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

Frontiers in Neuroscience

Paul Matthew Thompson¹  Yashu eLiu¹  Yalin eWang²  Jiayu eZhou²  Neda eJahanshad²  Jieping eYe³  Liang eZhan³ 

[1] Informatics, University of Southern California;Arizona State University;;Imaging Genetics Center, Institute for Neuroimaging &

关键词: Classification;    Mild Cognitive Impairment;    Alzheimer's disease;    diffusion MRI;    connectome;    High-order SVD;   

DOI  :  10.3389/fnins.2015.00257

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown