Title
Predicting the age of healthy adults from structural MRI by sparse representation
Abstract
It is generally accepted that degenerative brain diseases lead to abnormal aging process of the human brain. Thus, healthy brain aging model has great potential in clinical diagnosis and intervention. The aim of this work is to construct a regression model which is efficient for age prediction of healthy brain. Two groups of T1-weighted MRI images were involved. The first group was used for voxel selection then corresponding voxels in the second group were used for age prediction. Then mean absolute error (MAE) between the predicted age and the true age is obtained. The age prediction accuracy can reach as high as 4.67 years (MAE). In conclusion, the framework in current study can be a healthy aging model for abnormality detection of human brain. The brain regions identified by this model is sensitive to aging process which can be viewed as biomarker of brain age.
Year
DOI
Venue
2012
10.1007/978-3-642-36669-7_34
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Keywords
Field
DocType
healthy brain,human brain,degenerative brain disease,abnormal aging process,brain region,sparse representation,brain age,healthy adult,structural mri,age prediction accuracy,age prediction,healthy aging model,true age
Voxel,Regression analysis,Sparse approximation,Mean absolute error,Human brain,Biomarker (medicine),Clinical diagnosis,Audiology,Abnormality detection,Medicine
Conference
Volume
Issue
ISSN
7751 LNCS
null
16113349
Citations 
PageRank 
References 
1
0.37
11
Authors
3
Name
Order
Citations
PageRank
Longfei Su120.71
Lubin Wang2847.68
Dewen Hu31290101.20