Abstract
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’s generalization ability are provided. We demonstrate that incorporation of computer science principles into functional neuroimaging clinical studies, facilitates deduction about the behavioral probes from the brain activation data, thus providing a valid tool that incorporates objective brain imaging data into clinical classification of psychopathologies and identification of genetic vulnerabilities.
We thank Steve Berry, B.A., for help with preliminary data analyses, F. Telang, E.C. Caparelli, L. Chang, T. Ernst and N.K. Squires for helpful discussions; This study was supported by grants from the National Institute on Drug Abuse (to NDV: DA06891-06; and to RZG: 1K23 DA15517-01), Laboratory Directed Research and Development from U.S. Department of Energy (OBER), NARSAD Young Investigator Award, SB/BNL seed grant (79/1025459), National Institute on Alcohol Abuse and Alcoholism (AA/ODO9481-04), ONDCP, and General Clinical Research Center (5-MO1-RR-10710).
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Keywords
- Feature Selection
- Temporal Information
- fMRI Data
- Dynamic Time Warping
- Functional Magnetic Resonance Image
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Zhang, L. et al. (2005). Exploiting Temporal Information in Functional Magnetic Resonance Imaging Brain Data. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_84
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DOI: https://doi.org/10.1007/11566465_84
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