Striatal brain volume linked to severity of substance use in high-risk incarcerated youth.

Substance use disorders among juveniles are a major public health concern and are often intertwined with other psychosocial risk factors including antisocial behavior. Identifying etiological risks and mechanisms promoting substance use disorders remains a high priority for informing more focused interventions in high-risk populations. The present study examined brain gray matter structure in relation to substance use severity among n = 152 high-risk, incarcerated boys (aged 14-20). Substance use severity was positively associated with gray matter volume across several frontal/striatal brain regions including amygdala, pallidum, putamen, insula, and orbitofrontal cortex. Effects were apparent when using voxel-based-morphometric analysis, as well as in whole-brain, data-driven, network-based approaches (source-based morphometry). These findings support the hypothesis that elevated gray matter volume in striatal reward circuits may be an endogenous marker for vulnerability to severe substance use behaviors among youth.

Psychopathic traits and altered resting-state functional connectivity in incarcerated adolescent girls.

Previous work in incarcerated boys and adult men and women suggest that individuals scoring high on psychopathic traits show altered resting-state limbic/paralimbic, and default mode functional network properties. However, it is unclear whether similar results extend to high-risk adolescent girls with elevated psychopathic traits. This study examined whether psychopathic traits [assessed via the Hare Psychopathy Checklist: Youth Version (PCL:YV)] were associated with altered inter-network connectivity, intra-network connectivity (i.e., functional coherence within a network), and amplitude of low-frequency fluctuations (ALFFs) across resting-state networks among high-risk incarcerated adolescent girls (n = 40). Resting-state networks were identified by applying group independent component analysis (ICA) to resting-state fMRI scans, and a priori regions of interest included limbic, paralimbic, and default mode network components. We tested the association of psychopathic traits (PCL:YV Factor 1 measuring affective/interpersonal traits and PCL:YV Factor 2 assessing antisocial/lifestyle traits) to these three resting-state measures. PCL:YV Factor 1 scores were associated with increased low-frequency and decreased high-frequency fluctuations in components corresponding to the default mode network, as well as increased intra-network FNC in components corresponding to cognitive control networks. PCL:YV Factor 2 scores were associated with increased low-frequency fluctuations in sensorimotor networks and decreased high-frequency fluctuations in default mode, sensorimotor, and visual networks. Consistent with previous analyses in incarcerated adult women, our results suggest that psychopathic traits among incarcerated adolescent girls are associated with altered intra-network ALFFs-primarily that of increased low-frequency and decreased high-frequency fluctuations-and connectivity across multiple networks including paralimbic regions. These results suggest stable neurobiological correlates of psychopathic traits among women across development.

Machine learning classification of chronic traumatic brain injury using diffusion tensor imaging and NODDI: A replication and extension study.

Individuals with acute and chronic traumatic brain injury (TBI) are associated with unique white matter (WM) structural abnormalities, including fractional anisotropy (FA) differences. Our research group previously used FA as a feature in a linear support vector machine (SVM) pattern classifier, observing high classification between individuals with and without acute TBI (i.e., an area under the curve [AUC] value of 75.50%). However, it is not known whether FA could similarly classify between individuals with and without history of chronic TBI. Here, we attempted to replicate our previous work with a new sample, investigating whether FA could similarly classify between incarcerated men with (n = 80) and without (n = 80) self-reported history of chronic TBI. Additionally, given limitations associated with FA, including underestimation of FA values in WM tracts containing crossing fibers, we extended upon our previous study by incorporating neurite orientation dispersion and density imaging (NODDI) metrics, including orientation dispersion (ODI) and isotropic volume (Viso). A linear SVM based classification approach, similar to our previous study, was incorporated here to classify between individuals with and without self-reported chronic TBI using FA and NODDI metrics as separate features. Overall classification rates were similar when incorporating FA and NODDI ODI metrics as features (AUC: 82.50%). Additionally, NODDI-based metrics provided the highest sensitivity (ODI: 85.00%) and specificity (Viso: 82.50%) rates. The current study serves as a replication and extension of our previous study, observing that multiple diffusion MRI metrics can reliably classify between individuals with and without self-reported history of chronic TBI.

