Abstract #4331

Spatio-Temporal Association Between Simultaneously BOLD and FDG Resting State Networks

Shenpeng Li^1,2, Sharna D. Jamadar^1,3,4, Phillip G.D. Ward^1,3,4, Malin Premaratne², Gary F. Egan^1,3,4, and Zhaolin Chen^1,2

¹Monash Biomedical Imaging, Monash University, Clayton, Australia, ²Department of Electrical and Computer System Engineering, Monash University, Clayton, Australia, ³Monash Institute for Cognitive and Clinical Neuroscience, Monash University, Clayton, Australia, ⁴Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne, Australia

The slow-infusion-based dynamic FDG-PET have demonstrated excellent sensitivity to glucose uptake in the brain. Using fPET which contains unprecedented temporal information, we introduce a cross-modality spatiotemporal regression method to measure the association of resting net works (RSNs) between simultaneously acquired fMRI and fPET datasets. By projecting both temporal and spatial information in RSNs from two modalities into the proposed cross-modality association index, several associated networks have been identified by the proposed method.

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