Sparse Representation of Brain Aging: Extracting Covariance Patterns from Structural MRI

An enhanced understanding of how normal aging alters brain structure is urgently needed for the early diagnosis and treatment of age-related mental diseases. Structural magnetic resonance imaging (MRI) is a reliable technique used to detect age-related changes in the human brain. Currently, multivar...

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Main Authors:	Su, Longfei, Wang, Lubin, Chen, Fanglin, Shen, Hui, Li, Baojuan, Hu, Dewen
Format:	Online
Language:	English
Published:	Public Library of Science 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3348167/

id	pubmed-3348167
recordtype	oai_dc
spelling	pubmed-33481672012-05-15 Sparse Representation of Brain Aging: Extracting Covariance Patterns from Structural MRI Su, Longfei Wang, Lubin Chen, Fanglin Shen, Hui Li, Baojuan Hu, Dewen Research Article An enhanced understanding of how normal aging alters brain structure is urgently needed for the early diagnosis and treatment of age-related mental diseases. Structural magnetic resonance imaging (MRI) is a reliable technique used to detect age-related changes in the human brain. Currently, multivariate pattern analysis (MVPA) enables the exploration of subtle and distributed changes of data obtained from structural MRI images. In this study, a new MVPA approach based on sparse representation has been employed to investigate the anatomical covariance patterns of normal aging. Two groups of participants (group 1∶290 participants; group 2∶56 participants) were evaluated in this study. These two groups were scanned with two 1.5 T MRI machines. In the first group, we obtained the discriminative patterns using a t-test filter and sparse representation step. We were able to distinguish the young from old cohort with a very high accuracy using only a few voxels of the discriminative patterns (group 1∶98.4%; group 2∶96.4%). The experimental results showed that the selected voxels may be categorized into two components according to the two steps in the proposed method. The first component focuses on the precentral and postcentral gyri, and the caudate nucleus, which play an important role in sensorimotor tasks. The strongest volume reduction with age was observed in these clusters. The second component is mainly distributed over the cerebellum, thalamus, and right inferior frontal gyrus. These regions are not only critical nodes of the sensorimotor circuitry but also the cognitive circuitry although their volume shows a relative resilience against aging. Considering the voxels selection procedure, we suggest that the aging of the sensorimotor and cognitive brain regions identified in this study has a covarying relationship with each other. Public Library of Science 2012-05-08 /pmc/articles/PMC3348167/ /pubmed/22590522 http://dx.doi.org/10.1371/journal.pone.0036147 Text en Su et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Su, Longfei Wang, Lubin Chen, Fanglin Shen, Hui Li, Baojuan Hu, Dewen
spellingShingle	Su, Longfei Wang, Lubin Chen, Fanglin Shen, Hui Li, Baojuan Hu, Dewen Sparse Representation of Brain Aging: Extracting Covariance Patterns from Structural MRI
author_facet	Su, Longfei Wang, Lubin Chen, Fanglin Shen, Hui Li, Baojuan Hu, Dewen
author_sort	Su, Longfei
title	Sparse Representation of Brain Aging: Extracting Covariance Patterns from Structural MRI
title_short	Sparse Representation of Brain Aging: Extracting Covariance Patterns from Structural MRI
title_full	Sparse Representation of Brain Aging: Extracting Covariance Patterns from Structural MRI
title_fullStr	Sparse Representation of Brain Aging: Extracting Covariance Patterns from Structural MRI
title_full_unstemmed	Sparse Representation of Brain Aging: Extracting Covariance Patterns from Structural MRI
title_sort	sparse representation of brain aging: extracting covariance patterns from structural mri
description	An enhanced understanding of how normal aging alters brain structure is urgently needed for the early diagnosis and treatment of age-related mental diseases. Structural magnetic resonance imaging (MRI) is a reliable technique used to detect age-related changes in the human brain. Currently, multivariate pattern analysis (MVPA) enables the exploration of subtle and distributed changes of data obtained from structural MRI images. In this study, a new MVPA approach based on sparse representation has been employed to investigate the anatomical covariance patterns of normal aging. Two groups of participants (group 1∶290 participants; group 2∶56 participants) were evaluated in this study. These two groups were scanned with two 1.5 T MRI machines. In the first group, we obtained the discriminative patterns using a t-test filter and sparse representation step. We were able to distinguish the young from old cohort with a very high accuracy using only a few voxels of the discriminative patterns (group 1∶98.4%; group 2∶96.4%). The experimental results showed that the selected voxels may be categorized into two components according to the two steps in the proposed method. The first component focuses on the precentral and postcentral gyri, and the caudate nucleus, which play an important role in sensorimotor tasks. The strongest volume reduction with age was observed in these clusters. The second component is mainly distributed over the cerebellum, thalamus, and right inferior frontal gyrus. These regions are not only critical nodes of the sensorimotor circuitry but also the cognitive circuitry although their volume shows a relative resilience against aging. Considering the voxels selection procedure, we suggest that the aging of the sensorimotor and cognitive brain regions identified in this study has a covarying relationship with each other.
publisher	Public Library of Science
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3348167/
_version_	1611528377554960384

Sparse Representation of Brain Aging: Extracting Covariance Patterns from Structural MRI

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