Exploiting Temporal Information in Functional Magnetic Resonance Imaging Brain Data

Functional Magnetic Resonance Imaging(fMRI) has enabled scientists to look into the active human brain, leading to a flood of new data, thus encouraging the development of new data analysis methods. In this paper, we contribute a comprehensive framework for spatial and temporal exploration of fMRI data, and apply it to a challenging case study: separating drug addicted subjects from healthy non-drug-using controls. To our knowledge, this is the first time that learning on fMRI data is performed explicitly on temporal information for classification in such applications. Experimental results demonstrate that, by selecting discriminative features, group classification can be successfully performed on our case study although training data are exceptionally high dimensional, sparse and noisy fMRI sequences. The classification performance can be significantly improved by incorporating temporal information into machine learning. Both statistical and neuroscientific validation of the method�...

• Type: Conference
• Year: 2005
• Where: MICCAI
• Authors: Lei Zhang 0002, Dimitris Samaras, Dardo Tomasi, Nelly Alia-Klein, Lisa Cottone, Andreana Leskovjan, Nora D. Volkow, Rita Goldstein
• Keywords: Brain Activation Data, Brain Imaging Data, Fmri Data, Medical Imaging, MICCAI 2005