The charm of structural neuroimaging in insanity evaluations. guidelines to avoid misinterpretation of the findings

Despite the popularity of structural neuroimaging techniques in twenty-first-century research, its results have had limited translational impact in real-world settings, where inferences need to be made at the individual level. Structural neuroimaging methods are now introduced frequently to aid in assessing defendants for insanity in criminal forensic evaluations, with the aim of providing “convergence” of evidence on the mens rea of the defendant. This approach may provide pivotal support for judges’ decisions. Although neuroimaging aims to reduce uncertainty and controversies in legal settings and to increase the objectivity of criminal rulings, the application of structural neuroimaging in forensic settings is hampered by cognitive biases in the evaluation of evidence that lead to misinterpretation of the imaging results. It is thus increasingly important to have clear guidelines on the correct ways to apply and interpret neuroimaging evidence. In the current paper, we review the literature concerning structural neuroimaging in court settings with the aim of identifying rules for its correct application and interpretation. These rules, which aim to decrease the risk of biases, focus on the importance of (i) descriptive diagnoses, (ii) anatomo-clinical correlation, (iii) brain plasticity and (iv) avoiding logical fallacies, such as reverse inference. In addition, through the analysis of real forensic cases, we describe errors frequently observed due to incorrect interpretations of imaging. Clear guidelines for both the correct circumstances for introducing neuroimaging and its eventual interpretation are defined

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

Validation of a new instrument to guide and support insanity evaluations: the defendant’s insanity assessment support scale (DIASS)

The insanity defense represents one of the most controversial and debated evaluations performed by forensic psychiatrists and psychologists. Despite the variation among different jurisdictions, in Western countries, the legal standards for insanity often rely on the presence of cognitive and/or volitional impairment of the defendant at the time of the crime. We developed the defendant’s insanity assessment support scale (DIASS) based on a wide view of competent decision-making, which reflects core issues relevant to legal insanity in many jurisdictions. To assess the characteristics of the DIASS we asked 40 forensic experts (16% women; years of experience = 20.6 ± 12.9) to evaluate 10 real-life derived forensic cases with the DIASS; cases included defendants’ psychiatric symptom severity, evaluated through the 24-itemBrief Psychiatric Rating Scale (BPRS). Exploratory factor analysis by principal axis factoring was conducted, which disclosed a two-factor solution explaining 57.6% of the total variance. The DIASS showed a good internal consistency (Cronbach’s alpha = 0.86), and substantial inter-rater reliability (Cohen’s kappa = 0.72). The capacities analyzed through the DIASS were mainly affected by mania/excitement and psychotic dimensions in nonresponsible and with substantially diminished responsibility defendants, while by hostility and negative symptoms in responsible defendants. The DIASS proved to be an effective psychometric tool to guide and structure insanity defense evaluations, in order to improve their consistency and reliability

Sex offenders in jail: A mini review of treatment programs and outcomes

The debate in the scientific literature about sex offender treatment and its effectiveness remains divided and controversial. Several studies have uncovered that psychological treatment reduces the risk of recidivism in such subjects (Gallagher, et al., 1999; Hall, 1995; Hanson, et al., 2009; Hanson et al., 2002; Lösel & Schmucker, 2005; Reitzel & Carbonell, 2006; Schmucker & Lösel, 2008, 2015), whilst other studies have shown that there is insufficient evidence for this conclusion (Furby, Weinrott, & Blackshaw, 1989; Harris, Rice, & Quinsey, 1998; Kenworthy, et al., 2004; Rice & Harris, 2003). In order to clarify which treatments are applied to the sex offender population in jail, together with the associations between these treatments and reduced risk of recidivism, the present study comprised a review of the literature to determine the current state of research in this area

Forensic psychiatric evaluations of defendants: Italy and the Netherlands compared

Background: Forensic psychiatric practices and provisions vary considerably across jurisdictions. The diversity provides the possibility to compare forensic psychiatric practices, as we will do in this paper regarding Italy and the Netherlands. Aim: We aim to perform a theoretical analysis of legislations dealing with the forensic psychiatric evaluation of defendants, including legal insanity and the management of mentally ill offenders deemed insane. This research is carried out not only to identify similarities and differences regarding the assessment of mentally ill offenders in Italy and the Netherlands, but, in addition, to identify strengths and weaknesses of the legislation and procedures used for the evaluation of the mentally ill offenders in the two countries. Results: Italy and the Netherlands share some basic characteristics of their criminal law systems. Yet, forensic psychiatric practices differ significantly, even if we consider only evaluations of defendants. A strong point of Italy concerns its test for legal insanity which defines the legal norm and enables a straightforward communication between the experts and the judges on this crucial matter. A strong point of the Netherlands concerns more standardized practices including guidelines and the use of risk assessment tools, which enable better comparisons and scientific research in this area. Conclusions: We argue that there appears to be room for improvement on both sides with regards to the evaluation of mentally ill offenders. More generally, a transnational approach to these issues, as applied in this paper, could help to advance forensic psychiatric services in different legal systems

The Development of a Short Version of the SIMS Using Machine Learning to Detect Feigning in Forensic Assessment

