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Abstract #3559

Atlas-Based Analysis of Resting State Functional Connectivity MRI

Andreia Vasconcellos Faria1,2, Suresh Joel1,3, Xiaoying Tang4, Peter vanZijl1,3, Michael Miller4, James Pekar1,3, Susumu Mori1

1Radiology, Johns Hopkins University, Baltimore, MD, United States; 2Radiology, State University of Campinas, Campinas, SP, Brazil; 3FM Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States; 4Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States

Resting state functional connectivity MRI (rsfc-MRI) is becoming widely-used for neuroscience studies. However, the identification of corresponding cortical areas across subjects is not straightforward and the pixel-to-pixel time-domain correlation is inherently very noisy. An Atlas-Based Approach (ABA), where an automated 3D segmentation is applied in each individual, reduces the dimensionality of the data and can be an alternative to evaluate functional connectivity. In this study we report on initial findings in functional brain connectivity and inter-session intra-subject reproducibility of the results obtained by applying an ABA on rsfc-MRI data acquired in two sessions from 21 normal volunteers