TY - JOUR
T1 - Machine learning of structural magnetic resonance imaging predicts psychopathic traits in adolescent offenders
AU - Steele, Vaughn R.
AU - Rao, Vikram
AU - Calhoun, Vince D.
AU - Kiehl, Kent A.
N1 - Funding Information:
This study was funded by the National Institute of Mental Health (NIMH) grant 1R01MH071896 (K.A.K., PI), the National Institute of Biomedical Imaging and Bioengineering (NIBIB) grant 1R01EB006841 (V.D.C., PI), and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) grant 1R01HD082257 (K.A.K., PI). We thank Ebany Martinez-Finley, J. Michael Maurer, and Prashanth Nyalakanti for their contributions. We gratefully acknowledge the staff and inmates of the New Mexico Corrections Department for without their generous cooperation, this work could not have been completed.
Publisher Copyright:
© 2015 Elsevier Inc.
PY - 2017/1/15
Y1 - 2017/1/15
N2 - Classification models are becoming useful tools for finding patterns in neuroimaging data sets that are not observable to the naked eye. Many of these models are applied to discriminating clinical groups such as schizophrenic patients from healthy controls or from patients with bipolar disorder. A more nuanced model might be to discriminate between levels of personality traits. Here, as a proof of concept, we take an initial step toward developing prediction models to differentiate individuals based on a personality disorder: psychopathy. We included three groups of adolescent participants: incarcerated youth with elevated psychopathic traits (i.e., callous and unemotional traits and conduct disordered traits; n = 71), incarcerated youth with low psychopathic traits (n = 72), and non-incarcerated youth as healthy controls (n = 21). Support vector machine (SVM) learning models were developed to separate these groups using an out-of-sample cross-validation method on voxel-based morphometry (VBM) data. Regions of interest from the paralimbic system, identified in an independent forensic sample, were successful in differentiating youth groups. Models seeking to classify incarcerated individuals to have high or low psychopathic traits achieved 69.23% overall accuracy. As expected, accuracy increased in models differentiating healthy controls from individuals with high psychopathic traits (82.61%) and low psychopathic traits (80.65%). Here we have laid the foundation for using neural correlates of personality traits to identify group membership within and beyond psychopathy. This is only the first step, of many, toward prediction models using neural measures as a proxy for personality traits. As these methods are improved, prediction models with neural measures of personality traits could have far-reaching impact on diagnosis, treatment, and prediction of future behavior.
AB - Classification models are becoming useful tools for finding patterns in neuroimaging data sets that are not observable to the naked eye. Many of these models are applied to discriminating clinical groups such as schizophrenic patients from healthy controls or from patients with bipolar disorder. A more nuanced model might be to discriminate between levels of personality traits. Here, as a proof of concept, we take an initial step toward developing prediction models to differentiate individuals based on a personality disorder: psychopathy. We included three groups of adolescent participants: incarcerated youth with elevated psychopathic traits (i.e., callous and unemotional traits and conduct disordered traits; n = 71), incarcerated youth with low psychopathic traits (n = 72), and non-incarcerated youth as healthy controls (n = 21). Support vector machine (SVM) learning models were developed to separate these groups using an out-of-sample cross-validation method on voxel-based morphometry (VBM) data. Regions of interest from the paralimbic system, identified in an independent forensic sample, were successful in differentiating youth groups. Models seeking to classify incarcerated individuals to have high or low psychopathic traits achieved 69.23% overall accuracy. As expected, accuracy increased in models differentiating healthy controls from individuals with high psychopathic traits (82.61%) and low psychopathic traits (80.65%). Here we have laid the foundation for using neural correlates of personality traits to identify group membership within and beyond psychopathy. This is only the first step, of many, toward prediction models using neural measures as a proxy for personality traits. As these methods are improved, prediction models with neural measures of personality traits could have far-reaching impact on diagnosis, treatment, and prediction of future behavior.
KW - Prediction
KW - Psychopathy
KW - SVM
KW - Voxel-based morphometry
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U2 - 10.1016/j.neuroimage.2015.12.013
DO - 10.1016/j.neuroimage.2015.12.013
M3 - Article
C2 - 26690808
AN - SCOPUS:84953238729
SN - 1053-8119
VL - 145
SP - 265
EP - 273
JO - NeuroImage
JF - NeuroImage
ER -