Identifying Individuals with Antisocial Personality Disorder Using Resting-State fMRI

Abstract

Antisocial personality disorder (ASPD) is closely connected to criminal behavior. A better understanding of functional connectivity in the brains of ASPD patients will help to explain abnormal behavioral syndromes and to perform objective diagnoses of ASPD. In this study we designed an exploratory data-driven classifier based on machine learning to investigate changes in functional connectivity in the brains of patients with ASPD using resting state functional magnetic resonance imaging (fMRI) data in 32 subjects with ASPD and 35 controls. The results showed that the classifier achieved satisfactory performance (86.57% accuracy, 77.14% sensitivity and 96.88% specificity) and could extract stabile information regarding functional connectivity that could be used to discriminate ASPD individuals from normal controls. More importantly, we found that the greatest change in the ASPD subjects was uncoupling between the default mode network and the attention network. Moreover, the precuneus, superior parietal gyrus and cerebellum exhibited high discriminative power in classification. A voxel-based morphometry analysis was performed and showed that the gray matter volumes in the parietal lobule and white matter volumes in the precuneus were abnormal in ASPD compared to controls. To our knowledge, this study was the first to use resting-state fMRI to identify abnormal functional connectivity in ASPD patients. These results not only demonstrated good performance of the proposed classifier, which can be used to improve the diagnosis of ASPD, but also elucidate the pathological mechanism of ASPD from a resting-state functional integration viewpoint.

Structure and dynamics of functional networks in child-onset schizophrenia

“…Indeed, our classification of patients with child-onset schizophrenia achieved a sensitivity of 90% and specificity of 74%. This finding supports the notion that analysis based on network measures and data mining methods may present a possible strategy for automatic diagnostics for neurological disorders Previous studies on restingstate fMRI achieved similar levels of specificity and accuracy, not only for schizophrenia in adults (Tang et al, 2012;Venkataraman et al, 2012;Shen et al, 2010), but also for other neurological disorders (Welsh et al, 2013;Zeng et al, 2013;Tang et al, 2013).…”

“…The analysis of brain network properties may provide a way to facilitate and accelerate diagnosis of such disorders. Neural network properties derived from resting-state fMRI data have already successfully been used for distinguishing between healthy subjects and patients with different neurological disorders, such as amyotrophic lateral sclerosis (Welsh et al, 2013) major depression (Zeng et al, 2013) antisocial personality disorder (Tang et al, 2013). In our study we focused on child-onset schizophrenia, which is a rare form of schizophrenia having its onset before age of 13.…”

Genetic and Neuroimaging Features of Personality Disorders: State of the Art

“…One particular case is the frontal lobe, where there is either a volumetric or a functional decrease in all but schizotypal personality disorder, indicating that this type differs from the other types in executive or thinking strategies. Moreover, neuroimaging findings might in help the development of pharmacological and psychotherapeutic interventions for some disorders [157].…”

Meditative interventions and antisocial personality disorder

“…Specifically, various research findings provided evidence for prefrontal cortex dysfunctions (Davidson, Putnam, & Larson, 2000;Fitzgerald & Demakis, 2007;Raine et al, 2000;Tang, Jiang, Liao, Wang, & Luo, 2013;Trebuchon, Bartolomei, McGonigal, Laguitton, & Chauvel, 2013), reduced gray matter volume in the parietal lobe (Tang et al, 2013), and reduced gray matter volume in the prefrontal cortex (Raine et al, 2000) in ASPD individuals. Research examining the direct and indirect impact of these brain regions on facets of empathic functioning and inhibitory control are explored below.…”