Impersonal and Predatory Relations With Child Victims of Sexual Assault: Pedophilic Interest and Early Childhood Abuse

Patterns of risk-Using machine learning and structural neuroimaging to identify pedophilic offenders

Background

Child sexual abuse (CSA) has become a focal point for lawmakers, law enforcement, and mental health professionals. With high prevalence rates around the world and far-reaching, often chronic, individual, and societal implications, CSA and its leading risk factor, pedophilia, have been well investigated. This has led to a wide range of clinical tools and actuarial instruments for diagnosis and risk assessment regarding CSA. However, the neurobiological underpinnings of pedosexual behavior, specifically regarding hands-on pedophilic offenders (PO), remain elusive. Such biomarkers for PO individuals could potentially improve the early detection of high-risk PO individuals and enhance efforts to prevent future CSA.

Aim

To use machine learning and MRI data to identify PO individuals.

Methods

From a single-center male cohort of 14 PO individuals and 15 matched healthy control (HC) individuals, we acquired diffusion tensor imaging data (anisotropy, diffusivity, and fiber tracking) in literature-based regions of interest (prefrontal cortex, anterior cingulate cortex, amygdala, and corpus callosum). We trained a linear support vector machine to discriminate between PO and HC individuals using these WM microstructure data. Post hoc, we investigated the PO model decision scores with respect to sociodemographic (age, education, and IQ) and forensic characteristics (psychopathy, sexual deviance, and future risk of sexual violence) in the PO subpopulation. We assessed model specificity in an external cohort of 53 HC individuals.

Results

The classifier discriminated PO from HC individuals with a balanced accuracy of 75.5% (sensitivity = 64.3%, specificity = 86.7%, P ₅₀₀₀ = 0.018) and an out-of-sample specificity to correctly identify HC individuals of 94.3%. The predictive brain pattern contained bilateral fractional anisotropy in the anterior cingulate cortex, diffusivity in the left amygdala, and structural prefrontal cortex-amygdala connectivity in both hemispheres. This brain pattern was associated with the number of previous child victims, the current stance on sexuality, and the professionally assessed risk of future sexual violent reoffending.

Conclusion

Aberrant white matter microstructure in the prefronto-temporo-limbic circuit could be a potential neurobiological correlate for PO individuals at high-risk of reoffending with CSA. Although preliminary and exploratory at this point, our findings highlight the general potential of MRI-based biomarkers and particularly WM microstructure patterns for future CSA risk assessment and preventive efforts.

Motivational and Cognitive Factors in Pathways to Offending Amongst Women who Have Sexually Offended

Data-driven models, addressing the internal motivations, pathways to offending, and supportive cognitions amongst women who have sexually offended have emerged in recent years, with significant consequences for understanding risk factors and developing treatment programmes. In the current research, recent empirically based models were explored in a national sample consisting of all women who had committed sexual offences and were assessed over a 12-year period (n = 28). The utility of the motivational and cognitive models tested was supported, although not all cases could be easily coded, and new categories are suggested in order to better characterize the unique aspects of sex offending amongst women who have sexually offended. Key findings included fewer women than expected who reported being coerced by a male partner, frequent conjunction between sex and intimacy needs, and a lack of a clear relationship between offence-related cognitions and motivational pathways. The results support the importance of integrating and further developing gender-specific clinical factors within a gender-neutral treatment paradigm.