Identification of psychopathic individuals using pattern classification of MRI images

• Published in: Social neuroscience 2011-10, Vol.6 (5-6), p.627-639
• Main Authors: Sato, João R., de Oliveira-Souza, Ricardo, Thomaz, Carlos E., Basílio, Rodrigo, Bramati, Ivanei E., Amaro, Edson, Tovar-Moll, Fernanda, Hare, Robert D., Moll, Jorge
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Antisocial; Antisocial Personality Disorder - diagnosis; Brain - pathology; Brain mapping; Brain Mapping - methods; Classification; Data processing; Emotions; Female; Gene mapping; Genetic screening; Humans; Image Interpretation, Computer-Assisted - methods; Integration; Machine learning; Magnetic Resonance Imaging; Male; Mental disorders; Moral; Nervous system; Neurobiology; Neuroimaging; Neurology; Neuropsychology; NMR; Nuclear magnetic resonance; Pattern recognition; Personality; Psychopathy; Social behavior; Substantia grisea; superior temporal sulcus; Support Vector Machine; Voxel-based morphometry
• ISSN: 1747-0919; 1747-0927
• DOI: 10.1080/17470919.2011.562687

Background: Psychopathy is a disorder of personality characterized by severe impairments of social conduct, emotional experience, and interpersonal behavior. Psychopaths consistently violate social norms and bring considerable financial, emotional, or physical harm to others and to society as a whole. Recent developments in analysis methods of magnetic resonance imaging (MRI), such as voxel-based-morphometry (VBM), have become major tools to understand the anatomical correlates of this disorder. Nevertheless, the identification of psychopathy by neuroimaging or other neurobiological tools (e.g., genetic testing) remains elusive. Methods/Principal findings: The main aim of this study was to develop an approach to distinguish psychopaths from healthy controls, based on the integration between pattern recognition methods and gray matter quantification. We employed support vector machines (SVM) and maximum uncertainty linear discrimination analysis (MLDA), with a feature-selection algorithm. Imaging data from 15 healthy controls and 15 psychopathic individuals (7 women in each group) were analyzed with SPM2 and the optimized VBM preprocessing routines. Participants were scanned with a 1.5 Tesla MRI system. Both SVM and MLDA achieved an overall leave-one-out accuracy of 80%, but SVM mapping was sparser than using MLDA. The superior temporal sulcus/gyrus (bilaterally) was identified as a region containing the most relevant information to separate the two groups. Conclusion/significance: These results indicate that gray matter quantitative measures contain robust information to predict high psychopathy scores in individual subjects. The methods employed herein might prove useful as an adjunct to the established clinical and neuropsychological measures in patient screening and diagnostic accuracy.