Abstract #3462
IVA to detect spatial map differences between Schizophrenia patients and Healthy Controls
Shruti Gopal 1,2 , Robyn Miller 1 , Andrew Michael 1 , Mustafa Cetin 1 , Srinivas Rachakonda 1 , Stefi Baum 2 , and Vince Calhoun 1
1
The Mind Research Network, Albuquerque, NM,
United States,
2
Rochester
Institute of Technology, Rochester, NY, United States
The ability of independent vector analysis(IVA) to
preserve subject variability among network spatial maps
brings additional power to analyses of group differences
between healthy and patient populations for disorders in
which specific brain structures are believed to play
critical roles. We demonstrate the benefits of IVA over
group ICA in what we believe is the first application of
IVA to a clinical population. Our results indicate that
IVA is not only effective in identifying the networks
relevant to Schizophrenia such as basal ganglia,
superior temporal gyrus, visual cortex and the
sensorimotor network, but is also demonstrably better at
differentiating schizophrenia patients from controls
based exclusively on easily-assessed properties of the
network spatial maps.
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