Aberrant resting-state functional connectivity associated with childhood trauma among juvenile offenders.

Individuals with history of childhood trauma are characterized by aberrant resting-state limbic and paralimbic functional network connectivity. However, it is unclear whether specific subtypes of trauma (i.e., experienced vs observed or community) showcase differential effects. This study examined whether subtypes of childhood trauma (assessed via the Trauma Checklist [TCL] 2.0) were associated with aberrant intra-network amplitude of fluctuations and connectivity (i.e., functional coherence within a network), and inter-network connectivity across resting-state networks among incarcerated juvenile males (n = 179). Subtypes of trauma were established via principal component analysis of the TCL 2.0 and resting-state networks were identified by applying group independent component analysis to resting-state fMRI scans. We tested the association of subtypes of childhood trauma (i.e., TCL Factor 1 measuring experienced trauma and TCL Factor 2 assessing community trauma), and TCL Total scores to the aforementioned functional connectivity measures. TCL Factor 2 scores were associated with increased high-frequency fluctuations and increased intra-network connectivity in cognitive control, auditory, and sensorimotor networks, occurring primarily in paralimbic regions. TCL Total scores exhibited similar neurobiological patterns to TCL Factor 2 scores (with the addition of aberrant intra-network connectivity in visual networks), and no significant associations were found for TCL Factor 1. Consistent with previous analyses of community samples, our results suggest that childhood trauma among incarcerated juvenile males is associated with aberrant intra-network amplitude of fluctuations and connectivity across multiple networks including predominately paralimbic regions. Our results highlight the importance of accounting for traumatic loss, observed trauma, and community trauma in assessing neurobiological aberrances associated with adverse experiences in childhood, as well as the value of trained-rater trauma assessments compared to self-report.

Hemodynamic activity in the limbic system predicts reoffending in women.

Previous research (Aharoni et al., 2013, 2014) found that hemodynamic activity in the dorsal anterior cingulate cortex (dACC) during error monitoring predicted non-violent felony rearrest in men released from prison. This article reports an extension of the Aharoni et al. (2013, 2014) model in a sample of women released from state prison (n = 248). Replicating aspects of prior work, error monitoring activity in the dACC, as well as psychopathy scores and age at release, predicted non-violent felony rearrest in women. Sex differences in the directionality of dACC activity were observed-high error monitoring activity predicted rearrest in women, whereas prior work found low error monitoring activity predicted rearrest in men. As in prior analyses, the ability of the dACC to predict rearrest outcomes declines with more generalized outcomes (i.e., general felony). Implications for future research and clinical and forensic risk assessment are discussed.

Aberrant resting-state functional connectivity in incarcerated women with elevated psychopathic traits.

Previous work in incarcerated men suggests that individuals scoring high on psychopathy exhibit aberrant resting-state paralimbic functional network connectivity (FNC). However, it is unclear whether similar results extend to women scoring high on psychopathy. This study examined whether psychopathic traits [assessed via the Hare Psychopathy Checklist - Revised (PCL-R)] were associated with aberrant inter-network connectivity, intra-network connectivity (i.e., functional coherence within a network), and amplitude of fluctuations across limbic and surrounding paralimbic regions among incarcerated women (n = 297). Resting-state networks were identified by applying group Independent Component Analysis to resting-state fMRI scans. We tested the association of psychopathic traits (PCL-R Factor 1 measuring interpersonal/affective psychopathic traits and PCL-R Factor 2 assessing lifestyle/antisocial psychopathic traits) to the three FNC measures. PCL-R Factor 1 scores were associated with increased low-frequency fluctuations in executive control and attentional networks, decreased high-frequency fluctuations in executive control and visual networks, and decreased intra-network FNC in default mode network. PCL-R Factor 2 scores were associated with decreased high-frequency fluctuations and default mode networks, and both increased and decreased intra-network functional connectivity in visual networks. Similar to previous analyses in incarcerated men, our results suggest that psychopathic traits among incarcerated women are associated with aberrant intra-network amplitude fluctuations and connectivity across multiple networks including limbic and surrounding paralimbic regions.

Brain gray matter differences among forensic psychiatric patients with psychosis and incarcerated individuals without psychosis: A source-based morphometry study.

