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The aim of this project is to improve simultaneous EEG-fMRI techniques through the use of constrained principal component analysis (CPCA). The main problem with combining EEG measures and fMRI measures is that recording EEG in the MR scanner always injects noise into the data. CPCA partitions the variability of the data into systematic and error variance. By using CPCA, a technique optimized in the Woodward lab, we can greatly reduce the noise in the signal (by dropping the error variance), and thus greatly improve our ability to merge the two modalities (Metzak, 2011; Metzak, 2012; Woodward, 2006, Woodward, 2013) The proposed research program will explore this question using the newly installed GE scanner at the Child and Family Research Imaging Facility at Children's & Women's Health Centre of BC, and the newly purchased MR-compatible EEG system from EGI. We intend to create a protocol for collecting and analyzing data using these new systems, and then use that protocol to start looking for neural disease makers, using an available obsessive-compulsive disorder (OCD) group of participants. Both GE Healthcare and EGI will benefit greatly from this research, as both companies want to be on the leading edge of imaging and psychophysiology.
Drs. Todd Woodward, Evelyn Stewart & Mario Liotti
Fern Jaspers-Fayer, Killian Kleffner-Canucci, Paul Metzak & Juliana Negreiros
Children's & Women's Health Centre of British Columbia
Psychology
Medical devices
Simon Fraser University
Accelerate
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