In the present study, we applied machine learning techniques to evaluate whether the Structured Inventory of Malingered Symptomatology (SIMS) can be reduced in length yet maintain accurate discrimination between consistent participants (i.e., presumed truth tellers) and symptom producers. We applied machine learning item selection techniques on data from Mazza et al. (2019c) to identify the minimum number of original SIMS items that could accurately distinguish between consistent participants, symptom accentuators, and symptom producers in real personal injury cases. Subjects were personal injury claimants who had undergone forensic assessment, which is known to incentivize malingering and symptom accentuation. Item selection yielded short versions of the scale with as few as 8 items (to differentiate between consistent participants and symptom producers) and as many as 10 items (to differentiate between consistent and inconsistent participants). The scales had higher classification accuracy than the original SIMS and did not show the bias that was originally reported between false positives and false negatives

Forensic value of genetic variants associated with anti‐social behavior

Insanity defense is sometimes invoked in criminal cases, and its demonstration is usually based on a multifactorial contribution of behavioural, clinical, and neurological elements. Neuro-radiological evidence of structural alterations in cerebral areas that involve decision‐making and moral reasoning is often accepted as a useful tool in these evaluations. On the other hand, the genetic predisposition to anti‐social behavior is still controversial. In this paper, we describe two cases of violent crimes committed by young carriers of genetic variants associated with personality disorder; both the defendants claimed to be insane at the time of the crime. We discuss these cases and review the scientific literature regarding the relationship between legal incapaci-ty/predisposition to criminal behavior and genetic mutations. In conclusion, despite some genetic variants being able to influence several cognitive processes (like moral judgement and impulse control), there is currently no evidence that carriers of these mutations are, per se, incapable of intentionally committing crimes

Neuromuscular Control Modelling of Human Perturbed Posture Through Piecewise Affine Autoregressive With Exogenous Input Models

In this study, the neuromuscular control modeling of the perturbed human upright stance is assessed through piecewise affine autoregressive with exogenous input (PWARX) models. Ten healthy subjects underwent an experimental protocol where visual deprivation and cognitive load are applied to evaluate whether PWARX can be used for modeling the role of the central nervous system (CNS) in balance maintenance in different conditions. Balance maintenance is modeled as a single-link inverted pendulum; and kinematic, dynamic, and electromyography (EMG) data are used to fit the PWARX models of the CNS activity. Models are trained on 70% and tested on the 30% of unseen data belonging to the remaining dataset. The models are able to capture which factors the CNS is subjected to, showing a fitting accuracy higher than 90% for each experimental condition. The models present a switch between two different control dynamics, coherent with the physiological response to a sudden balance perturbation and mirrored by the data-driven lag selection for data time series. The outcomes of this study indicate that hybrid postural control policies, yet investigated for unperturbed stance, could be an appropriate motor control paradigm when balance maintenance undergoes external disruption

How to improve compliance with protective health measures during the covid-19 outbreak. Testing a moderated mediation model and machine learning algorithms

In the wake of the sudden spread of COVID-19, a large amount of the Italian population practiced incongruous behaviors with the protective health measures. The present study aimed at examining psychological and psychosocial variables that could predict behavioral compliance. An online survey was administered from 18–22 March 2020 to 2766 participants. Paired sample t-tests were run to compare efficacy perception with behavioral compliance. Mediation and moderated mediation models were constructed to explore the association between perceived efficacy and compliance, mediated by self-efficacy and moderated by risk perception and civic attitudes. Machine learning algorithms were trained to predict which individuals would be more likely to comply with protective measures. Results indicated significantly lower scores in behavioral compliance than efficacy perception. Risk perception and civic attitudes as moderators rendered the mediating effect of self-efficacy insignificant. Perceived efficacy on the adoption of recommended behaviors varied in accordance with risk perception and civic engagement. The 14 collected variables, entered as predictors in machine learning models, produced an ROC area in the range of 0.82–0.91 classifying individuals as high versus low compliance. Overall, these findings could be helpful in guiding age-tailored information/advertising campaigns in countries affected by COVID-19 and directing further research on behavioral compliance

Evidence-based considerations exploring relations between sars-cov-2 pandemic and air pollution: Involvement of pm2.5-mediated up-regulation of the viral receptor ace-2

The COVID-19/SARS-CoV-2 pandemic struck health, social and economic systems worldwide, and represents an open challenge for scientists —coping with the high inter-individual variability of COVID-19, and for policy makers —coping with the responsibility to understand environmental factors affecting its severity across different geographical areas. Air pollution has been warned of as a modifiable factor contributing to differential SARS-CoV-2 spread but the biological mechanisms underlying the phenomenon are still unknown. Air quality and COVID-19 epidemiological data from 110 Italian provinces were studied by correlation analysis, to evaluate the association between particulate matter (PM)2.5 concentrations and incidence, mortality rate and case fatality risk of COVID-19 in the period 20 February–31 March 2020. Bioinformatic analysis of the DNA sequence encoding the SARS-CoV-2 cell receptor angiotensin-converting enzyme 2 (ACE-2) was performed to identify consensus motifs for transcription factors mediating cellular response to pollutant insult. Positive correlations between PM2.5 levels and the incidence (r = 0.67, p < 0.0001), the mortality rate (r = 0.65, p < 0.0001) and the case fatality rate (r = 0.7, p < 0.0001) of COVID-19 were found. The bioinformatic analysis of the ACE-2 gene identified nine putative consensus motifs for the aryl hydrocarbon receptor (AHR). Our results confirm the supposed link between air pollution and the rate and outcome of SARS-CoV-2 infection and support the hypothesis that pollution-induced over-expression of ACE-2 on human airways may favor SARS-CoV-2 infectivity