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脑疾病的结构连接组学。

Structural connectomics in brain diseases.

机构信息

Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

出版信息

Neuroimage. 2013 Oct 15;80:515-26. doi: 10.1016/j.neuroimage.2013.04.056. Epub 2013 Apr 25.

Abstract

Imaging the connectome in vivo has become feasible through the integration of several rapidly developing fields of science and engineering, namely magnetic resonance imaging and in particular diffusion MRI on one side, image processing and network theory on the other side. This framework brings in vivo brain imaging closer to the real topology of the brain, contributing to narrow the existing gap between our understanding of brain structural organization on one side and of human behavior and cognition on the other side. Given the seminal technical progresses achieved in the last few years, it may be ready to tackle even greater challenges, namely exploring disease mechanisms. In this review we analyze the current situation from the technical and biological perspectives. First, we critically review the technical solutions proposed in the literature to perform clinical studies. We analyze for each step (i.e. MRI acquisition, network building and network statistical analysis) the advantages and potential limitations. In the second part we review the current literature available on a selected subset of diseases, namely, dementia, schizophrenia, multiple sclerosis and others, and try to extract for each disease the common findings and main differences between reports.

摘要

通过整合几个快速发展的科学和工程领域,即磁共振成像,特别是扩散磁共振成像,以及图像处理和网络理论,在体连接组成像已经成为可能。这一框架使脑成像更接近大脑的真实拓扑结构,有助于缩小我们对大脑结构组织的理解与人类行为和认知之间的现有差距。鉴于过去几年取得的开创性技术进展,它可能已经准备好应对更大的挑战,即探索疾病机制。在这篇综述中,我们从技术和生物学的角度来分析当前的情况。首先,我们从技术角度批判性地回顾了文献中提出的用于进行临床研究的解决方案。我们分析了每一步(即 MRI 采集、网络构建和网络统计分析)的优点和潜在局限性。在第二部分,我们回顾了选定疾病子集(即痴呆、精神分裂症、多发性硬化症等)的现有文献,并试图为每种疾病提取共同发现和报告之间的主要差异。

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