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The current study tested the hypothesis that deficits in multiple subprocesses of behavioral control are associated with reliable neural\u2010processing alterations that define cocaine addiction. While undergoing functional magnetic resonance imaging (<jats:styled-content style=\"fixed-case\">fMRI<\/jats:styled-content>), 38 cocaine\u2010dependent men and 27 healthy control men performed a stop\u2010signal task of motor inhibition. An independent component analysis on <jats:styled-content style=\"fixed-case\">fMRI<\/jats:styled-content> time courses identified task\u2010related neural networks attributed to motor, visual, cognitive and affective processes. The statistical associations of these components with five different stop\u2010signal task conditions were selected for use in a linear discriminant analysis to define a classifier for cocaine addiction from a subsample of 26 cocaine\u2010dependent men and 18 controls. Leave\u2010one\u2010out cross\u2010validation accurately classified 89.5% (39\/44; chance accuracy\u2009=\u200926\/44\u2009=\u200959.1%) of subjects with 84.6% (22\/26) sensitivity and 94.4% (17\/18) specificity. The remaining 12 cocaine\u2010dependent and 9 control men formed an independent test sample, for which accuracy of the classifier was 81.9% (17\/21; chance accuracy\u2009=\u200912\/21\u2009=\u200957.1%) with 75% (9\/12) sensitivity and 88.9% (8\/9) specificity. The cocaine addiction classification score was significantly correlated with a measure of impulsiveness as well as the duration of cocaine use for cocaine\u2010dependent men. The results of this study support the ability of a pattern of multiple neural network alterations associated with inhibitory motor control to define a binary classifier for cocaine addiction.<\/jats:p>","DOI":"10.1111\/adb.12011","type":"journal-article","created":{"date-parts":[[2012,12,12]],"date-time":"2012-12-12T06:49:14Z","timestamp":1355294954000},"page":"427-438","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Neural network activation during a stop\u2010signal task discriminates cocaine\u2010dependent from non\u2010drug\u2010abusing men"],"prefix":"10.1111","volume":"19","author":[{"given":"Amanda","family":"Elton","sequence":"first","affiliation":[{"name":"Brain Imaging Research Center Psychiatric Research Institute University of Arkansas for Medical Sciences  Little Rock AR 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