Abstract #0837
Nodal centrality of the resting state functional network in the differentiation of schizophrenia using a support vector machine
Hu Cheng 1 , Sharlene Newman 1 , Jerillyn S. Kent 1 , Josselyn Howell 1 , Amanda Bolbecker 1 , Aina Puce 1 , Brian F. O'Donnell 1 , and William P. Hetrick 1
1
Indiana University, Bloomington, IN, United
States
Resting state functional networks with 278 cortical and
subcortical nodes were constructed on 19 schizophrenics
and 29 normal controls. By computing the nodal
centrality of the resting state functional network, we
show that the two groups can be differentiated using
support vector machine based on the order of centrality
for a certain number of nodes. The performance of the
differentiation is better for the nodes with higher
centrality in comparison to the same number of nodes
with lower centrality. This finding indicates that
change of network hubs may be associated with
schizophrenia.
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