BACKGROUND: While psychosis is a risk factor for violence, the majority of individuals who perpetrate aggression do not present psychotic symptoms. Pathological aggressive behavior is associated with brain gray matter differences, which, in turn, has shown a relationship with increased psychopathic traits. However, no study, to our knowledge, has ever investigated gray matter differences in forensic psychiatric patients with psychosis compared with incarcerated individuals without psychosis matched on levels of psychopathic traits. Here, we employed source-based morphometry (SBM) to investigate gray matter differences in these two populations. METHODS: We scanned 137 participants comprising two offender subgroups: 69, non-psychotic incarcerated offenders and 68, psychotic, forensic psychiatric patients. Groups showed no difference in age, race, ethnicity, handedness, and Hare Psychopathy Checklist-Revised scores. Source-based morphometry was utilized to identify spatially distinct sets of brain regions where gray matter volumes covaried between groups. SBM is a data-driven, multivariate technique that uses independent components analysis to categorize groups of voxels that display similar variance patterns (e.g., components) that are compared across groups. RESULTS: SBM identified four components that differed between groups. These findings indicated greater loading weights in the superior, transverse, and middle temporal gyrus and anterior cingulate in the non-psychotic versus psychotic group; greater loading weights in the basal ganglia in the psychotic versus non-psychotic group; greater loading weights in the frontal pole, precuneus, and visual cortex among psychotic versus non-psychotic offenders; and greater loading weights in the thalamus and parahippocampal gyrus in psychotic versus non-psychotic groups. CONCLUSIONS: Two different offender groups that perpetrate violence and show comparable levels of psychopathic traits evidenced different gray matter volumes. We suggest that future studies of violent offenders with psychosis take psychopathic traits into account to refine neural phenotypes.

Neural Correlates of Moral Judgment in Criminal Offenders with Sadistic Traits.

Sexual sadism is a paraphilia that focuses on domination, humiliation, and infliction of pain on a victim to stimulate sexual arousal. Although extensively described in psychology and forensic sciences, less is known about whether the harmful acts committed by sexual sadists are accompanied by deficits in moral judgment. A limited amount of behavioral research suggests moral insensitivity in sexual sadists; however, the neural networks underlying moral judgment in sadists have not been studied. In this pilot study, 21 incarcerated male sexual offenders with (n = 11) and without (n = 10) sexual sadism were scanned with functional magnetic resonance imaging (fMRI) while they viewed pictures that did or did not depict situations considered by most individuals to represent moral transgressions, and rated their degree of moral transgression severity. Results indicated primarily overlapping neural systems underlying moral judgment in sadists and non-sadists. However, non-sadists but not sadists showed a positive correlation between moral transgression severity ratings and activity in the anterior temporal cortex (ATC). This lack of ATC engagement in sadists might be a biomarker of altered moral judgment.

Aberrant brain gray matter in murderers.

Homicide is a significant societal problem with economic costs in the billions of dollars annually and incalculable emotional impact on victims and society. Despite this high burden, we know very little about the neuroscience of individuals who commit homicide. Here we examine brain gray matter differences in incarcerated adult males who have committed homicide (n = 203) compared to other non-homicide offenders (n = 605; total n = 808). Homicide offenders' show reduced gray matter in brain areas critical for behavioral control and social cognition compared with subsets of other violent and non-violent offenders. This demonstrates, for the first time, that unique brain abnormalities may distinguish offenders who kill from other serious violent offenders and non-violent antisocial individuals.

Source-based morphometry reveals gray matter differences related to suicidal behavior in criminal offenders.

Relative to the general population, criminal offenders have a higher risk of suicide. Neurobiological deficits related to suicidal behavior have been identified in the general population, but unexamined in offenders to date. We examined the association between brain morphology and suicidal behavior in adult male criminal offenders. Brain morphology was examined using voxel-based morphometry (VBM) and source-based morphometry (SBM), a multivariate alternative to VBM which analyzes brain volume in between-subject spatially independent networks. Results showed that offenders with past suicide attempts (n = 19), relative to offenders without past suicide attempts (n = 19) and non-offenders (n = 26), had reduced gray matter in an SBM component that comprised the posterior cingulate, dorsal prefrontal cortex, and amygdala. The SBM source weights were significantly associated with suicide attempts independent of other suicide risk variables (e.g., depression). VBM results were similar to the SBM results but less robust. The results reveal a potential neurobiological marker of vulnerability to suicidal behavior among criminal offenders and illustrate the utility of multivariate methods of gray matter analyses.

