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

David Popovic; Maximilian Wertz; Carolin Geisler; Joern Kaufmann; Markku Lähteenvuo; Johannes Lieslehto; Joachim Witzel; Bernhard Bogerts; Martin Walter; Peter Falkai; Nikolaos Koutsouleris; Kolja Schiltz

doi:10.3389/fpsyt.2023.1001085

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

Front Psychiatry. 2023 Apr 20:14:1001085. doi: 10.3389/fpsyt.2023.1001085. eCollection 2023.

Authors

David Popovic^{1

2

3

4}, Maximilian Wertz^{1

2}, Carolin Geisler⁵, Joern Kaufmann⁶, Markku Lähteenvuo^{7

8}, Johannes Lieslehto⁷, Joachim Witzel⁹, Bernhard Bogerts^{10

11}, Martin Walter¹², Peter Falkai^{1

3

4}, Nikolaos Koutsouleris^{1

3

4

13}, Kolja Schiltz^{1

2}

Affiliations

¹ Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany.
² Department of Forensic Psychiatry, Ludwig-Maximilians-University Munich, Munich, Germany.
³ International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany.
⁴ Max Planck Institute of Psychiatry, Munich, Germany.
⁵ Department of Dermatology, Venereology, and Allergology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
⁶ Department of Neurology, Otto-von-Guericke-University, Magdeburg, Germany.
⁷ Department of Forensic Psychiatry, University of Eastern Finland, Niuvanniemi Hospital, Kuopio, Finland.
⁸ Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland.
⁹ Central State Forensic Psychiatric Hospital of Saxony-Anhalt, Uchtspringe, Germany.
¹⁰ Salus Institut, Salus gGmbH, Magdeburg, Germany.
¹¹ Department of Psychiatry and Psychotherapy, Otto-von-Guericke-University, Magdeburg, Germany.
¹² Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.
¹³ Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Abstract

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.

Keywords: MRI; child sexual abuse (CSA); forensic psychiatry; machine learning; pedophilia; support vector machines.

Grants and funding

DP was supported by the “Else-Kröner-Fresenius-Stiftung” through the Clinician Scientist Program “EKFS-Translational Psychiatry”. This work was also supported by the Bavarian State Ministry of Justice through the “Kein Täter werden” project as well as by the PRONIA project as funded by the European Union 7th Framework Program (grant 602152). NK was supported through grants from NIH (U01MH124639-01; ProNET), the Wellcome Trust, the German Innovation Fund (CARE project), the German Federal Ministry of Education and Research (COMMITMENT and BEST projects), as well as ERA PerMed (IMPLEMENT project). PF had received and was currently receiving grants from several national and international foundations and institutions, e.g., from the “German Science Foundation,” the “German Ministry of Science,” and the “German Ministry of Health”. The funding organizations were not involved in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.