NDLI: Sparse model learning for high dimensional diffusion MRI data in traumatic brain injury

Content Provider	IEEE Xplore Digital Library
Author	Shaker, M. Erdogmus, D. Dy, J. Bouix, S.
Copyright Year	2015
Description	Author affiliation: Med. Sch., Brigham & Women's Hosp., Harvard Univ., Boston, MA, USA (Bouix, S.) \|\| ECE Dept., Northeastern Univ., Boston, MA, USA (Shaker, M.; Erdogmus, D.; Dy, J.)
Abstract	The estimation of multivariate distributions of from high dimensional data with low sample size is at the core of many image analysis applications. Here, we present a method to estimate a multivariate Gaussian distribution of diffusion tensor features in a set of brain regions based on 45 healthy individuals. This distribution is used to identify the imaging abnormalities in subjects with traumatic brain injury (TBI). The model receives apriori knowledge in the form of a neighborhood graph, and maximizes the likelihood of the distribution while putting an additional L1 sparsity constraint on the model parameters. This leads to an interpretable model where the interactions of brain regions are encoded into the estimated model coefficients. The likelihoods of normal and TBI subjects under the estimated distribution are used as features to evaluate discriminatory power of model, using the area under receiver operating characteristic (ROC) curves. Our experiments show that the addition of the neighborhood graph constraint results in significant improvements in classification compared to a model which does not take into account the brain region interactions. In addition, our prior graph leads to a better model than one using a fully connected prior graph as it provides similar classification performance with 10 times fewer parameters (model order), and significantly lower Bayesian information criterion (BIC).
Starting Page	1
Ending Page	6
File Size	151307
Page Count	6
File Format	PDF
ISBN	9781467374545
DOI	10.1109/MLSP.2015.7324375
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain modeling Diffusion tensor imaging Computational modeling Data models Brain injuries Estimation Covariance matrices
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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