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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