Age of gray matters: Neuroprediction of recidivism

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.

A proven reoffending study of individuals managed under the multi-agency public protection arrangements (MAPPA) in England and Wales

Introduction

Past research into the effectiveness of multi-agency public protection arrangements (MAPPA) in reducing reoffending it limited. Thus, the current study aimed to evaluate proven reoffending patterns for MAPPA managed individuals.

Methods

Proven reoffending for 39,501 MAPPA managed individuals was investigated by (1) examining patterns in the timing and frequency of proven reoffending for MAPPA managed individuals; (2) examining 1-, 3-, and 5-year proven reoffending patterns of MAPPA managed individuals by MAPPA category, age, and gender; and (3) comparing crime harm levels and recall to custody for MAPPA managed individuals pre- and post-MAPPA adoption.

Results

Taken together, our findings show that proven reoffending rates for individuals managed under MAPPA are substantially lower than those reported in proven reoffending statistics for England and Wales.

Discussion

Our results suggest that MAPPA is making a positive contribution to a managing individuals convicted of sexual and violent offenses. Additionally, our findings provide the best evidence to date that MAPPA management may also be effective at reducing less serious offenses which do not typically involve immediate removal from society. These findings are considered in light of their theoretical and practical implications while potential limitations and avenues for future research are outlined.

Psychopathy Scores Predict Recidivism in High-risk Youth: A Five-year Follow-up Study

Psychopathic traits have been associated with rearrest in adolescents involved in the criminal legal system. Much of the prior work has focused on White samples, short follow-up windows, and relatively low-risk youth. The current study aimed to evaluate the utility of the Hare Psychopathy Checklist: Youth Version (PCL:YV) for predicting general and violent felony recidivism in a large sample of high-risk, predominantly Hispanic/Latino, male adolescents (n = 254) with a five-year follow-up period. Results indicated higher PCL:YV scores and lower full-scale estimated IQ scores were significantly associated with a shorter time to felony and violent felony rearrest. These effects generalized to Hispanic/Latino adolescents (n = 193)-a group that faces disproportionate risk of being detained or committed to juvenile correctional facilities in the U.S. These results suggest that expert-rated measures of psychopathic traits and IQ are reliable predictors of subsequent felony and violent felony rearrest among high-risk male adolescents.

Neurotechnology to reduce recidivism: Ethical and legal challenges

Crime comes with enormous costs, not only financial but also in terms of loss of mental and physical health and, in some cases, even loss of life. Recidivism is responsible for a considerable percentage of the crimes, and therefore, society deems reducing recidivism a priority. To reduce recidivism, several types of interventions can be used, such as education and employment-focused rehabilitation programs which are intended to improve psychological and social factors. Another way to prevent reoffending is to influence the offender's brain functions. For example, medication can be offered to treat delusions or to diminish sexual drive. In the near future, innovative neurotechnologies are expected to improve prediction and prevention of reoffending. Potential positive effects of such neurotechniques include a safer society and earlier release of prisoners who are no longer "at high risk" to relapse into criminal behavior. Meanwhile, employing these neurotechniques in the criminal justice system raises fundamental concerns, for example, about autonomy, privacy and mental integrity. This chapter aims to identify some of the ethical and legal challenges of using neurotechnologies to reduce recidivism.

Quantitative Head Dynamics Associated with Interpersonal (Grandiose-Manipulative) Psychopathic Traits in Incarcerated Youth

Clinicians have long noted that individuals with elevated psychopathic traits can be characterized by unique interpersonal styles, including prolonged eye contact, invasion of interpersonal space, and frequent use of hand gestures. Such forms of nonverbal communication can be measured via hand, body, and head position and dynamics. Previous studies have developed an automated algorithm designed to capture head position and dynamics from digital recordings of clinical interviews in a sample of incarcerated adult men. We observed that higher psychopathy scores were associated with stationary head dwell time. Here, we applied a similar automated algorithm to assess head position and dynamics on videotaped clinical interviews assessing psychopathic traits from n = 242 youth housed at a maximum-security juvenile correctional facility. We observed that higher psychopathy scores (assessed via the Hare Psychopathy Checklist: Youth Version [PCL:YV]) were associated with unique patterns of head dynamics. Specifically, PCL:YV Total, Factor 1 (measuring grandiose-manipulative and callous-unemotional traits), and Facet 1 (measuring grandiose-manipulative traits) scores were associated with a higher proportion of time spent in a head dynamics pattern consisting of moderate movement away from the average head position. This study lays the groundwork for future investigations to apply quantitative methods to better understand patterns of nonverbal communication styles in clinical populations characterized by severe antisocial behavior.

Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry

It is now possible to estimate an individual's brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.

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.

The age of violence: Mapping brain age in psychosis and psychopathy

Young chronological age is one of the strongest predictors for antisocial behaviour in the general population and for violent offending in individuals with psychotic disorders. An individual's age can be predicted with high accuracy using neuroimaging and machine-learning. The deviation between predicted and chronological age, i.e., brain age gap (BAG) has been suggested to reflect brain health, likely relating partly to neurodevelopmental and aging-related processes and specific disease mechanisms. Higher BAG has been demonstrated in patients with psychotic disorders. However, little is known about the brain-age in violent offenders with psychosis and the possible associations with psychopathy traits. We estimated brain-age in 782 male individuals using T1-weighted MRI scans. Three machine learning models (random forest, extreme gradient boosting with and without hyper parameter tuning) were first trained and tested on healthy controls (HC, n = 586). The obtained BAGs were compared between HC and age matched violent offenders with psychosis (PSY-V, n = 38), violent offenders without psychosis (NPV, n = 20) and non-violent psychosis patients (PSY-NV, n = 138). We ran additional comparisons between BAG of PSY-V and PSY-NV and associations with Positive and Negative Syndrome Scale (PANSS) total score as a measure of psychosis symptoms. Psychopathy traits in the violence groups were assessed with Psychopathy Checklist-revised (PCL-R) and investigated for associations with BAG. We found significantly higher BAG in PSY-V compared with HC (4.9 years, Cohen'sd = 0.87) and in PSY-NV compared with HC (2.7 years, d = 0.41). Total PCL-R scores were negatively associated with BAG in the violence groups (d = 1.17, p < 0.05). Additionally, there was a positive association between psychosis symptoms and BAG in the psychosis groups (d = 1.12, p < 0.05). While the significant BAG differences related to psychosis and not violence suggest larger BAG for psychosis, the negative associations between BAG and psychopathy suggest a complex interplay with psychopathy traits. This proof-of-concept application of brain age prediction in severe mental disorders with a history of violence and psychopathy traits should be tested and replicated in larger samples.

Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.

Reduced neural responsiveness to looming stimuli is associated with increased aggression

While neuro-cognitive work examining aggression has examined patients with conditions at increased risk for aggression or individuals self-reporting past aggression, little work has attempted to identify neuro-cognitive markers associated with observed/recorded aggression. The goal of the current study was to determine the extent to which aggression by youth in the first three months of residential care was associated with atypical responsiveness to threat stimuli. This functional MRI study involved 98 (68 male; mean age = 15.96 [sd = 1.52]) adolescents in residential care performing a looming threat task involving images of threatening and neutral human faces or animals that appeared to be either loom or recede. Level of aggression was negatively associated with responding to looming stimuli (irrespective of whether these were threatening or neutral) within regions including bilateral inferior frontal gyrus, right inferior parietal lobule, right superior/middle temporal gyrus and a region of right uncus proximal to the amygdala. These data indicate that aggression level is associated with a decrease in responsiveness to a basic threat cue-looming stimuli. Reduced threat responsiveness likely results in the individual being less able to represent the negative consequences that may result from engaging in aggression, thereby increasing the risk for aggressive episodes.