Psychopathy is associated with shifts in the organization of neural networks in a large incarcerated male sample.

Psychopathy is a personality disorder defined by antisocial behavior paired with callousness, low empathy, and low interpersonal emotions. Psychopathic individuals reliably display complex atypicalities in emotion and attention processing that are evident when examining task performance, activation within specific neural regions, and connections between regions. Recent advances in neuroimaging methods, namely graph analysis, attempt to unpack this type of processing complexity by evaluating the overall organization of neural networks. Graph analysis has been used to better understand neural functioning in several clinical disorders but has not yet been used in the study of psychopathy. The present study applies a minimum spanning tree graph analysis to resting-state fMRI data collected from male inmates assessed for psychopathy with the Hare Psychopathy Checklist-Revised (n = 847). Minimum spanning tree analysis provides several metrics of neural organization optimality (i.e., the effectiveness, efficiency, and robustness of neural network organization). Results show that inmates higher in psychopathy exhibit a more efficiently organized dorsal attention network (ß = =0.101, pcorrected = =0.018). Additionally, subcortical structures (e.g., amygdala, caudate, and hippocampus) act as less of a central hub in the global flow of information in inmates higher in psychopathy (ß = =-0.104, pcorrected = =0.048). There were no significant effects of psychopathy on neural network organization in the default or salience networks. Together, these shifts in neural organization suggest that the brains of inmates higher in psychopathy are organized in a fundamentally different way than other individuals.

Resting-state fMRI dynamic functional network connectivity and associations with psychopathy traits.

Studies have used resting-state functional magnetic resonance imaging (rs-fMRI) to examine associations between psychopathy and brain connectivity in selected regions of interest as well as networks covering the whole-brain. One of the limitations of these approaches is that brain connectivity is modeled as a constant state through the scan duration. To address this limitation, we apply group independent component analysis (GICA) and dynamic functional network connectivity (dFNC) analysis to uncover whole-brain, time-varying functional network connectivity (FNC) states in a large forensic sample. We then examined relationships between psychopathic traits (PCL-R total scores, Factor 1 and Factor 2 scores) and FNC states obtained from dFNC analysis. FNC over the scan duration was better represented by five states rather than one state previously shown in static FNC analysis. Consistent with prior findings, psychopathy was associated with networks from paralimbic regions (amygdala and insula). In addition, whole-brain FNC identified 15 networks from nine functional domains (subcortical, auditory, sensorimotor, cerebellar, visual, salience, default mode network, executive control and attentional) related to psychopathy traits (Factor 1 and PCL-R scores). Results also showed that individuals with higher Factor 1 scores (affective and interpersonal traits) spend more time in a state with weaker connectivity overall, and changed states less frequently compared to those with lower Factor 1 scores. On the other hand, individuals with higher Factor 2 scores (impulsive and antisocial behaviors) showed more dynamism (changes to and from different states) than those with lower scores.

The structural brain correlates of callous-unemotional traits in incarcerated male adolescents.

Youth with severe conduct problems impose a significant cost on society by engaging in high levels of antisocial and aggressive behavior. Within this group, adolescents with high levels of callous- unemotional traits have been found to exhibit more severe and persistent patterns of antisocial behavior than youth with severe conduct problems but normative levels of callous-unemotional traits. Existing neuroimaging studies, along with theoretical accounts of psychopathology, suggest that dysfunction within the paralimbic cortex and limbic system may underlie elevated levels of callous-unemotional traits. The present study examines this hypothesis by investigating gray matter correlates associated with callous-unemotional traits. A sample of incarcerated male adolescents (N = 269), were assessed using voxel-based morphometry. Callous-unemotional traits were assessed using the Inventory of Callous-Unemotional traits (Frick 2004). Total callous-unemotional traits were negatively correlated with anterior temporal lobe gray matter volume (GMV). Callous traits in particular exhibited a reliable negative correlation with gray matter volume in nearly every paralimbic brain region examined. Uncaring traits were positively correlated with GMV in the orbitofrontal and anterior cingulate cortices. These findings demonstrate specific neural features within the paralimbic cortex and limbic system that accompany elevated callous-unemotional traits and serves to expand our understanding of pathophysiological mechanisms that may give rise to severe conduct problems in youth.

