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Enhanced disease characterization through multi network functional normalization in fMRI.

Çetin MS, Khullar S, Damaraju E, Michael AM, Baum SA, Calhoun VD - Front Neurosci (2015)

Bottom Line: Conventionally, structural topology is used for spatial normalization during the pre-processing of fMRI.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of New Mexico Albuquerque, NM, USA ; The Mind Research Network Albuquerque, NM, USA.

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.

No MeSH data available.


Related in: MedlinePlus

An illustration of the subdivision of images in MN/4 matrices alongside decomposition of a 2 × 2 sub-matrix into approximation and detail bands.
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Figure 4: An illustration of the subdivision of images in MN/4 matrices alongside decomposition of a 2 × 2 sub-matrix into approximation and detail bands.

Mentions: Unique analysis operators (forward wavelet transform)—(ψ↑, ω↑) and synthesis operators (reconstruction)—(ψ↓, ω↓) are used for a single level decomposition scheme. Due to the low resolution of the fMRI data, we restricted to a dual-resolution fusion scheme, which is only one level of decomposition for fusion. Further experimentation with multi-resolution may prove to be useful in analyzing performance. Let X is an image in a signal space V0which can further be decomposed in to subspaces V1 (approximation) and W1(detail) at level 1 in the wavelet domain. Let X ∈ V0 is a set of gray values in a 2-D space of size M × N such that M and N are both even. Thus, X can be subdivided in several disjoint 2 × 2 sub-images or blocks resulting in a total of MN/4 matrices as shown in Figure 4. Through quadrature down sampling, the analysis and synthesis operators ψ↑: V0 → V1 and ω↑: V0 → W1 are defined analytically in Equations (1, 2).


Enhanced disease characterization through multi network functional normalization in fMRI.

Çetin MS, Khullar S, Damaraju E, Michael AM, Baum SA, Calhoun VD - Front Neurosci (2015)

An illustration of the subdivision of images in MN/4 matrices alongside decomposition of a 2 × 2 sub-matrix into approximation and detail bands.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4379901&req=5

Figure 4: An illustration of the subdivision of images in MN/4 matrices alongside decomposition of a 2 × 2 sub-matrix into approximation and detail bands.
Mentions: Unique analysis operators (forward wavelet transform)—(ψ↑, ω↑) and synthesis operators (reconstruction)—(ψ↓, ω↓) are used for a single level decomposition scheme. Due to the low resolution of the fMRI data, we restricted to a dual-resolution fusion scheme, which is only one level of decomposition for fusion. Further experimentation with multi-resolution may prove to be useful in analyzing performance. Let X is an image in a signal space V0which can further be decomposed in to subspaces V1 (approximation) and W1(detail) at level 1 in the wavelet domain. Let X ∈ V0 is a set of gray values in a 2-D space of size M × N such that M and N are both even. Thus, X can be subdivided in several disjoint 2 × 2 sub-images or blocks resulting in a total of MN/4 matrices as shown in Figure 4. Through quadrature down sampling, the analysis and synthesis operators ψ↑: V0 → V1 and ω↑: V0 → W1 are defined analytically in Equations (1, 2).

Bottom Line: Conventionally, structural topology is used for spatial normalization during the pre-processing of fMRI.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of New Mexico Albuquerque, NM, USA ; The Mind Research Network Albuquerque, NM, USA.

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.

No MeSH data available.


Related in: MedlinePlus