No abstract

A pedophilic disorder is recognized for its impairment to the individual and for the harm it may cause to others. Pedophilia is often considered a side issue and research into the nature of pedophilia is delayed in comparison to research into other psychiatric disorders. However, with the increasing use of neuroimaging techniques, such as functional and structural magnetic resonance imaging (sMRI, fMRI), together with neuropsychological studies, we are increasing our knowledge of predisposing and accompanying factors contributing to pedophilia development. At the same time, we are faced with methodological challenges, such as group differences between studies, including age, intelligence, and comorbidities, together with a lack of careful assessment and control of child sexual abuse. Having this in mind, this review highlights the most important studies investigating pedophilia, with a strong emphasis on (neuro-) biological studies, combined with a brief explanation of research into normal human sexuality. We focus on some of the recent theories on the etiology of pedophilia such as the concept of a general neurodevelopmental disorder and/or alterations of structure and function in frontal, temporal, and limbic brain areas. With this approach, we aim to not only provide an update and overview but also a framework for future research and to address one of the most significant questions of how pedophilia may be explained by neurobiological and developmental alterations.

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Contrary to public perception, child sex offending (CSO) and paedophilia are not the same. Only half of all cases of CSO are motivated by paedophilic preference, and a paedophilic preference does not necessarily lead to CSO. However, studies that investigated clinical factors accompanying and contributing to paedophilia so far mainly relied on paedophiles with a history of CSO. The aim of this study was to distinguish between factors associated with sexual preference (paedophile versus non-paedophile) and offender status (with versus without CSO). Accordingly, a 2 (sexual preference) × 2 (offender status) factorial design was used for a comprehensive clinical assessment of paedophiles with and without a history of CSO (n = 83, n = 79 respectively), child sex offenders without paedophilia (n = 32) and healthy controls (n = 148). Results indicated that psychiatric comorbidities, sexual dysfunctions and adverse childhood experiences were more common among paedophiles and child sex offenders than controls. Offenders and non-offenders differed in age, intelligence, educational level and experience of childhood sexual abuse, whereas paedophiles and non-paedophiles mainly differed in sexual characteristics (e.g., additional paraphilias, onset and current level of sexual activity). Regression analyses were more powerful in segregating offender status than sexual preference (mean classification accuracy: 76% versus 68%). In differentiating between offence- and preference-related factors this study improves clinical understanding of both phenomena and may be used to develop scientifically grounded CSO prevention and treatment programmes. It also highlights that some deviations are not traceable to just one of these two factors, thus raising the issue of the mechanism underlying both phenomena.

Context: Accurately assessing sexual preference is important in the treatment of child sex offenders. Phallometry is the standard method to identify sexual preference; however, this measure has been criticized for its intrusiveness and limited reliability. Objective: To evaluate whether spatial response pattern to sexual stimuli as revealed by a change in the blood oxygen level-dependent signal facilitates the identification of pedophiles. Design: During functional magnetic resonance imaging, pedophilic and nonpedophilic participants were briefly exposed to same-and opposite-sex images of nude children and adults. We calculated differences in blood oxygen level-dependent signals to child and adult sexual stimuli for each participant. The corresponding contrast images were entered into a group analysis to calculate whole-brain difference maps between groups. We calculated an expression value that corresponded to the group result for each participant. These expression values were submitted to 2 different classification algorithms: Fisher linear discriminant analysis and κ-nearest neighbor analysis. This classification procedure was cross-validated using the leave-one-out method.