NDLI: Multivariate Analysis of structural MRI and PET (FDG and 18F-AV-45) for Alzheimer's disease and its prodromal stages

Content Provider	IEEE Xplore Digital Library
Author	Qi Zhou Goryawala, M. Cabrerizo, M. Barker, W. Loewenstein, D. Duara, R. Adjouadi, M.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. Eng., Florida Int. Univ., Miami, FL, USA (Qi Zhou; Goryawala, M.; Cabrerizo, M.; Adjouadi, M.) \|\| Wien Center for Alzheimer's Disease & Memory Disorders, Mount Sinai Med. Center, Miami Beach, FL, USA (Barker, W.; Loewenstein, D.; Duara, R.)
Abstract	A multivariate analysis method, orthogonal partial least squares to latent structures (OPLS), was used to discriminate Alzheimer's disease (AD), early and late mild cognitive impairment (EMCI and LMCI) from cognitively normal control (CN) using MRI and PET measures. FreeSurfer 5.1 generated 271 MRI features including 49 subcortical volumes, 68 cortical volumes, 68 cortical thicknesses, 70 surface areas and 16 hippocampus subfields. Subjects with all aforementioned MRI measures passing quality control and valid Fludeoxyglucose (18F) (FDG) and Florbetapir (18F) PET scans were selected from ADNI database, resulting in a total of 524 participants (137 CN, 214 EMCI, 103 LMCI and 70 AD) for the study. Altogether 286 features including 15 significant PET uptake features (7 for FDG and 8 for AV-45) were utilized for OPLS analysis. Predictive power was evaluated by $Q_{2}(Y),$ a quantifier of the statistical significance for class separation. The results show that MRI features $(Q_{2}(Y)$ =0.645), and PET features $(Q_{2}(Y)$ = 0.636) has comparable predictive power in separating AD from CN, and MRI features are better predictor of LMCI $(Q_{2}(Y)$ = 0.282) than PET $(Q_{2}(Y)$ = 0.294). Combination of PET and MRI has the most predictive power for LMCI and AD with $Q_{2}(Y)$ of 0.294 and 0.721, respectively. While for EMCI, cortical thickness was found to be the best predictor with a $Q_{2}(Y)$ of 0.108, suggesting cortical thickness may be the first structural change ahead of others and should be prioritized in prediction of very mild cognitive impairment.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	1051
Ending Page	1054
File Size	885343
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6943774
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Positron emission tomography Magnetic resonance imaging Predictive models Alzheimer's disease Testing Training
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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