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Abstract #2901

SVM Analysis of Nicotine Craving Using Functional MRI

Yash Shailesh Shah1, Luis Hernandez-Garcia1, Douglas C. Noll1, Kinh Luan Phan1, Mark K. Greenwald2, Jon Kar Zubieta1, Scott J. Peltier1

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

Support vector machine learning from fMRI images for application in real-time neurofeedback to regulate craving in nicotine-dependent subjects.

Keywords

abstinence accordingly accuracy acquisition activation addiction addictive allowed amount analyses anterior application arbor axial behaviors binary blocks bold brain button capabilities capable cigarette cigarettes class classification classified collected comprising considered consistent correlates cortex craving created cues custom daily decrease default dependence dependent depicted disturbing domino done draw emotional employed enable encourage ensure expired explain exploit fast feature findings fixation functional generate gilbert goodness greater green health help helps implying increasingly involved kernel king label labels learning least light linear machine machines made maps mark matrix measures measuring mechanisms model moderate much multivariate nature neural nicotine novel overnight package paradigm paradigms pattern pictures plot possibility predicting prediction previously quantified rating real rear recently regulate regulation repeats report reported required response runs scanner scenes scores self series several shah shot since smoked smoking spiral studies subject subjects sudden suggesting support synapse throws tool track trained unexpected unseen user vector view whereas whole yellow