Abstract
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.
Highlights
Brain imaging is the use of various techniques to create images of the structure or function of the nervous system, with two major types: structural imaging and functional imaging
Resting-state fMRI data for the 64 total subjects was acquired from 3T Philips MR scanners with a T2* weighted single-shot echo-planar imaging (EPI) sequence with 48 slices based on the following parameters: slice thickness = 3.3mm, repetition time (TR) = 3000ms, echo time = 30ms, flip angle = 80°, voxel size = 3.3125 * 3.3125 mm2, and 140 temporal volumes in each run
The algorithm with the most accurate cluster assignments for fMRI data was K-Medoids, which had an accuracy of 82.8125%
Summary
Brain imaging is the use of various techniques to create images of the structure or function of the nervous system, with two major types: structural imaging and functional imaging. The most common form of fMRI uses the Blood-Oxygen-Level-Dependent (BOLD) contrast, which measures the ratio of oxygenated to deoxygenated hemoglobin in the blood. This measures the metabolic demands of active neurons, and not actual neural activity; when neurons fire, they require energy to be brought in from an external source because they do not have any deposits of energy. This leads to the hemodynamic response, or when the blood releases oxygen to active neurons faster than it does to inactive neurons. Since hemoglobin has different magnetic properties in its oxygenated and deoxygenated forms, this leads to a signal that can be detected by an MRI scanner
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