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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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