Automated Classification to Predict the Progression of Alzheimer's Disease Using Whole-Brain Volumetry and DTI.
- Author:
Won Beom JUNG
1
;
Young Min LEE
;
Young Hoon KIM
;
Chi Woong MUN
Author Information
- Publication Type:Original Article
- Keywords: Magnetic resonance imaging; Alzheimer's disease; Diagnosis; Support vector machines
- MeSH: Alzheimer Disease*; Anisotropy; Atrophy; Brain; Classification*; Diagnosis; Diffusion Tensor Imaging; Humans; Magnetic Resonance Imaging; Memory; Mild Cognitive Impairment; Pathology; Support Vector Machine
- From:Psychiatry Investigation 2015;12(1):92-102
- CountryRepublic of Korea
- Language:English
- Abstract: OBJECTIVE: This study proposes an automated diagnostic method to classify patients with Alzheimer's disease (AD) of degenerative etiology using magnetic resonance imaging (MRI) markers. METHODS: Twenty-seven patients with subjective memory impairment (SMI), 18 patients with mild cognitive impairment (MCI), and 27 patients with AD participated. MRI protocols included three dimensional brain structural imaging and diffusion tensor imaging to assess the cortical thickness, subcortical volume and white matter integrity. Recursive feature elimination based on support vector machine (SVM) was conducted to determine the most relevant features for classifying abnormal regions and imaging parameters, and then a factor analysis for the top-ranked factors was performed. Subjects were classified using nonlinear SVM. RESULTS: Medial temporal regions in AD patients were dominantly detected with cortical thinning and volume atrophy compared with SMI and MCI patients. Damage to white matter integrity was also accredited with decreased fractional anisotropy and increased mean diffusivity (MD) across the three groups. The microscopic damage in the subcortical gray matter was reflected in increased MD. Classification accuracy between pairs of groups (SMI vs. MCI, MCI vs. AD, SMI vs. AD) and among all three groups were 84.4% (+/-13.8), 86.9% (+/-10.5), 96.3% (+/-4.6), and 70.5% (+/-11.5), respectively. CONCLUSION: This proposed method may be a potential tool to diagnose AD pathology with the current clinical criteria.
