Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

Abstract

We report evidence that computer-based high-dimensional pattern classification of MRI detects patterns of brain structure characterizing mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's Disease (AD). 90% diagnostic accuracy was achieved, using cross-validation, for 30 participants in the Baltimore Longitudinal Study of Aging. Retrospective evaluation of serial scans obtained during prior years revealed gradual increases in structural abnormality for the MCI group, often before clinical symptoms, but slower increase for individuals remaining cognitively normal. Detecting complex patterns of brain abnormality in very early stages of cognitive impairment has pivotal importance for the detection and management of AD.

Automatic detection of preclinical neurodegeneration

“…There is a wide range of techniques for extracting image characteristics to feed into various classification methods. 3,9,11,28 The purpose of our study was to test gray matter-based SVM classification successfully applied to patients with mild to moderate AD on preclinical HD. 7 The study in AD demonstrated the utility when cases were at a point where clinical signs were significant and disease-related atrophy was significant.…”

Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease

“…(2011) using a different database reported up to 81% sensitivity and 95% specificity. Similar or slightly lower results were found for methods relying on tissue segmentation (Davatzikos et al., 2008; Fan, Resnick, Wu, & Davatzikos, 2008; Westman, Aguilar, Muehlboeck, & Simmons, 2013; Zhang, Wang, Zhou, Yuan, & Shen, 2011), elastic deformations (Magnin et al., 2009), semiautomatic segmentation of the hippocampus (Barnes et al., 2004), or combinations of one or more of them (Farhan, Fahiem, & Tauseef, 2014; Kloppel et al., 2008; Plant et al., 2010; Teipel et al., 2007; Wolz et al., 2011). …”

T1ρ MRI of Alzheimer's disease

“…However, there is ample evidence in the literature to suggest that a significant number of cognitive normal elderly, and the majority of MCI individuals, have quite pronounced AD pathology. Recently developed high-dimensional pattern classification methods (Davatzikos et al 2006) have also shown that distinctive patterns of atrophy are present in those groups. Therefore, it is possible that the individuals with AD-like T 1ρ levels do actually have significant amounts of plaques and tangles.…”

“…TE/TR=3.5ms/3000ms for a total imaging time ~10 minutes. A previously developed and validated (Davatzikos et al 2006) method (HAMMER) based on a high-dimensional elastic warping of a digital atlas of the human brain (Kabani et al 2001;Mazziotta et al 2001) was used to partition an individual's volumetric MRI brain scan into 92 ROIs incorporating all major cortical and sub-cortical structures. These methods deform MRI scans of different brains into anatomical co-registration with each other, and into co-registration with a standardized template.…”