Multivariate Analysis of Magnetic Resonance Imaging Signals of the Human Brain

Title:Multivariate Analysis of Magnetic Resonance Imaging Signals of the Human Brain

Volume: 16 Issue: 24

Author(s): Yoichi Miyawaki

Affiliation:

Keywords: Magnetic resonance imaging, Medical imaging, Neuroscience, Machine learning, Pattern classification, Diagnosis, perception, Vision.

Abstract: Magnetic resonance imaging (MRI) of the human brain plays an important role in the field of medical imaging as well as basic neuroscience. It measures proton spin relaxation, the time constant of which depends on tissue type, and allows us to visualize anatomical structures in the brain. It can also measure functional signals that depend on the local ratio of oxyhemoglobin to deoxyhemoglobin in the blood, which is believed to reflect the degree of neural activity in the corresponding area. MRI thus provides anatomical and functional information about the human brain with high spatial resolution. Conventionally, MRI signals are measured and analyzed for each individual voxel. However, these signals are essentially multivariate because they are measured from multiple voxels simultaneously, and the pattern of activity might carry more useful information than each individual voxel does. This paper reviews recent trends in multivariate analysis of MRI signals in the human brain, and discusses applications of this technique in the fields of medical imaging and neuroscience.

Cite this article as:

Miyawaki Yoichi, Multivariate Analysis of Magnetic Resonance Imaging Signals of the Human Brain, Current Topics in Medicinal Chemistry 2016; 16 (24) . https://dx.doi.org/10.2174/1568026616666160413135303