Title
Persistent Homological Sparse Network Approach to Detecting White Matter Abnormality in Maltreated Children: MRI and DTI Multimodal Study.
Abstract
We present a novel persistent homological sparse network analysis framework for characterizing white matter abnormalities in tensor-based morphometry (TBM) in magnetic resonance imaging (MRI). Traditionally TBM is used in quantifying tissue volume change in each voxel in a massive univariate fashion. However, this obvious approach cannot be used in testing, for instance, if the change in one voxel is related to other voxels. To address this limitation of univariate-TBM, we propose a new persistent homological approach to testing more complex relational hypotheses across brain regions. The proposed methods are applied to characterize abnormal white matter in maltreated children. The results are further validated using fractional anisotropy (FA) values in diffusion tensor imaging (DTI).
Year
Venue
Keywords
2013
Lecture Notes in Computer Science
diffusion tensor imaging,algorithms
Field
DocType
Volume
Voxel,Computer vision,Diffusion MRI,White matter,Tensor,Pattern recognition,Computer science,Fractional anisotropy,Abnormality,Artificial intelligence,Univariate,Magnetic resonance imaging
Conference
8149
Issue
ISSN
Citations 
Pt 1
0302-9743
2
PageRank 
References 
Authors
0.41
4
7
Name
Order
Citations
PageRank
Moo K. Chung170760.36
Jamie L Hanson291.61
Hyekyoung Lee336327.31
Nagesh Adluru420820.57
Andrew L Alexander537540.59
Richard J. Davidson647850.39
Seth D Pollak792.29