Structural connectomics in brain diseases
Structural connectomics in brain diseases
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DOI：
10.1016/j.neuroimage.2013.04.056
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发表时间：
2013-10-15
期刊：
影响因子：
5.7
通讯作者：
Hagmann, Patric
中科院分区：
文献类型：
--
作者：
Griffa, Alessandra;Baumann, Philipp S.;Hagmann, Patric
通过整合几个快速发展的科学和工程领域，即一方面是磁共振成像，特别是扩散MRI，另一方面是图像处理和网络理论，体内连接体成像已经变得可行。该框架使体内大脑成像更接近大脑的真实的拓扑结构，有助于缩小我们对大脑结构组织的理解与对人类行为和认知的理解之间存在的差距。鉴于过去几年取得的开创性技术进步，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分析了目前的情况。首先，我们严格审查文献中提出的技术解决方案，以进行临床研究。我们分析了每个步骤（即MRI采集、网络构建和网络统计分析）的优势和潜在局限性。在第二部分中，我们回顾了目前的文献中所选择的一个子集的疾病，即痴呆症，精神分裂症，多发性硬化症和其他，并试图提取每种疾病的共同发现和报告之间的主要差异。(C)2013 Elsevier Inc. All rights reserved.
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. (C) 2013 Elsevier Inc. All rights reserved.