Der Einfluss des richterlichen Auftrags auf die Qualität der Arbeit von Sachverständigen im Rahmen der Prognosebegutachtung

Im Jahr 2006 wurden von einer Arbeitsgruppe (nicht verbindliche) Mindestanforderungen für Prognosegutachten formuliert, die bereits im Gutachtenauftrag Berücksichtigung finden sollen. Insbesondere sollen die Sachverständigen sich an folgenden vier prognostischen Fragestellungen orientieren: (1) der Wahrscheinlichkeit erneuter Straftaten, (2) der Art, Häufigkeit und des Schweregrades erneuter Straftaten, (3) möglicher risikoreduzierender Maßnahmen und (4) möglicher risikoerhöhender Umstände. In einer empirischen Studie werden N = 787 Prognosegutachten von Gewalt- und Sexualstraftätern, die zwischen 1999 und 2016 erstellt worden sind, hinsichtlich der richterlichen Auftragsstellung und deren Beantwortung analysiert. Einen Teil der Stichprobe bilden n = 412 externe Prognosegutachten der JVA Freiburg und n = 375 Prognosegutachten der Abteilung für Forensische Psychiatrie der Klinik und Poliklinik für Psychiatrie und Psychotherapie der Ludwig-Maximilians-Universität München. Es wurden n = 407 (Freiburg: 253, München: 154) vor 2006 erstellt und n = 380 (Freiburg: 159, München: 221) nach 2006. Es zeigt sich, dass ab 2006 die Münchner Abteilung eine statistisch signifikant häufigere Beantwortung der Fragestellungen (1), (2) und (4) verzeichnet, wohingegen keine Veränderung bei den externen Prognosegutachten der JVA Freiburg festzustellen ist. Es wird argumentiert, dass in universitären Einrichtungen eher wissenschaftliche Empfehlungen aufgegriffen werden als in der allgemeinen Gutachterpraxis. Zudem wird die Bedeutung der Bezugnahme auf die prognostischen Fragestellungen bereits im richterlichen Gutachtenauftrag betont, da statistisch gezeigt werden kann, dass diese zu einer konkreteren Beantwortung durch die Sachverständigen führt

Neuroanatomical correlates of aggressiveness: a case-control voxel- and surface-based morphometric study

Aggression occurs across the population ranging on a symptom continuum. Most previous studies have used magnetic resonance imaging in clinical/forensic samples, which is associated with several confounding factors. The present study examined structural brain characteristics in two healthy samples differing only in their propensity for aggressive behavior. Voxel- and surface-based morphometry (SBM) analyses were performed on 29 male martial artists and 32 age-matched male controls. Martial artists had significantly increased mean gray matter volume in two frontal (left superior frontal gyrus and bilateral anterior cingulate cortex) and one parietal (bilateral posterior cingulate gyrus and precuneus) brain clusters compared to controls (whole brain: p < 0.001, cluster level: family-wise error (FWE)-corrected). SBM analyses revealed a trend for greater gyrification indices in martial artists compared to controls in the left lateral orbital frontal cortex and the left pars orbitalis (whole brain: p < 0.001, cluster level: FWE-corrected). The results indicate brain structural differences between martial artists and controls in frontal and parietal brain areas critical for emotion processing/inhibition of emotions as well as empathic processes. The present study highlights the importance of studying healthy subjects with a propensity for aggressive behavior in future structural MRI research on aggression

S100B Serum Levels in Schizophrenia Are Presumably Related to Visceral Obesity and Insulin Resistance

Elevated blood levels of S100B in schizophrenia have so far been mainly attributed to glial pathology, as S100B is produced by astro- and oligodendroglial cells and is thought to act as a neurotrophic factor with effects on synaptogenesis, dopaminergic and glutamatergic neutrotransmission. However, adipocytes are another important source of S100B since the concentration of S100B in adipose tissue is as high as in nervous tissue. Insulin is downregulating S100B in adipocytes, astrocyte cultures and rat brain. As reviewed in this paper, our recent studies suggest that overweight, visceral obesity, and peripheral/cerebral insulin resistance may be pivotal for at least part of the elevated S100B serum levels in schizophrenia. In the context of this recently identified framework of metabolic disturbances accompanying S100B elevation in schizophrenia, it rather has to be attributed to systemic alterations in glucose metabolism than to be considered a surrogate marker for astrocyte-specific pathologies

Glutamatergic and Resting-State Functional Connectivity Correlates of Severity in Major Depression – The Role of Pregenual Anterior Cingulate Cortex and Anterior Insula

