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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Autores principales:	Longfei Su, Lubin Wang, Fanglin Chen, Hui Shen, Baojuan Li, Dewen Hu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8a40ca70578d49f1bb90fe3fa6e3a98d
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spelling	oai:doaj.org-article:8a40ca70578d49f1bb90fe3fa6e3a98d2021-11-18T07:19:25ZSparse representation of brain aging: extracting covariance patterns from structural MRI.1932-620310.1371/journal.pone.0036147https://doaj.org/article/8a40ca70578d49f1bb90fe3fa6e3a98d2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22590522/?tool=EBIhttps://doaj.org/toc/1932-6203An 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.Longfei SuLubin WangFanglin ChenHui ShenBaojuan LiDewen HuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 5, p e36147 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Longfei Su Lubin Wang Fanglin Chen Hui Shen Baojuan Li Dewen Hu 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.
format	article
author	Longfei Su Lubin Wang Fanglin Chen Hui Shen Baojuan Li Dewen Hu
author_facet	Longfei Su Lubin Wang Fanglin Chen Hui Shen Baojuan Li Dewen Hu
author_sort	Longfei Su
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.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/8a40ca70578d49f1bb90fe3fa6e3a98d
work_keys_str_mv	AT longfeisu sparserepresentationofbrainagingextractingcovariancepatternsfromstructuralmri AT lubinwang sparserepresentationofbrainagingextractingcovariancepatternsfromstructuralmri AT fanglinchen sparserepresentationofbrainagingextractingcovariancepatternsfromstructuralmri AT huishen sparserepresentationofbrainagingextractingcovariancepatternsfromstructuralmri AT baojuanli sparserepresentationofbrainagingextractingcovariancepatternsfromstructuralmri AT dewenhu sparserepresentationofbrainagingextractingcovariancepatternsfromstructuralmri
_version_	1718423609536413696

Sparse representation of brain aging: extracting covariance patterns from structural MRI.

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