Affective and interpersonal psychopathic traits associated with reduced corpus callosum volume among male inmates - RETRACTED.

BACKGROUND: Psychopathy is a personality disorder associated with severe emotional and interpersonal consequences and persistent antisocial behavior. Neurobiological models of psychopathy emphasize impairments in emotional processing, attention, and integration of information across large-scale neural networks in the brain. One of the largest integrative hubs in the brain is the corpus callosum (CC) - a large white matter structure that connects the two cerebral hemispheres. METHOD: The current study examines CC volume, measured via Freesurfer parcellation, in a large sample (n = 495) of incarcerated men who were assessed for psychopathic traits using the Hare Psychopathy Checklist-Revised (PCL-R). RESULTS: Psychopathy was associated with reduced volume across all five sub-regions of the CC. These relationships were primarily driven by the affective/interpersonal elements of psychopathy (PCL-R Factor 1), as no significant associations were found between the CC and the lifestyle/antisocial traits of psychopathy. The observed effects were not attributable to differences in substance use severity, age, IQ, or total brain volume. CONCLUSIONS: These findings align with suggestions that core psychopathic traits may be fostered by reduced integrative capacity across large-scale networks in the brain.

Machine learning of brain gray matter differentiates sex in a large forensic sample.

Differences between males and females have been extensively documented in biological, psychological, and behavioral domains. Among these, sex differences in the rate and typology of antisocial behavior remains one of the most conspicuous and enduring patterns among humans. However, the nature and extent of sexual dimorphism in the brain among antisocial populations remains mostly unexplored. Here, we seek to understand sex differences in brain structure between incarcerated males and females in a large sample (n = 1,300) using machine learning. We apply source-based morphometry, a contemporary multivariate approach for quantifying gray matter measured with magnetic resonance imaging, and carry these parcellations forward using machine learning to classify sex. Models using components of brain gray matter volume and concentration were able to differentiate between males and females with greater than 93% generalizable accuracy. Highly differentiated components include orbitofrontal and frontopolar regions, proportionally larger in females, and anterior medial temporal regions proportionally larger in males. We also provide a complimentary analysis of a nonforensic healthy control sample and replicate our 93% sex discrimination. These findings demonstrate that the brains of males and females are highly distinguishable. Understanding sex differences in the brain has implications for elucidating variability in the incidence and progression of disease, psychopathology, and differences in psychological traits and behavior. The reliability of these differences confirms the importance of sex as a moderator of individual differences in brain structure and suggests future research should consider sex specific models.

Age of gray matters: Neuroprediction of recidivism.

Age is one of the best predictors of antisocial behavior. Risk models of recidivism often combine chronological age with demographic, social and psychological features to aid in judicial decision-making. Here we use independent component analyses (ICA) and machine learning techniques to demonstrate the utility of using brain-based measures of cerebral aging to predict recidivism. First, we developed a brain-age model that predicts chronological age based on structural MRI data from incarcerated males (n = 1332). We then test the model's ability to predict recidivism in a new sample of offenders with longitudinal outcome data (n = 93). Consistent with hypotheses, inclusion of brain-age measures of the inferior frontal cortex and anterior-medial temporal lobes (i.e., amygdala) improved prediction models when compared with models using chronological age; and models that combined psychological, behavioral, and neuroimaging measures provided the most robust prediction of recidivism. These results verify the utility of brain measures in predicting future behavior, and suggest that brain-based data may more precisely account for important variation when compared with traditional proxy measures such as chronological age. This work also identifies new brain systems that contribute to recidivism which has clinical implications for treatment development.

Abnormal cortical gyrification in criminal psychopathy.

