Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease

Abstract

Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimer's disease (AD). Voxel-based MRI studies have demonstrated that widely distributed cortical and subcortical brain areas show atrophic changes in MCI, preceding the onset of AD-type dementia. Here we developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. MRI was collected in 32 AD patients, 24 MCI subjects and 18 healthy controls (HC). Nine out of 24 MCI subjects converted to AD after an average follow-up interval of 2.5 years. Using feature selection algorithms, brain regions showing the highest accuracy for the discrimination between AD and HC were identified, reaching a classification accuracy of up to 92%. The extracted AD clusters were used as a search region to extract those brain areas that are predictive of conversion to AD within MCI subjects. The most predictive brain areas included the anterior cingulate gyrus and orbitofrontal cortex. The best prediction accuracy, which was cross-validated via train-and-test, was 75% for the prediction of the conversion from MCI to AD. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

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). …”

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI

“…[10][11][12][13][14][15] A series of voxel-based morphometric studies revealed volume differences between patients with MCI and controls mainly distributed within the precuneus and cingulate gyrus. 16 More recently, several contributions on various neurodegenerative diseases reported that the changes in WM microstructure assessed with DTI may be a more sensitive parameter compared with gray matter data [17][18][19][20][21] for detecting mild structural changes occurring at the early stages of the degenerative process. Applying DTI analyses with voxelwise TBSS, 22 an increasing number of contributions described the damage of long interhemispheric and intrahemispheric white matter tracts with homogeneously oriented fibers (ie, genu or splenium of the corpus callosum, superior longitudinal fasciculus, cingulus) and, more rarely, in frontal, parietal, and temporal white matter in patients with MCI compared with healthy controls.…”

“…In contrast, individual faces can be identified by the combination of multiple features such as nose, ear, chin, eyebrow, and so on, even though each feature per se is not necessarily significantly different between groups (for a more detailed description of pattern recognition analyses, see Haller et al 41 ). Originating from machine learning, this technique provided individual risk scores for MCI conversion to AD on the basis of gray matter voxel-based morphometry 16,[42][43][44][45] and WM DTI data. 30 In contrast to these studies that focused on the discrimination between MCI versus controls, or stable versus progressive MCI, this work aims to explore the neuroradiologic background of the previously cited subgroups of MCI and to provide MR imaging tools for the individual classification of MCI subtypes.…”

Identifying Deep Contrasting Networks from Time Series Data: Application to Brain Network Analysis

“…One active area of research is the automatic diagnosis of brain disease from these images and many technique, some of which use deep learning, have been proposed. Plant et al proposed techniques that use Support Vector Machines, Bayes classifiers, and Voting Feature Intervals for the prediction of patients with Alzheimer's disease [16]. More recently, a number of papers have been published which used deep learning models [3,13].…”