Boosting Brain Connectome Classification Accuracy in Alzheimer’s disease using Higher-Order Singular Value Decomposition

• Main Authors: Liang eZhan, Yashu eLiu, Yalin eWang, Jiayu eZhou, Neda eJahanshad, Jieping eYe, Paul Matthew Thompson
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2015-07-01
• Series: Frontiers in Neuroscience
• Subjects: Classification; Mild Cognitive Impairment; Alzheimer's disease; diffusion MRI; connectome; High-order SVD
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fnins.2015.00257/full

Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer’s disease. Here, we focused on anatomical brain networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer’s Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer’s disease.