Mapping the Psychopathic Brain: Divergent Neuroimaging Findings converge onto a Common Brain Network

Jules R. Dugre, Stephane A De Brito
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Abstract

Psychopathy is a personality disorder characterized by a constellation of interpersonal, affective, lifestyle, and antisocial features. However, its neural underpinnings remain poorly understood because functional neuroimaging studies have produced disparate findings. Here, we tackled this lack of replication by investigating whether peak coordinates of studies on psychopathy could in fact map onto a common functional connectivity network. An updated meta-analysis of 23 functional neuroimaging studies (534 cases vs 594 controls) first revealed no significant regional spatial convergence. However, using functional connectomes of 1,000 healthy participants, we demonstrated that the heterogeneous study findings do indeed converge onto a common brain network with a replicability reaching up to 85.2% across studies. We subsequently showed strong associations between this Psychopathy Network and a lesion network of 17 lesion sites causally linked to antisocial behaviors, as well as its association with neurotransmission systems and genetic markers previously implicated in the pathophysiology of psychopathy. Taken together, our study highlights the importance of examining the neural correlates of psychopathy from a network perspective, which can be validated using a multilevel approach, encompassing neural, genetic and neurochemical data. Ultimately, this approach may pave the way for novel and more personalised treatments.
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绘制精神变态者的大脑图谱:不同的神经影像学发现汇聚成一个共同的大脑网络
精神变态是一种人格障碍,具有人际关系、情感、生活方式和反社会特征。然而,由于功能神经影像学研究的结果不尽相同,人们对其神经基础仍然知之甚少。在此,我们通过研究精神变态研究的峰值坐标是否实际上映射到了一个共同的功能连接网络,来解决这一缺乏复制的问题。对 23 项功能神经影像学研究(534 例病例与 594 例对照)进行的最新荟萃分析首先显示,这些研究没有发现明显的区域空间趋同性。然而,通过使用 1000 名健康参与者的功能连接组,我们证明了不同的研究结果确实汇聚到了一个共同的大脑网络上,不同研究之间的可复制性高达 85.2%。随后,我们还证明了这一精神病态网络与一个由 17 个与反社会行为有因果关系的病变部位组成的病变网络之间的密切联系,以及它与神经传递系统和遗传标记之间的联系,这些系统和标记以前曾被认为与精神病态的病理生理学有关。综上所述,我们的研究强调了从网络角度研究精神变态的神经相关性的重要性,这种研究可以通过多层次的方法进行验证,包括神经、遗传和神经化学数据。最终,这种方法可能会为新颖和更加个性化的治疗铺平道路。
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