Background: Psychopathy is a personality disorder characterized by interpersonal and emotional abnormalities (e.g., lack of empathy and guilt) and antisocial behavior. Psychopathy has been associated with a number of structural brain abnormalities, most notably in orbital frontal and anterior/medial temporal regions, that may underlie psychopathic individuals' problematic behaviors. Past research evaluating cortical structure in psychopathy has considered thickness and volume, but to date no study has investigated differences in cortical gyrification, a measure of cortical complexity thought to reflect early neurodevelopmental cortical connectivity. Methods: We measured the local gyrification index (LGI) in a sample of 716 adult male inmates and performed a whole brain analysis assessing the relationship between LGI and total and factor scores on the Hare Psychopathy Checklist-Revised (PCL-R). Results: PCL-R scores were negatively associated with LGI measures within the right hemisphere in the midcingulate cortex (MCC) and adjacent regions of the superior frontal gyrus as well as lateral superior parietal cortex. Additionally, PCL-R Factor 1 scores (interpersonal/affective traits) predicted less LGI within the right MCC and adjacent dorsomedial frontal cortex and greater LGI in bilateral occipital cortex. Scores on PCL-R Factor 2, indicating impulsivity and antisocial behaviors, did not predict LGI in any regions. Conclusions: These findings suggest that psychopathy, particularly the interpersonal and affective traits, are associated with specific structural abnormalities that form during neurodevelopment and these abnormalities may underlie aberrant brain functioning in regions important in emotional processing and cognitive control.

Aberrant functional network connectivity in psychopathy from a large (N = 985) forensic sample.

Psychopathy is a personality disorder characterized by antisocial behavior, lack of remorse and empathy, and impaired decision making. The disproportionate amount of crime committed by psychopaths has severe emotional and economic impacts on society. Here we examine the neural correlates associated with psychopathy to improve early assessment and perhaps inform treatments for this condition. Previous resting-state functional magnetic resonance imaging (fMRI) studies in psychopathy have primarily focused on regions of interest. This study examines whole-brain functional connectivity and its association to psychopathic traits. Psychopathy was hypothesized to be characterized by aberrant functional network connectivity (FNC) in several limbic/paralimbic networks. Group-independent component and regression analyses were applied to a data set of resting-state fMRI from 985 incarcerated adult males. We identified resting-state networks (RSNs), estimated FNC between RSNs, and tested their association to psychopathy factors and total summary scores (Factor 1, interpersonal/affective; Factor 2, lifestyle/antisocial). Factor 1 scores showed both increased and reduced functional connectivity between RSNs from seven brain domains (sensorimotor, cerebellar, visual, salience, default mode, executive control, and attentional). Consistent with hypotheses, RSNs from the paralimbic system-insula, anterior and posterior cingulate cortex, amygdala, orbital frontal cortex, and superior temporal gyrus-were related to Factor 1 scores. No significant FNC associations were found with Factor 2 and total PCL-R scores. In summary, results suggest that the affective and interpersonal symptoms of psychopathy (Factor 1) are associated with aberrant connectivity in multiple brain networks, including paralimbic regions.

Functional connectivity during affective mentalizing in criminal offenders with psychotic disorders: Associations with clinical symptoms.

Psychotic disorders are associated with neurobehavioral impairments in mental state attribution (mentalizing). These impairments are most severe in psychotic patients with elevated symptom levels, particularly negative and cognitive symptoms. There have been few studies of functional connectivity related to mentalizing in psychotic disorders and associations with symptoms. We conducted a functional MRI study of affective mentalizing in individuals with psychotic disorders and varying symptom levels (positive, negative, cognitive). Participants were drawn from an adjudicated inpatient forensic psychiatric population (criminal offenders). Functional MRI scans were acquired using a 32-channel ultra-fast multiband imaging sequence. Mentalizing task performance and functional connectivity were assessed in psychotic criminal offenders (n = 46) and nonpsychotic offenders (n = 41). Temporal coherent brain networks were estimated with group independent component analysis (ICA). Relative to nonpsychotic offenders, psychotic offenders showed impaired task performance and reduced activation in a component comprising the dorsomedial prefrontal cortex, superior temporal gyrus, and ventrolateral prefrontal cortex. Positive and cognitive symptoms were inversely correlated with component activity and task performance. The results are discussed with reference to potential mechanisms underlying impaired social cognition in psychotic disorders and across symptom types.