ICA image feature extraction for improving diagnosis of Alzheimer´s disease and mild cognitive impairment

Alzheimer´s disease (AD) is a progressive and often fatal brain disease that destroys brain cells, resulting in memory loss as well as other cognitive and behavioral problems. Here we propose a novel method combining independent components from MRI measures and clinical assessments to distinguish Alzheimer´s patients or mild cognitive impairment (MCI) subjects from healthy elderly controls. Our method includes the following steps: pre-processing, estimating the number of independent components, extracting effective voxels for classification, and classification using a support vector machine (SVM)-based classifier. We found that (1) both AD and MCI subjects showed brain tissue loss, but the volumes of gray matter lost in MCI subjects was far less, in line with the notion that MCI is a prodromal stage of AD; and (2) combining gray matter features from MRI and three commonly used measures of mental status/cognitive function improves classification accuracy, sensitivity and specificity compared with classification using only independent components or clinical measurements. As a result, for classifying AD from healthy controls, we achieved a classification accuracy of 97.7%, sensitivity of 99.2%, and specificity of 96.7%; for differentiating MCI from healthy controls, we achieved a classification accuracy of 87.8%, a sensitivity of 86.0%, and a specificity of 89.6%; these results are better than those obtained with clinical measurements alone (accuracy of 79.5%; sensitivity of 74.0%, and specificity of 85.1%).