Automated Classification to Predict the Progression of Alzheimer's Disease Using Whole-Brain Volumetry and DTI

Abstract

ObjectiveThis study proposes an automated diagnostic method to classify patients with Alzheimer's disease (AD) of degenerative etiology using magnetic resonance imaging (MRI) markers.MethodsTwenty-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.ResultsMedial 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.ConclusionThis proposed method may be a potential tool to diagnose AD pathology with the current clinical criteria.

Insights into White Matter Damage in Alzheimer's Disease: From Postmortem to in vivo Diffusion Tensor MRI Studies

“…A prominent involvement of temporoparietal regions and, to a lesser extent, the frontal lobes has been detected by both region of interest [35,36,37,38] and voxel-based DT MRI studies [39,40,41,42]. Tractography studies in AD showed the presence of diffusion abnormalities in the major limbic tracts, such as the fornix [43,44] and cingulum [45,46], and corticocortical association tracts, such as the corpus callosum (CC) [43,45,47], uncinate [43,45,48], inferior fronto-occipital [43,49], superior longitudinal [43] and inferior longitudinal [43] fasciculi.…”

In Vivo Detection of Gray Matter Neuropathology in the 3xTg Mouse Model of Alzheimer’s Disease with Diffusion Tensor Imaging

“…Typically, DTI studies of gray matter measure mean diffusivity (MD), an indication of the overall magnitude of water diffusion that is independent of direction. Specifically, increased MD has been reported in the hippocampi of: 1) MCI patients versus healthy age-matched controls [10], 2) MCI patients who converted to AD versus those who did not [14,15], 3) AD versus MCI patients [16,17], 4) AD patients versus healthy age-matched controls, and 5) AD patients versus those with another common type of dementia, dementia with Lewy bodies [18]. Further, increased hippocampal MD better predicts impaired verbal memory in MCI patients than decreased hippocampal volume [10].…”

Feature Selection on MR Images Using Genetic Algorithm with SVM and Naive Bayes Classifiers

“…Thus, subjects were classified using nonlinear SVM. The procedure was repeated 1000 times, and 2-class based classification accuracy was reported as various percentages between %84.4 and %96.3, in other 3-class based case, performance was %70.5 (±11.5) [6].In 2013, MR images of 345 people from the AddNeuroMed cohort were analyzed with FreeSurfer v4.5.0. The dataset contained 116 AD, 119 MCI, and 110 control samples.…”