Evaluating effects of methylphenidate on brain activity in cocaine addiction: a machine-learning approach

Irina Rish; Pouya Bashivan; Guillermo A. Cecchi; Rita Z. Goldstein

doi:10.1117/12.2218212

29 March 2016 Evaluating effects of methylphenidate on brain activity in cocaine addiction: a machine-learning approach

Irina Rish, Pouya Bashivan, Guillermo A. Cecchi, Rita Z. Goldstein

Proceedings Volume 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging; 97880O (2016) https://doi.org/10.1117/12.2218212
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

The objective of this study is to investigate effects of methylphenidate on brain activity in individuals with cocaine use disorder (CUD) using functional MRI (fMRI). Methylphenidate hydrochloride (MPH) is an indirect dopamine agonist commonly used for treating attention deficit/hyperactivity disorders; it was also shown to have some positive effects on CUD subjects, such as improved stop signal reaction times associated with better control/inhibition,¹ as well as normalized task-related brain activity² and resting-state functional connectivity in specific areas.³ While prior fMRI studies of MPH in CUDs have focused on mass-univariate statistical hypothesis testing, this paper evaluates multivariate, whole-brain effects of MPH as captured by the generalization (prediction) accuracy of different classification techniques applied to features extracted from resting-state functional networks (e.g., node degrees). Our multivariate predictive results based on resting-state data from3 suggest that MPH tends to normalize network properties such as voxel degrees in CUD subjects, thus providing additional evidence for potential benefits of MPH in treating cocaine addiction.

Citation Download Citation

Irina Rish, Pouya Bashivan, Guillermo A. Cecchi, and Rita Z. Goldstein "Evaluating effects of methylphenidate on brain activity in cocaine addiction: a machine-learning approach", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97880O (29 March 2016); https://doi.org/10.1117/12.2218212

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