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Abstract #3259

Multitask Machine Learning for Brain-State Classification

Yash S. Shah1, Ashish Farmer1, Luis Hernandez-Garcia1, Douglas C. Noll1, Mark Greenwald2, Jon-Kar Zubieta1, Scott J. Peltier1

1University of Michigan, Ann Arbor, MI, United States; 2Wayne State University, Detroit, MI, United States

Multitask learning formulation presents a novel way of accommodating information from other subjects' data and building a generalized classifier. In our study, we use multitask learning to classify the temporal crave-state of a nicotine dependent subject and compare results to standard single subject SVM. We demonstrate that multitask learning is a promising novel analysis technique for fMRI data analysis.