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Abstract #2092

Trends, seasonality, and persistence of resting-state fMRI over 185 weeks

Ann Sunah Choe 1,2 , Craig K Jones 3,4 , Suresh E Joel 3,4 , John Muschelli 5 , Visar Belegu 6,7 , Martin A Lindquist 5 , Brian S Caffo 5 , Peter CM van Zijl 3,4 , and James J Pekar 3,4

1 Radiology and radiological sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 2 F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, 3 Radiology and radiological sciences, Johns Hopkins School of Medicine, MD, United States, 4 F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, MD, United States, 5 Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, MD, United States, 6 Neurology, Johns Hopkins School of Medicine, MD, United States, 7 International Center for Spinal Cord Injury, Kennedy Krieger Institute, MD, United States

Despite strong interest in using resting state fMRI (rsfMRI) outcome measures as imaging biomarkers for clinical studies, the temporal structure (e.g., seasonality) of such measures is poorly understood. This study aimed to assess the existence of temporal structure in three commonly used rsfMRI outcomes measures; namely spatial map similarity, temporal fluctuation magnitude, and between-network connectivity. A unique longitudinal dataset reporting on one healthy adult subject scanned on a weekly basis over 185 weeks enabled timeseries analysis on the measures of interest. Results revealed significant linear trend, annual periodicity, and persistence in many resting state networks, for all outcome measures.

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