Glutamatergic mechanisms and resting-state functional connectivity alterations have been recently described as factors contributing to major depressive disorder (MDD). Furthermore, the pregenual anterior cingulate cortex (pgACC) seems to play an important role for major depressive symptoms such as anhedonia and impaired emotion processing. We investigated 22 MDD patients and 22 healthy subjects using a combined magnetic resonance spectroscopy (MRS) and resting-state functional magnetic resonance imaging (fMRI) approach. Severity of depression was rated using the 21-item Hamilton depression scale (HAMD) and patients were divided into severely and mildly depressed subgroups according to HAMD scores. Because of their hypothesized role in depression we investigated the functional connectivity between pgACC and left anterior insular cortex (AI). The sum of Glutamate and Glutamine (Glx) in the pgACC, but not in left AI, predicted the resting-state functional connectivity between the two regions exclusively in depressed patients. Furthermore, functional connectivity between these regions was significantly altered in the subgroup of severely depressed patients (HAMD > 15) compared to healthy subjects and mildly depressed patients. Similarly the Glx ratios, relative to Creatine, in the pgACC were lowest in severely depressed patients. These findings support the involvement of glutamatergic mechanisms in severe MDD which are related to the functional connectivity between pgACC and AI and depression severity

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

BackgroundChild 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.AimTo use machine learning and MRI data to identify PO individuals.MethodsFrom 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.ResultsThe classifier discriminated PO from HC individuals with a balanced accuracy of 75.5% (sensitivity = 64.3%, specificity = 86.7%, P5000 = 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.ConclusionAberrant 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

Two Sides of One Coin: A Comparison of Clinical and Neurobiological Characteristics of Convicted and Non-Convicted Pedophilic Child Sexual Offenders

High prevalence of child sexual offending stand in contradiction to low conviction rates (one-tenth at most) of child sexual offenders (CSOs). Little is known about possible differences between convicted and non-convicted pedophilic CSOs and why only some become known to the judicial system. This investigation takes a closer look at the two sides of "child sexual offending" by focusing on clinical and neurobiological characteristics of convicted and non-convicted pedophilic CSOs as presented in the Neural Mechanisms Underlying Pedophilia and sexual offending against children (NeMUP)*-study. Seventy-nine male pedophilic CSOs were examined, 48 of them convicted. All participants received a thorough clinical examination including the structured clinical interview (SCID), intelligence, empathy, impulsivity, and criminal history. Sixty-one participants (38 convicted) underwent an inhibition performance task (Go/No-go paradigm) combined with functional magnetic resonance imaging (fMRI). Convicted and non-convicted pedophilic CSOs revealed similar clinical characteristics, inhibition performances, and neuronal activation. However, convicted subjects' age preference was lower (i.e., higher interest in prepubescent children) and they had committed a significantly higher number of sexual offenses against children compared to non-convicted subjects. In conclusion, sexual age preference may represent one of the major driving forces for elevated rates of sexual offenses against children in this sample, and careful clinical assessment thereof should be incorporated in every preventive approach

How can forensic psychiatry contribute to legal and ethical controversies in society? What everybody should know about forensic psychiatry

Abstract: This chapter considers important topics in forensic psychiatry. First, several definition of forensic psychiatry are given and seven specialistic forensic skills are considered. Next, the relationship between various psychiatric disorders and violence is explained. Legal issues in the field of forensic psychiatry are explained. The importance of risk assessment and risk management is highlighted. Some major issues about correctional psychiatry and forensic psychiatric treatment are explained. Finally, some key figures about European forensic psychiatry, ethical issues, and future directions are addressed

Precision Psychiatry and the Contribution of Brain Imaging and Other Biomarkers

'Precision Psychiatry' as the psychiatric variant of 'Precision Medicine' aims to provide high-level diagnosis and treatment based on robust biomarkers and tailored to the individual clinical, neurobiological, and genetic constitution of the patient. The specific peculiarity of psychiatry, in which disease entities are normatively defined based on clinical experience and are also significantly influenced by contemporary history, society and philosophy, has so far made the search for valid and reliable psychobiological connections difficult. Nevertheless, considerable progress has now been made in all areas of psychiatric research, made possible above all by the critical review and renewal of previous concepts of disease and psychopathology, the increased orientation towards neurobiology and genetics, and in particular the use of machine learning methods. Notably, modern machine learning methods make it possible to integrate high-dimensional and multimodal data sets and generate models which provide new psychobiological insights and offer the possibility of individualized, biomarker-driven single-subject prediction of diagnosis, therapy response and prognosis. The aim of the present review is therefore to introduce the concept of 'Precision Psychiatry' to the interested reader, to concisely present modern, machine learning methods required for this, and to clearly present the current state and future of biomarker-based 'precision psychiatry'