Abstract #4155
Dynamics of functional and effective brain connectivity better predicts disease state compared to traditional static connectivity
Gopikrishna Deshpande 1,2 , Hao Jia 1 , Xiaoping Hu 3 , Changfeng Jin 4 , Lingjiang Li 4 , and Tianming Liu 5
1
MRI Research Center, Department of
Electrical and Computer Engineering, Auburn University,
Auburn, Alabama, United States,
2
Department
of Psychology, Auburn University, Auburn, Alabama,
United States,
3
Biomedical
Imaging Technology Center, Coulter Department of
Biomedical Engineering, Georgia Institute of Technology
and Emory University, Atlanta, Georgia, United States,
4
The
Mental Health Institute, The Second Xiangya Hospital,
Central South University, Changsha, China,
5
Department
of Computer Science, University of Georgia, Athens,
Georgia, United States
It is acknowledged that functional connectivity (FC) in
the brain obtained from resting state fMRI dynamically
changes with time. Further, it has been shown that
dynamic changes in FC and effective connectivity (EC)
are relevant to disease processes. However, an
outstanding question that remains is whether dynamic
information from FC and EC provide increased sensitivity
for identifying brain pathologies in addition to that
obtained by static connectivity metrics? Here, we
provide answers to these questions by demonstrating that
information from temporal variations in FC and EC
provides better accuracy for classifying subjects with
PTSD (post-traumatic stress disorder) from healthy
controls.
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