Comparison of Unsupervised Learning Algorithms for Identifying Disease Clusters in Cognitive Impairment Using Functional MRI Connectivity Features

Machine learning techniques are often used to model data from functional MRI, a noninvasive technique to study and measure brain activity by identifying changes in blood flow which can be used to classify healthy and disease populations. Most studies use supervised machine learning techniques that require training data labeling to make predictions. To avoid this problem, unsupervised clustering, which does not require training, is performed. However, most fMRI studies using unsupervised learning offer no justification for selecting one unsupervised clustering algorithm over another and normally default to the popular K-Means algorithm. To reach the true potential benefit of unsupervised learning techniques when applied to fMRI data, we examine and compare 12 unsupervised learning algorithms in identifying Alzheimer’s disease clusters based on fMRI connectivity features, with the intention to identify the most effective unsupervised clustering algorithm for fMRI connectivity clustering. Through an analysis of both clustering accuracy and execution time, the K-Medoids algorithm was found to be most optimal for fMRI connectivity data.