Track-weighted functional connectivity (TW-FC): A tool for characterizing the structural-functional connections in the brain
Track-weighted functional connectivity (TW-FC): A tool for characterizing the structural-functional connections in the brain
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DOI：
10.1016/j.neuroimage.2012.12.054
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发表时间：
2013-04-15
期刊：
影响因子：
5.7
通讯作者：
Connelly, Alan
中科院分区：
文献类型：
--
作者：
Calamante, Fernando;Masterton, Richard A. J.;Connelly, Alan
MRI为非侵入性地研究大脑中的功能和结构连接提供了强有力的工具。功能连接（FC）技术利用缓慢的自发信号波动与人脑功能网络的内在时间相关性。此外，弥散磁共振纤维追踪可用于研究白色物质的结构连接。近年来，人们对结合这两种技术来提供大脑的整体结构-功能描述产生了相当大的兴趣。在这项工作中，我们应用了最近提出的超分辨率轨迹加权成像（TWI）方法来演示如何将全脑纤维跟踪数据与FC数据相结合，以生成FC网络的轨迹加权（TW）FC地图。该方法应用于来自8名健康志愿者的数据，并用（i）使用基于种子连接性的分析（在楔前叶/后扣带皮层（PCC）中播种，已知是默认模式网络的一部分）获得的FC网络和（ii）使用独立成分分析生成的FC网络（特别是默认模式，注意力，视觉和感觉运动网络）进行说明。TW-FC图显示连接FC网络节点的白色物质结构具有高强度。例如，扣带束在基于PCC种子的分析中显示出最强的TW-FC值，因为它们在内侧额叶皮质和楔前叶/后扣带皮质之间的连接中起主要作用;同样，上级纵束在注意力网络中得到很好的代表，视觉网络中的视辐射，以及感觉运动网络中的皮质脊髓束和胼胝体。TW-FC图突出显示了与给定PC网络相关的白色物质连接，并且它们在给定体素中的强度反映了由穿过该体素的结构连接所链接的网络节点部分的功能连接性。因此，它们包含与用于生成它们的图像不同的（并且新颖的）图像对比度。在这项研究中所示的结果说明了潜在的TW-FC方法的结构和功能数据融合成一个单一的定量图像。因此，这种技术可能在神经科学和神经病学中有重要的应用，例如基于体素的比较研究。(C)2013 Elsevier Inc. All rights reserved.
MRI provides a powerful tool for studying the functional and structural connections in the brain non-invasively. The technique of functional connectivity (FC) exploits the intrinsic temporal correlations of slow spontaneous signal fluctuations to characterise brain functional networks. In addition, diffusion MRI fibre-tracking can be used to study the white matter structural connections. In recent years, there has been considerable interest in combining these two techniques to provide an overall structural-functional description of the brain. In this work we applied the recently proposed super-resolution track-weighted imaging (TWI) methodology to demonstrate how whole-brain fibre-tracking data can be combined with FC data to generate a track-weighted (TW) FC map of FC networks. The method was applied to data from 8 healthy volunteers, and illustrated with (i) FC networks obtained using a seeded connectivity-based analysis (seeding in the precuneus/posterior cingulate cortex, PCC, known to be part of the default mode network), and (ii) with FC networks generated using independent component analysis (in particular, the default mode, attention, visual, and sensory-motor networks). TW-FC maps showed high intensity in white matter structures connecting the nodes of the FC networks. For example, the cingulum bundles show the strongest TW-FC values in the PCC seeded-based analysis, due to their major role in the connection between medial frontal cortex and precuneus/posterior cingulate cortex; similarly the superior longitudinal fasciculus was well represented in the attention network, the optic radiations in the visual network, and the corticospinal tract and corpus callosum in the sensory-motor network. The TW-FC maps highlight the white matter connections associated with a given PC network, and their intensity in a given voxel reflects the functional connectivity of the part of the nodes of the network linked by the structural connections traversing that voxel. They therefore contain a different (and novel) image contrast from that of the images used to generate them. The results shown in this study illustrate the potential of the TW-FC approach for the fusion of structural and functional data into a single quantitative image. This technique could therefore have important applications in neuroscience and neurology, such as for voxel-based comparison studies. (C) 2013 Elsevier Inc. All rights reserved.