Study of brain imaging correlates of Mild Cognitive Impairment and Alzheimer’s Disease with machine learning

Abstract: Accurate diagnosis in the early stages is an important challenge for the prevention and effective treatment of Alzheimer’s Disease (AD). This work proposes a method of analysis of the correlation of Mild Cognitive Impairment (MCI) subtypes and its progression to AD using neuroimages such as structural magnetic resonance imaging (MRI) scans. Basic data pre-processing such as the extraction of brain-tissue related parts of the image, image registration and standardization to the mean and deviation is applied. A convolutional autoencoder (CAE) is used to reduce data dimensionality and learn generic features capturing AD biomarkers, followed by various clustering techniques in order to detect different patterns on MCI data. In addition, six MCI patient clusters are generated based on AD progression information provided by ADNI. The method is evaluated on a total of 1069 structural MRI scans (522 MCI scans, 243 AD scans and, 304 CN scans) on the baseline from ADNI database. No clearly separable clusters are found after using CAE model trained on MCI data. Therefore, it is difficult to confirm a strong correlation between different subtypes of MCI patients and its progression to AD. Nevertheless, a significant correlation within the baseline images of the respective six groups identified based on AD progression is reported. It is hypothesized that lack of domain-specific MRI processing, planned in this work, could be a deciding factor about the findings in this research.