Boosting Classification Accuracy of Diffusion MRI Derived Brain Networks for the Subtypes of Mild Cognitive Impairment Using Higher Order Singular Value Decomposition.
Proc IEEE Int Symp Biomed Imaging
; 2015: 131-135, 2015 Apr.
Article
in En
| MEDLINE
| ID: mdl-26413202
ABSTRACT
Mild cognitive impairment (MCI) is an intermediate stage between normal aging and Alzheimer's disease (AD), and around 10-15% of people with MCI develop AD each year. More recently, MCI has been further subdivided into early and late stages, and there is interest in identifying sensitive brain imaging biomarkers that help to differentiate stages of MCI. 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 early versus late MCI.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Proc IEEE Int Symp Biomed Imaging
Year:
2015
Document type:
Article
Affiliation country:
United States