Abstract
Conventionally, structural topology is used for spatial normalization during the pre-processing 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 vs. rest which shows the existence of common spatial excitation patterns between these identified networks. Previous work (Khullar 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 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 toward 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.
Highlights
Spatial registration of multiple subjects onto a common template is a necessary step while analyzing functional MRI (fMRI) data across a group of subjects
The auditory oddball design (AOD) task stimulates a subject with three kinds of sounds: “standard” stimuli (1000 Hz tones with probability p = 0.8), infrequent target stimuli (1200 Hz tones, p = 0.1) and infrequent novel stimuli.The auditory stimuli were presented to each participant by a computer stimulus presentation system called visual and audio presentation package via insert earphones attached within a pair of 30-dB noise-canceling MR compatible headphones
For the purpose of demonstrating the possibility of using multi-network functional templates” (FT) with our proposed independent component analysis (ICA)-mfNORM framework, we extend the multi-focal image fusion scheme presented by De and Chanda (2006) to fMRI analysis
Summary
Spatial registration of multiple subjects onto a common template is a necessary step while analyzing fMRI data across a group of subjects This step is required for several reasons: (1) brains differ in shapes and sizes and spatial normalization enforces brain boundaries to. In fMRI we measure the blood oxygenation level dependent (BOLD) changes of brain activation and previous work (Mazziotta et al, 2001; Brett et al, 2002) has shown that structural and functional data may not have direct one-to-one correspondence. By this we mean functional activation patterns in a well-defined structural region can vary across subjects even for a well-defined functional task
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