Authors

Qi ZhouFollow

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Department

Electrical Engineering

First Advisor's Name

Malek Adjouadi

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Armando Barreto

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Mercedes Cabrerizo

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Ranjan Duara

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Sakhrat Khizroev

Fifth Advisor's Committee Title

Committee Member

Sixth Advisor's Name

Naphtali David Rishe

Sixth Advisor's Committee Title

Committee Member

Keywords

Alzheimer's Disease, Classification, Prediction, Machine Learning, Statistical Analysis, Neuroimage

Date of Defense

6-4-2015

Abstract

This dissertation proposes to combine magnetic resonance imaging (MRI), positron emission tomography (PET) and a neuropsychological test, Mini-Mental State Examination (MMSE), as input to a multidimensional space for the classification of Alzheimer’s disease (AD) and it’s prodromal stages including amnestic MCI (aMCI) and non-amnestic MCI (naMCI). An assessment is provided on the effect of different MRI normalization techniques on the prediction of AD. Statistically significant variables selected for each combination model were used to construct the classification space using support vector machines. To combine MRI and PET, orthogonal partial least squares to latent structures is used as a multivariate analysis to discriminate between AD, early and late MCI (EMCI and LMCI) from cognitively normal (CN)s. In addition, this dissertation proposes a new effective mean indicator (EMI) method for distinguishing stages of AD from CN. EMI utilizes the mean of specific top-ranked measures, determined by incremental error analysis, to achieve optimal separation of AD and CN.

For AD vs. CN, the two most discriminative volumetric variables (right hippocampus and left inferior lateral ventricle), when combined with MMSE scores, provided an average accuracy of 92.4% (sensitivity: 84.0%; specificity: 96.1%). MMSE scores were found to improve classification accuracy by 8.2% and 12% for aMCI vs. CN and naMCI vs. CN, respectively. Brain atrophy was almost evenly seen on both sides of the brain for AD subjects, which was different from right side dominance for aMCI and left side dominance for naMCI. Findings suggest that subcortical volume need not be normalized, whereas cortical thickness should be normalized either by intracranial volume or the mean thickness. Furthermore, MRI and PET had comparable predictive power in separating AD from CN. For the EMCI prediction, cortical thickness was found to be the best predictor, even better than using all features together. Validation with an external test set demonstrated that best of feature-selected models for the LMCI group was able to classify 83% of the LMCI subjects. The EMI-based method achieved an accuracy of 92.7% using only MRI features. The performance of the EMI-based method along with its simplicity suggests great potential for its use in clinical trials.

Identifier

FIDC000070

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