Abstract #4159
Automatic Resting State Network Decomposition using ICA and Classification in a Clinical Population
Svyatoslav Vergun 1 , Wolfgang Gaggl 2 , Veena A Nair 2 , Rasmus M Birn 3 , M. Elizabeth Meyerand 3 , James Reuss 4 , Edgar A DeYoe 5 , and Vivek Prabhakaran 2
1
Medical Physics, UW-Madison, Madison, WI,
United States,
2
Radiology,
UW-Madison, WI, United States,
3
Medical
Physics, UW-Madison, WI, United States,
4
Prism
Clinical Imaging, Inc, WI, United States,
5
Radiology,
Medical College of Wisconsin, WI, United States
We present a clinically motivated, automated component
decomposition and classification method using resting
state functional MRI data of epilepsy and vascular/tumor
patients. Preprocessed resting state scans are
decomposed, with respect to their functional time series
signal, using spatial independent component analysis.
The resultant components are used in the classification
step in which they are spatially correlated with a
template compiled by a previous study. The automated
classifier achieved promising performance for the
visual, sensorimotor, default-mode and auditory
networks.
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