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(EIGMA)倡议代表着一项国际团队科学努力，旨在通过对全球研究小组的神经成像数据进行元分析来揭示遗传学和疾病是如何影响大脑的(Bearden&Thompson，2017)。这一方法旨在通过促进协作分析来克服小规模和方法上不同的神经科学研究的局限性，这最终可能产生更可靠和可复制的结果。Enigma目前由50多个工作组组成，涵盖神经科学的各个领域和一些精神障碍(Thompson等人，2019年)。最近，谜反社会行为工作组已经成立，这为该项目提供了背景。反社会行为(AB)包括暴力、侵略和违反规则，包括严重侵犯他人权利。严重的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)或精神变态的核心。这些疾病很普遍，并与各种负面后果有关，包括青少年犯罪、药物滥用和糟糕的身心健康，这意味着对它们的研究很重要。神经成像研究发现，与健康对照组相比，严重AB患者的大脑结构存在差异。然而，研究结果并不一致，而且许多研究结果还没有被复制。这可能与之前研究的局限性有关。首先，由于样本较小，许多研究在统计上不足以发现基于对其他精神障碍的研究可能会出现的微小大脑差异。其次，方法上的差异(例如，在MRI数据采集或分析方法方面)限制了结果的可比性。最后，研究经常调查不同的参与者群体(例如，不同的发病年龄)，而不考虑这些差异。这可能会混淆结果，并限制我们对这些因素的影响的理解。在谜的背景下，这个项目通过结合来自研究AB的领先研究小组的神经成像数据来解决这些限制(预计样本量超过N=5000)。这项研究的目的是找出大脑结构标志物，将患有AB相关诊断(CD、ASPD/精神病)的人与健康对照组区分开来，并确定这些标志物在整个生命周期中是否不同，以及它们是否受到发病年龄或共病等因素的影响。由于它将涉及到迄今为止对患有AB的个体进行的最全面的脑结构变化分析，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段的研究，使我们能够检查AB患者大脑变化的发育稳定性。此外，对不同参与者特征的探索提供了通过识别具有生物学意义的AB亚型来为未来修订诊断和分类系统(例如，DSM)提供信息的可能性。最后，我们也许能够确定预测结果的神经标记物(例如，持久性与缓解性)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.