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Abstract #1835

Identifying RSN-like spatially covarying sources in synaptic density PET (11C-UCB-J) with independent component analysis

Xiaotian T. Fang1, Takuya Toyonaga1, Ansel T. Hillmer1,2, Patrick D. Worhunsky2, and Richard E. Carson1
1Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States, 2Psychiatry, Yale School of Medicine, New Haven, CT, United States

Resting-state networks, functionally connected brain regions, are canonically investigated with resting-state functional-MRI. 11C-UCB-J is a recently developed PET tracer that binds and visualizes synaptic vesicle protein 2A. The aim is to identify networks of coherent 11C-UCB-J covariation patterns using independent component analysis. We find modest similarity between five 11C-UCB-J PET-based synaptic density sources and rs-fMRI template RSNs. In several sources, there are varying rates of age-related change in subject loading weight, consistent with functional changes in RSNs and age-related gray matter decline. Our findings support the independency of such networks and potential physiological links between synaptic density and brain function.

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