Abstract
Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer’s disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination.
Highlights
Alzheimer disease (AD), the most common form of dementia, is known for the unresolved etiology and pathophysiology
The primary goal of Alzheimer’s Disease Neuroimaging Initiative (ADNI) has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD)
We proposed that hippocampus, amygdala, the whole-brain gray matter, temporal lobe, and parietal lobe should be of higher preference for AD or MCI classification, where amygdala and hippocampus could be the leading candidate for predicting AD conversion in MCI, while occipital lobe, thalamus, globus pallidus, and putamen should be non-priority selections for early diagnosis
Summary
Alzheimer disease (AD), the most common form of dementia, is known for the unresolved etiology and pathophysiology. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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