title: Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis.
abstract: Fusing information from different imaging modalities is crucial for more accurate identification of the brain state because imaging data of different modalities can provide complementary perspectives on the complex nature of brain disorders. However, most existing fusion methods often extract features independently from each modality, and then simply concatenate them into a long vector for classification, without appropriate consideration of the correlation among modalities. In this paper, we propose a novel method to transform the original features from different modalities to a common space, where the transformed features become comparable and easy to find their relation, by canonical correlation analysis. We then perform the sparse multi-task learning for discriminative feature selection by using the canonical features as regressors and penalizing a loss function with a canonical regularizer. In our experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we use Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images to jointly predict clinical scores of Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) and also identify multi-class disease status for Alzheimer's disease diagnosis. The experimental results showed that the proposed canonical feature selection method helped enhance the performance of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.
authors.affiliation: Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Zhu
authors.affiliation: Department of Brain and Cognitive Engineering, Korea University, Seongbuk-gu, Republic of Korea.
authors.forename: Heung-Il
authors.initials: HI
authors.lastname: Suk
authors.affiliation: Department of Brain and Cognitive Engineering, Korea University, Seongbuk-gu, Republic of Korea.
authors.forename: Seong-Whan
authors.initials: SW
authors.lastname: Lee
authors.affiliation: Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. dgshen@med.unc.edu.|Department of Brain and Cognitive Engineering, Korea University, Seongbuk-gu, Republic of Korea. dgshen@med.unc.edu.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000145:classification / Q000000981:diagnostic imaging* / Q000503:physiopathology; D001921:Brain / Q000000981:diagnostic imaging* / Q000503:physiopathology; D066264:Datasets as Topic; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008609:Mental Status Schedule; D008875:Middle Aged; D009483:Neuropsychological Tests; D049268:Positron-Emission Tomography / Q000379:methods; D012044:Regression Analysis
keywords: Alzheimer’s disease; Canonical correlation analysis; Feature selection; Mild cognitive impairment conversion; Multi-class classification