title: Automated Classification to Predict the Progression of Alzheimer's Disease Using Whole-Brain Volumetry and DTI.
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.
  
  
  CONCLUSIONS
  This proposed method may be a potential tool to diagnose AD pathology with the current clinical criteria.
authors: Department of Biomedical Engineering/u-HARC, Inje University, Gimhae, Republic of Korea.
authors: Won Beom
authors: WB
authors: Jung
authors: Department of Psychiatry, Pusan National University Hospital, Busan, Republic of Korea. ; Medical Research Institute, Pusan National University Hospital, Busan, Republic of Korea.
authors: Young Min
authors: YM
authors: Lee
authors: Department of Psychiatry, Medical School, Inje University, Haeundae Paik Hospital, Busan, Republic of Korea.
authors: Young Hoon
authors: YH
authors: Kim
authors: Department of Biomedical Engineering/u-HARC, Inje University, Gimhae, Republic of Korea.
authors: Chi-Woong
authors: CW
authors: Mun
keywords: Alzheimer's disease; Diagnosis; Magnetic resonance imaging; Support vector machines