Connectomic intermediate phenotypes for psychiatric disorders.
Connectomic intermediate phenotypes for psychiatric disorders.
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DOI：
10.3389/fpsyt.2012.00032
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发表时间：
2012
影响因子：
4.7
通讯作者：
Bullmore ET
中科院分区：
文献类型：
--
作者：
Fornito A;Bullmore ET
精神疾病是具有复杂遗传基础的表型异质性实体。为了减轻这种复杂性，许多研究人员研究了所谓的中间表型（IPs），它被认为比明显的精神综合征更直接地反映了候选遗传风险变异的生理影响。磁共振成像（MRI）是测量这些表型的一种特别流行的技术，因为它允许对体内大脑结构和功能的各个方面进行调查。然而，这方面的大部分工作都集中在相对简单的测量上，这些测量是对特定大脑区域的生理或组织完整性的变化进行量化，这与新兴的共识相矛盾，即大多数主要精神疾病不是由一个或几个大脑区域的孤立功能障碍引起的，而是由分布式神经回路内部和之间的干扰相互作用引起的；也就是说，它们是大脑连接障碍。最近，用于全面绘制大脑整个连接结构（称为人类连接组）的新型MRI技术的普及，为理解与精神障碍有关的遗传变异如何影响不同的神经回路提供了丰富的工具。在本文中，我们回顾了使用这些连接组技术来了解遗传变异如何影响人类大脑网络的连通性和拓扑结构的研究。我们强调了最近来自双胞胎和成像遗传学研究的证据，表明精神疾病的候选风险变异，如SLC6A4， MAOA， ZNF804A和APOE，在分布式神经系统水平上表征的IPs可能比在空间定位的大脑区域水平上表征的IPs更高。研究结果表明，成像连接组学为理解精神疾病的遗传风险如何通过改变人类连接组的结构和功能来表达提供了一个强有力的框架。
Psychiatric disorders are phenotypically heterogeneous entities with a complex genetic basis. To mitigate this complexity, many investigators study so-called intermediate phenotypes (IPs) that putatively provide a more direct index of the physiological effects of candidate genetic risk variants than overt psychiatric syndromes. Magnetic resonance imaging (MRI) is a particularly popular technique for measuring such phenotypes because it allows interrogation of diverse aspects of brain structure and function in vivo. Much of this work however, has focused on relatively simple measures that quantify variations in the physiology or tissue integrity of specific brain regions in isolation, contradicting an emerging consensus that most major psychiatric disorders do not arise from isolated dysfunction in one or a few brain regions, but rather from disturbed interactions within and between distributed neural circuits; i.e., they are disorders of brain connectivity. The recent proliferation of new MRI techniques for comprehensively mapping the entire connectivity architecture of the brain, termed the human connectome, has provided a rich repertoire of tools for understanding how genetic variants implicated in mental disorder impact distinct neural circuits. In this article, we review research using these connectomic techniques to understand how genetic variation influences the connectivity and topology of human brain networks. We highlight recent evidence from twin and imaging genetics studies suggesting that the penetrance of candidate risk variants for mental illness, such as those in SLC6A4, MAOA, ZNF804A, and APOE, may be higher for IPs characterized at the level of distributed neural systems than at the level of spatially localized brain regions. The findings indicate that imaging connectomics provides a powerful framework for understanding how genetic risk for psychiatric disease is expressed through altered structure and function of the human connectome.