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BMC Bioinformatics. 2016 Oct 6;17(Suppl 13):357.

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach.

Author information

1
Department of Computer Science and Information Systems, Texas A&M University-Commerce, Commerce, TX, USA. Mutlu.Mete@tamuc.edu.
2
Computer Engineering, University of Houston - Clear Lake, Houston, TX, USA.
3
Center for Brain Health, University of Texas at Dallas, Richardson, TX, USA.
4
Department of Neurology, UT Southwestern Medical Center, Dallas, TX, USA.
5
Avid Radiopharmaceuticals, Philadelphia, PA, USA.
6
Veterans Affairs North Texas Health Care System, Dallas, TX, USA.
7
Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA.

Abstract

BACKGROUND:

Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2-4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations.

RESULTS:

The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively. Sensitivity and specificity were 0.90 and 0.89 for LOO; 0.83 and 0.83 for 10xCV. Many of the 30 selected clusters are highly relevant to the addictive process, including regions relevant to cognitive control, default mode network related self-referential thought, behavioral inhibition, and contextual memories. Relative hyperactivity and hypoactivity of regional cerebral blood flow in brain regions in cocaine-dependent participants are presented with corresponding level of significance.

CONCLUSIONS:

The SVM-based approach successfully classified cocaine-dependent and healthy control participants using voxels selected with information theoretic-based and statistical methods from participants' SPECT data. The regions found in this study align with brain regions reported in the literature. These findings support the future use of brain imaging and SVM-based classifier in the diagnosis of substance use disorders and furthering an understanding of their underlying pathology.

KEYWORDS:

Classification; Cocaine dependence; Machine learning; Substance use disorders; Support vector machines

PMID:
27766943
PMCID:
PMC5073995
DOI:
10.1186/s12859-016-1218-z
[Indexed for MEDLINE]
Free PMC Article

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