Abstract #0595
Dynamic brain states sequential modelling based on spontaneous brain activity of resting-state fMRI
Shiyang Chen 1 , Jason Langley 1 , and Xiaoping Hu 1
1
The Wallace H. Coulter Department of
Biomedical Engineering, Georgia Institute of Technology
and Emory University, Atlanta, GA, United States
Most dynamic functional connectivity analyses are
performed using sliding window correlation. One problem
is that a fixed sliding window with a predefined length
selected ad hoc is used even though the temporal
duration of the states is now known to vary. In order to
address this challenge, we introduced a Gaussian Hidden
Markov Model to model brain state transition with the
time series of the fMRI data (in contrast to the method
which models the functional connectivity states). This
model allows us to detect the spatial patterns of states
and the transition sequences of the states. In our
study, we detected 9 reproducible brain states as
combination of conventional resting state networks.
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