Support vector machine-based multivariate pattern classification of methamphetamine dependence using arterial spin labeling.
Arterial spin labeling (ASL) magnetic resonance imaging has been widely applied to identify cerebral blood flow (CBF) abnormalities in a number of brain disorders. To evaluate its significance in detecting methamphetamine (MA) dependence, this study used a multivariate pattern classification algorit...
| Published in: | Addiction Biology Vol. 24; no. 6; pp. 1254 - 1263 |
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| Main Authors: | , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
Nov2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139373057&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139373057 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13556215 ADB jtl: Addiction Biology issn: 13556215 maglogo: Y pubinfo: dt: Nov2019 vid: 24 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 139373057 139373057 NLM30623517 139373057 10.1111/adb.12705 NLM30623517 139373057 ppf: 1254 ppct: 9 formats: tig: atl: Support vector machine-based multivariate pattern classification of methamphetamine dependence using arterial spin labeling. aug: au: Li, Yadi Cui, Zaixu Liao, Qi Dong, Haibo Zhang, Jianbing Shen, Wenwen Zhou, Wenhua affil: Department of Radiology, Ningbo Medical Center Lihuili Hospital, Ningbo University, Ningbo China sug: subj: Perfusion Imaging Brain Substance Use Disorders Methamphetamine Magnetic Resonance Imaging Case Control Studies Male Adult Young Adult Cerebrovascular Circulation Brain Blood Supply Imaging, Three-Dimensional ROC Curve Human Free Radicals Adult: 19-44 years Male ab: Arterial spin labeling (ASL) magnetic resonance imaging has been widely applied to identify cerebral blood flow (CBF) abnormalities in a number of brain disorders. To evaluate its significance in detecting methamphetamine (MA) dependence, this study used a multivariate pattern classification algorithm, ie, a support vector machine (SVM), to construct classifiers for discriminating MA-dependent subjects from normal controls. Forty-five MA-dependent subjects, 45 normal controls, and 36 heroin-dependent subjects were enrolled. Classifiers trained with ASL-CBF data from the left or right cerebrum showed significant hemispheric asymmetry in their cross-validated prediction performance (P < 0.001 for accuracy, sensitivity, specificity, kappa, and area under the curve [AUC] of the receiver operating characteristics [ROC] curve). A classifier trained with ASL-CBF data from all cerebral regions (bilateral hemispheres and corpus callosum) was able to differentiate MA-dependent subjects from normal controls with a cross-validated prediction accuracy, sensitivity, specificity, kappa, and AUC of 89%, 94%, 84%, 0.78, and 0.95, respectively. The discrimination map extracted from this classifier covered multiple brain circuits that either constitute a network related to drug abuse and addiction or could be impaired in MA-dependence. The cerebral regions contribute most to classification include occipital lobe, insular cortex, postcentral gyrus, corpus callosum, and inferior frontal cortex. This classifier was also specific to MA-dependence rather than substance use disorders in general (ie, 55.56% accuracy for heroin dependence). These results support the future utilization of ASL with an SVM-based classifier for the diagnosis of MA-dependence and could help improve the understanding of MA-related neuropathology. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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