Alzheimer's Disease Classification using K-OPLS and MRI

Sammanfattning: In this thesis, we have used the kernel based orthogonal projection to latent structures (K-OPLS) method to discriminate between Alzheimer's Disease patients (AD) and healthy control subjects (CTL), and to predict conversion from mild cognitive impairment (MCI) to AD. In this regard three cohorts were used to create two different datasets; a small dataset including 63 subjects based on the Alzheimer’s Research Trust (ART) cohort and a large dataset including 1074 subjects combining the AddNeuroMed (ANM) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohorts. In the ART dataset, 34 regional cortical thickness measures and 21 volumetric measures from MRI in addition to 3 metabolite ratios from MRS, altogether 58 variables obtained for 28 AD and 35 CTL subjects. Three different K-OPLS models were created based on MRI and MRS measures and their combination. Combining the MRI and the MRS measures significantly improved the discriminant power resulting in a sensitivity of 96.4% and a specificity of 97.1%. In the combined dataset (ADNI and AddNeuroMed), the Freesurfer pipeline was utilized to extract 34 regional cortical thickness measures and 23 volumetric measures from MRI scans of 295 AD, 335 CTL and 444 MCI subjects. The classification of AD and CTL subjects using the K-OPLS model resulted in a high sensitivity of 85.8% and a specificity of 91.3%. Subsequently, the K-OPLS model was used to prospectively predict conversion from MCI to AD, according to the one year follow up diagnosis. As a result, 78.3% of the MCI converters were classified as AD-like and 57.5% of the MCI non-converters were classified as control-like. Furthermore, an age correction method was proposed to remove the effect of age as a confounding factor. The age correction method successfully removed the age-related changes of the data. Also, the age correction method slightly improved the performance regarding to classification and prediction. This resulted in that 82.1% of the MCI converters were correctly classified. All analyses were performed using 7-fold cross validation. The K-OPLS method shows strong potential for classification of AD and CTL, and for prediction of MCI conversion.