Frontiers in Neuroscience (Mar 2015)

Enhanced disease characterization through multi network functional normalization in fMRI

  • Mustafa Sinan Cetin,
  • Siddharth eKhullar,
  • Eswar eDamaraju,
  • Andrew M Michael,
  • Stefi A. Baum,
  • Vince D Calhoun

DOI
https://doi.org/10.3389/fnins.2015.00095
Journal volume & issue
Vol. 9

Abstract

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Conventionally, structural topology is used for spatial normalization during the preprocessing of fMRI. The co-existence of multiple intrinsic networks which can be detected in the resting brain are well studied. Also, these networks exhibit temporal and spatial modulation during cognitive task versus rest which shows the existence of common spatial excitation patterns between these identified networks. Previous work (Khullar S et al., 2011) has shown that structural and functional data may not have direct one-to-one correspondence and functional activation patterns in a well-defined structural region can vary across subjects even for a well-defined functional task. The results of this study and the existence of the neural activity patterns in multiple networks motivates us to investigate multiple resting-state networks as a single fusion template for functional normalization for multi groups of subjects. We extend the previous approach (Khullar S et al., 2011) by co-registering multi group of subjects (healthy control and schizophrenia patients) and by utilizing multiple resting-state networks (instead of just one) as a single fusion template for functional normalization. In this paper we describe the initial steps towards using multiple resting-state networks as a single fusion template for functional normalization. A simple wavelet-based image fusion approach is presented in order to evaluate the feasibility of combining multiple functional networks. Our results showed improvements in both the significance of group statistics (healthy control and schizophrenia patients) and the spatial extent of activation when a multiple resting-state network applied as a single fusion template for functional normalization after the conventional structural normalization. Also, our results provided evidence that the improvement in significance of group statistics lead to better accuracy results for classification of healthy controls and schizophrenia patients.

Keywords