Using multiple diffusion MRI measures to predict alzheimer’s disease with a TV-L1 prior

The Alzheimer’S Disease Neuroimaging Initiative (ADNI)

doi:10.1007/978-3-319-54130-3_13

Using multiple diffusion MRI measures to predict alzheimer’s disease with a TV-L1 prior

The Alzheimer’S Disease Neuroimaging Initiative (ADNI)

Radiology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Microstructural measures from diffusion MRI have been used for classification purposes in neurodegenerative and psychiatric conditions. Novel diffusion reconstruction models can lead to better and more accurate measures of tissue properties: each measure provides different information on white matter microstructure in the brain, revealing different signs of disease. The diversity of computable measures makes it necessary to develop novel classification procedures to capture all of the available information from each measure. Here we introduce a multichannel regularized logistic regression algorithm that classifies individuals’ diagnostic status based on several microstructural measures, derived from their diffusion MRI scans. With the aid of a TV-L1 prior, which ensures sparsity in the classification model, the resulting linear models point to the most classifying brain regions for each of the diffusion MRI measures, giving the method additional descriptive power. We apply our regularized regression approach to classify Alzheimer’s disease patients and healthy controls in the ADNI dataset, based on their diffusion MRI data.

Original language	English (US)
Title of host publication	Computational Diffusion MRI - MICCAI Workshop
Editors	Andrea Fuster, Yogesh Rathi, Marco Reisert, Enrico Kaden, Aurobrata Ghosh
Publisher	Springer Heidelberg
Pages	157-166
Number of pages	10
ISBN (Print)	9783319541297
DOIs	https://doi.org/10.1007/978-3-319-54130-3_13
State	Published - 2017
Event	MICCAI Workshop on Computational Diffusion MRI, CDMRI 2016 - Athens, Greece Duration: Oct 17 2016 → Oct 21 2016

Publication series

Name	Mathematics and Visualization
ISSN (Print)	1612-3786
ISSN (Electronic)	2197-666X

Other

Other	MICCAI Workshop on Computational Diffusion MRI, CDMRI 2016
Country/Territory	Greece
City	Athens
Period	10/17/16 → 10/21/16

ASJC Scopus subject areas

Modeling and Simulation
Geometry and Topology
Computer Graphics and Computer-Aided Design
Applied Mathematics

Access to Document

10.1007/978-3-319-54130-3_13

Cite this

The Alzheimer’S Disease Neuroimaging Initiative (ADNI) (2017). Using multiple diffusion MRI measures to predict alzheimer’s disease with a TV-L1 prior. In A. Fuster, Y. Rathi, M. Reisert, E. Kaden, & A. Ghosh (Eds.), Computational Diffusion MRI - MICCAI Workshop (pp. 157-166). (Mathematics and Visualization). Springer Heidelberg. https://doi.org/10.1007/978-3-319-54130-3_13

Using multiple diffusion MRI measures to predict alzheimer’s disease with a TV-L1 prior. / The Alzheimer’S Disease Neuroimaging Initiative (ADNI).
Computational Diffusion MRI - MICCAI Workshop. ed. / Andrea Fuster; Yogesh Rathi; Marco Reisert; Enrico Kaden; Aurobrata Ghosh. Springer Heidelberg, 2017. p. 157-166 (Mathematics and Visualization).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

The Alzheimer’S Disease Neuroimaging Initiative (ADNI) 2017, Using multiple diffusion MRI measures to predict alzheimer’s disease with a TV-L1 prior. in A Fuster, Y Rathi, M Reisert, E Kaden & A Ghosh (eds), Computational Diffusion MRI - MICCAI Workshop. Mathematics and Visualization, Springer Heidelberg, pp. 157-166, MICCAI Workshop on Computational Diffusion MRI, CDMRI 2016, Athens, Greece, 10/17/16. https://doi.org/10.1007/978-3-319-54130-3_13

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abstract = "Microstructural measures from diffusion MRI have been used for classification purposes in neurodegenerative and psychiatric conditions. Novel diffusion reconstruction models can lead to better and more accurate measures of tissue properties: each measure provides different information on white matter microstructure in the brain, revealing different signs of disease. The diversity of computable measures makes it necessary to develop novel classification procedures to capture all of the available information from each measure. Here we introduce a multichannel regularized logistic regression algorithm that classifies individuals{\textquoteright} diagnostic status based on several microstructural measures, derived from their diffusion MRI scans. With the aid of a TV-L1 prior, which ensures sparsity in the classification model, the resulting linear models point to the most classifying brain regions for each of the diffusion MRI measures, giving the method additional descriptive power. We apply our regularized regression approach to classify Alzheimer{\textquoteright}s disease patients and healthy controls in the ADNI dataset, based on their diffusion MRI data.",

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doi = "10.1007/978-3-319-54130-3_13",

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AU - Gutman, Boris A.

AU - Jahanshad, Neda

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N2 - Microstructural measures from diffusion MRI have been used for classification purposes in neurodegenerative and psychiatric conditions. Novel diffusion reconstruction models can lead to better and more accurate measures of tissue properties: each measure provides different information on white matter microstructure in the brain, revealing different signs of disease. The diversity of computable measures makes it necessary to develop novel classification procedures to capture all of the available information from each measure. Here we introduce a multichannel regularized logistic regression algorithm that classifies individuals’ diagnostic status based on several microstructural measures, derived from their diffusion MRI scans. With the aid of a TV-L1 prior, which ensures sparsity in the classification model, the resulting linear models point to the most classifying brain regions for each of the diffusion MRI measures, giving the method additional descriptive power. We apply our regularized regression approach to classify Alzheimer’s disease patients and healthy controls in the ADNI dataset, based on their diffusion MRI data.

AB - Microstructural measures from diffusion MRI have been used for classification purposes in neurodegenerative and psychiatric conditions. Novel diffusion reconstruction models can lead to better and more accurate measures of tissue properties: each measure provides different information on white matter microstructure in the brain, revealing different signs of disease. The diversity of computable measures makes it necessary to develop novel classification procedures to capture all of the available information from each measure. Here we introduce a multichannel regularized logistic regression algorithm that classifies individuals’ diagnostic status based on several microstructural measures, derived from their diffusion MRI scans. With the aid of a TV-L1 prior, which ensures sparsity in the classification model, the resulting linear models point to the most classifying brain regions for each of the diffusion MRI measures, giving the method additional descriptive power. We apply our regularized regression approach to classify Alzheimer’s disease patients and healthy controls in the ADNI dataset, based on their diffusion MRI data.

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Using multiple diffusion MRI measures to predict alzheimer’s disease with a TV-L1 prior

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