Meta-Analysis of Differences in Brain Structure in Severe Antisocial Behaviour

通过荟萃分析增强神经成像遗传学（ENIGMA）计划代表了一项国际团队科学努力，旨在通过对来自地球仪研究小组的神经成像数据进行元分析，揭示遗传学和疾病如何影响大脑（比尔登和汤普森，2017年）。这种方法旨在通过促进协作分析来克服小规模和方法异质性神经科学研究的局限性，这可能最终产生更强大和可复制的结果。ENIGMA目前包括50多个工作组，涵盖神经科学的各个领域和许多精神疾病（Thompson et al.，2019年）。最近，ENIGMA反社会行为工作组已经成立，为这个项目提供了背景。反社会行为（AB）包括暴力，侵略和违反规则，包括严重侵犯他人的权利。严重AB是DSM诊断儿童或青少年行为障碍（CD）和成人反社会人格障碍（ASPD）或精神病的核心。这些疾病普遍存在，并与各种负面结果有关，包括犯罪，药物滥用和身心健康状况不佳，这意味着研究它们很重要。神经影像学研究已经确定了与健康对照相比，严重AB患者的大脑结构差异。然而，调查结果并不一致，许多调查结果没有被复制。这可能与以前研究的局限性有关。首先，由于样本量小，许多研究在统计学上没有足够的力量来检测基于其他精神疾病研究可能预期的微小大脑差异。第二，方法上的差异（例如，在MRI数据采集或分析方法中）限制了结果的可比性。最后，研究经常调查异质参与者群体（例如，不同的发病年龄）而不考虑这种差异。这可能会混淆结果，并限制我们对这些因素影响的理解。在ENIGMA的背景下，该项目通过结合来自研究AB的领先研究小组的神经影像学数据（预计样本量超过N = 5000）来解决这些局限性。该研究旨在确定结构性大脑标记物，这些标记物可以将患有AB相关诊断（CD，ASPD/精神病）的个体与健康对照区分开来，并确定这些标记物在整个生命周期中是否存在差异，以及它们是否受到发病年龄或合并症等因素的影响。由于它将涉及迄今为止对AB患者大脑结构变化的最全面分析，因此该项目将帮助我们确定AB的强大结构相关性。这将是第一项同时调查不同年龄组的研究，使我们能够检查AB大脑变化的发育稳定性。此外，对各种参与者特征的探索提供了为诊断和分类系统的未来修订提供信息的可能性（例如，DSM）通过鉴定AB的生物学上有意义的亚型。最后，我们可能能够识别预测结果的神经标记物（例如，持久性与停止）或治疗反应。

The Enhancing Neuro-Imaging Genetics through Meta-Analysis (ENIGMA) initiative represents an international team-science effort aiming to uncover how genetics and disease influence the brain by meta-analysing neuroimaging data from research groups across the globe (Bearden & Thompson, 2017). This approach aims to overcome the limitations of small-scale and methodologically heterogenous neuroscientific research by facilitating collaborative analyses, which may ultimately produce more robust and replicable findings. ENIGMA currently comprises more than 50 working groups, covering various areas of neuroscience and a number of psychiatric disorders (Thompson et al., 2019). Recently, the ENIGMA Antisocial Behaviour working group has been established, which provides the context for this project. Antisocial behaviour (AB) comprises violence, aggression, and rule-breaking, including severe violations of others' rights. Severe AB is central to the DSM diagnoses of conduct disorder (CD) in children or adolescents, and antisocial personality disorder (ASPD) or psychopathy in adults. These disorders are prevalent and associated with various negative outcomes including delinquency, substance abuse and poor physical and mental health, meaning that it is important to study them. Neuroimaging research has identified structural brain differences in those with severe AB compared to healthy controls. However, findings are inconsistent, and many have not been replicated. This may be linked to limitations of previous research. First, due to small samples, many studies were statistically underpowered to detect the small brain differences that might be expected based on research into other psychiatric disorders. Second, methodological differences (e.g., in MRI data acquisition or analysis methods) limit the comparability of findings. Lastly, studies often investigate heterogenous participant groups (e.g., differing age-of-onsets) without accounting for such differences. This may confound results and limits our understanding of the influence of these factors. In the context of ENIGMA, this project addresses these limitations by combining neuroimaging data from leading research groups studying AB (with a projected sample size of over N = 5000). The study aims to identify structural brain markers that robustly distinguish individuals with AB-related diagnoses (CD, ASPD/psychopathy) from healthy controls, and determine whether these markers differ across the lifespan and whether they are impacted by factors such as age-of-onset or comorbidity. As it will involve the most comprehensive analysis of structural brain alterations in individuals with AB performed to date, this project will help us identify robust structural correlates of AB. It will be the first study to simultaneously investigate different age groups, allowing us to examine the developmental stability of brain alterations in AB. Moreover, the exploration of various participant characteristics offers the potential to inform future revisions of the diagnostic and classification systems (e.g., DSM) by identifying biologically-meaningful subtypes of AB. Lastly, we might be able to identify neural markers that predict outcomes (e.g., persistence versus desistance) or treatment response.