Incorporating higher dimensions in joint decomposition of EEG- fMRI Wout Swinnen, BIOMED KU Leuven.

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Presentation transcript:

Incorporating higher dimensions in joint decomposition of EEG- fMRI Wout Swinnen, BIOMED KU Leuven

Introduction: EEG and fMRI EEGfMRI

Introduction: EEG and fMRI

JointICA

(B. Mijovic, 2013)  Proved that assumption is correct But one EEG channel only 

Incorporate multiple channels in JointICA

Materials Visual detection task (Mijovic, 2013) Down-left visual stimulus Press of button 18 subjects, fMRI and EEG read non-simultaneously Preprocessing: o EEG  ERP’s Averaged and interpolated o fMRI  PSC maps Using SPM software and contrasting fMRI signal after stimulus vs background

Results: JointICA

Results: sJointICA

First 18 IC’s of 54 for electrode set [PO7,Oz,PO8] Results: ERP parts of the IC’s make up smaller time resolution But ERP phenomenon described is less natural (narrow peak) and more hard to interpret

Results: tJointICA

All 18 IC’s for electrode set [PO7,Oz,PO8] Results: Only strongest ERP characteristics described (such as N1) No good IC’s for weaker ERP characteristics (such as P1,...)

Conclusions JointICA: Meaningful decomposition showing underlying physiological mechanisms, but only 1 channel sJointICA: o Results are more difficult to interpret tJointICA: o IC’s show more pronounced and robust fMRI sources for certain patterns of ERP activity o More information on strong ERP characteristics at expense of weak ERP characteristics

Questions?