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. 2011;14(Pt 2):459-66.
doi: 10.1007/978-3-642-23629-7_56.

Pattern based morphometry

Bilwaj Gaonkar  1 , Kilian Pohl, Christos Davatzikos
Affiliations

Pattern based morphometry

Bilwaj Gaonkar et al. Med Image Comput Comput Assist Interv. 2011.

Abstract

Voxel based morphometry (VBM) is widely used in the neuroimaging community to infer group differences in brain morphology. VBM is effective in quantifying group differences highly localized in space. However it is not equally effective when group differences might be based on interactions between multiple brain networks. We address this by proposing a new framework called pattern based morphometry (PBM). PBM is a data driven technique. It uses a dictionary learning algorithm to extract global patterns that characterize group differences. We test this approach on simulated and real data obtained from ADNI. In both cases PBM is able to uncover complex global patterns effectively.

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Figures

Fig. 1
Fig. 1
PBM on simulated data. (a)simulated patterns introduced in the data(b)PBM results, basis ranked 1 and 2 for parameter settings r=3,K=2,T=1 (c)r=3,K=4,T=4 (d) r=3,K=7,T=2 (e)VBM t-statistic map corresponding to p-values not corrected for multiple testing.
Fig. 2
Fig. 2
The 5 top ranked basis/patterns obtained from PBM in WM from ADNI data
Fig. 3
Fig. 3
The 5 top ranked basis/patterns obtained from PBM in VN from ADNI data
Fig. 4
Fig. 4
The 5 top ranked basis/patterns obtained from PBM in GM from ADNI data
See this image and copyright information in PMC

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