Forensic Brain-Reading and Mental Privacy in European Human Rights Law: Foundations and Challenges

A central question in the current neurolegal and neuroethical literature is how brain-reading technologies could contribute to criminal justice. Some of these technologies have already been deployed within different criminal justice systems in Europe, including Slovenia, Italy, England and Wales, and the Netherlands, typically to determine guilt, legal responsibility, or recidivism risk. In this regard, the question arises whether brain-reading could permissibly be used against the person's will. To provide adequate legal protection from such non-consensual brain-reading in the European legal context, ethicists have called for the recognition of a novel fundamental legal right to mental privacy. In this paper, we explore whether these ethical calls for recognising a novel legal right to mental privacy are necessary in the European context. We argue that a right to mental privacy could be derived from, or at least developed within in the jurisprudence of the European Court of Human Rights, and that introducing an additional fundamental right to protect against (forensic) brain-reading is not necessary. What is required, however, is a specification of the implications of existing rights for particular neurotechnologies and purposes.

Pitfalls in brain age analyses

Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap.” Researchers have identified that the brain age gap, as a linear transformation of an out‐of‐sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R² will be artificially inflated to the extent that it is highly improbable that an R² value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.

Morphology of the criminal brain: gray matter reductions are linked to antisocial behavior in offenders

Aggression and psychopathy are multifaceted conditions determined interpersonal and antisocial factors. Only a few studies analyze the link between these separate factors and specific brain morphology distinctively. A voxel-based morphometry (VBM) analysis was performed on 27 violent offenders and 27 controls aiming to associate sub-features of aggressive and psychopathic behavior with specific gray matter volumes. Trait aggression was assessed using two self-report tests (Aggression Questionnaire, AQ, and Reactive-Proactive-Aggression Questionnaire, RPQ) and psychopathy with the Psychopathy Checklist-Revised (PCL-R). Total and sub-scale scores of these tests were correlated to the brain morphometry of the offenders group in separate analyses. It was found that psychopathic behavior was negatively correlated with prefrontal gray matter volume and that this result was primarily driven by the antisocial behavior sub-scale of the PCL-R. Furthermore, less gray matter in right superior frontal and left inferior parietal regions with increasing antisocial behavior could be observed. One cluster comprising the right middle and superior temporal gyrus was negatively correlated with both, reactive aggression and antisocial behavior. These results outline (1) the importance of distinctively analyzing sub-features that contribute to aggressive and psychopathic behavior, given that the negative correlation of psychopathy global scores with prefrontal volume was driven by one single facet of the PCL-R scale (antisocial behavior). Moreover, these results indicate (2) fronto-temporo-parietal network deficits in antisocial, criminal offenders, with a particular strong effect in the temporal lobe.

Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as 'A.I. neuroprediction,' and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed.

Re-wiring Guilt: How Advancing Neuroscience Encourages Strategic Interventions Over Retributive Justice

The increasing visibility of neuroscience employed in legal contexts has rightfully prompted critical discourse regarding the boundaries of its utility. High profile debates include some extreme positions that either undermine the relevance of neuroscience or overstate its role in determining legal responsibility. Here we adopt a conciliatory attitude, reaffirming the current value of neuroscience in jurisprudence and addressing its role in shifting normative attitudes about culpability. Adopting a balanced perspective about the interaction between two dynamic fields (science and law) allows for more fruitful consideration of practical changes likely to improve the way we engage in legal decision-making. Neuroscience provides a useful platform for addressing nuanced and multifaceted deterministic factors promoting antisocial behavior. Ultimately, we suggest that shifting normative attitudes about culpability vis-à-vis advancing neuroscience are not likely to promote major changes in the way we assign legal responsibility. Rather, it helps us to shed our harshest retributivist instincts in favor of more pragmatic strategies for combating the most conspicuous patterns promoting mass incarceration and recidivism.

Source-based morphometry: a decade of covarying structural brain patterns

In this paper, we review and discuss brain imaging studies which have used the source-based morphometry (SBM) approach over the past decade. SBM is a data-driven linear multivariate approach for decomposing structural brain imaging data into commonly covarying imaging components and subject-specific loading parameters. It is a well-established technique which has predominantly been used to study neuroanatomic differences between healthy controls and patients with neuropsychiatric diseases. We start by discussing the advantages of this technique over univariate analysis for imaging studies, followed by a discussion of results from recent studies which have successfully applied this methodology. We also present recent extensions of this framework including nonlinear SBM, biclustered independent component analysis (B-ICA) and conclude with the possible directions of work for future.

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.