroSpin/LNAO 2: IFR49 December 17, 2009

2. 2 /62 Signal characteristics EEG fMRI 500 Hz ↔ 2ms ~12 - 32 channels ~ 2cm 64x64x42 voxels TR ~ 2sec. 3x3x3 mm surfacic volumic

3. 3 /62 fMRI/EEG fusion, why ? Complementarity of temporal and spatial resolutions Improve detection and/or estimation More challenging: reveal unexpected dynamical characteristics → Not available from separate fMRI and EEG analyses

4. 4 /62 Common spatial support: the cortical surface EEG: “direct” projection of electrode signals ● Scalp surface is available from T1 MRI ● → better registration

5. 5 /62 Common spatial support: the cortical surface fMRI: projection through a kernel depending on the cortical curvature ● Inherent smoothing ? ● Partial volume effect ● Projection ambiguity → one activation may span 2 gyrii → may be informed by EEG → model uncertainty in projector kernel [Operto et al 2008]

6. 6 /62 Physiology  [HBo/r] PA  flow

7. 7 /62 fMRI / EEG joint analysis Asymmetric approach: EEG source reconstruction informed by fMRI analysis or fMRI informed by EEG events Onsets given by interictal spikes Constraint on the variance/covariance matrix expressed from the estimated hemodynamic parameters estimated by JDE Symmetric approach: Model transient neuronal signal between stimulus onset signal and the hemodynamic response. Spatio-temporal decoupling, identify a common spatial support Inspired from the balloon model (physiological models) [Daunizeau et al 2007] [Makni et al 2009] [Daunizeau et al 2005] [M. Dojat] [Sotero et al 2007, Donnet 2005]

8. 8 /62 physiological model: Metabolic Hemodynamic Model (MHM) [Sotero et al 2007]

9. 9 /62 physiological model: Metabolic Hemodynamic Model (MHM) [Sotero et al 2007] Identifiability, estimability issues Poorly parsimonious Enough data for a proper estimation ? May be simplified Linearisation Merge some compartments

10. 10 /62 Bayesian Dynamical System [Makni et al 2009] More parsimonious Extend established detection/estimation framework state- space EEG BOLD noise

11. 11 /62 Asymetric 1: EEG → fMRI Interictal spikes detection Use these events within design matrix in JDE Localization informed by EEG events Unused EEG dynamics Unused EEG spatial information → maybe work at the parcel level → 2 stages parcellation: electrod distribution Functional hemodynamic parcellation

12. 12 /5 Asymetric 2: fMRI → EEG t t t PyHRF contrasts HRF Source reconstruction Hemodynamic features Prior constraint EEG fMRI [Daunizeau et al 2005]

13. 13 /62 Perspective Training period on asymmetric approach for epilepsy Bayesian Dynamical System: spatial and temporal fusion Impact of fMRI signal projection...

14. 14 /62 Surface analysis - localizer Gyrii parcellation

15. 15 /62 Surface analysis - localizer Effect maps Auditive sentenceVertical checker-board

16. 16 /62 Surface analysis - localizer Audit. (Computation – Sentence) Normalized contrast

17. 17 /62 Surface analysis - localizer Estimated hemodynamic parameters Time-to-peak Width at half maximum

18. 18 /62 Ongoing works Group analysis SPM / JDE comparison Impact of antenna choice (12 or 32 channels) on statistical sensitivity in fMRI Whole brain acquisitions at different spatial resolutions: 64x64 up to 128x128 Development of surface analysis tools Parcellation Visualization tools GLM, JDE fMRI/EEG data fusion Asymmetric approach Symmetric approach – Bayesian dynamical systems