Abstract #3110
Partial least squares regression of dynamic functional connectivity and EEG reveals the epileptic network activity
Maria Giulia Preti 1,2 , Nora Leonardi 1,2 , F. Isik Karahanoglu 1,2 , Frdric Grouiller 3 , Mlanie Genetti 4 , Margitta Seeck 5 , Serge Vulliemoz 5 , and Dimitri Van De Ville 1,2
1
Institute of Bioengineering, Ecole
Polytechnique Fdrale de Lausanne (EPFL), Lausanne, VD,
Switzerland,
2
Medical
Imaging Processing Lab, University of Geneva, Geneva,
Switzerland,
3
Department
of Radiology and Medical Informatics, Geneva University
Hospitals, Geneva, Switzerland,
4
Functional
Brain Mapping Lab, University Hospital and Faculty of
Medicine of Geneva, Geneva, Switzerland,
5
EEG
and Epilepsy Unit, Neurology and Functional Brain
Mapping Lab, University Hospital and Faculty of Medicine
of Geneva, Geneva, Switzerland
Focal epilepsy is characterized by a not yet fully
understood abnormal brain network organization that can
be addressed with the integration of EEG, revealing the
epileptic activity, and dynamic functional connectivity,
exploring the connections dynamics during resting-state
functional magnetic resonance imaging. We proposed a new
method to combine the two techniques using partial least
squares regression, aiming to assess the functional
subnetworks related to epileptic activity. Results for
one subject were consistent with previous literature,
encouraging a new spectrum of future analysis with this
method.
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