Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.
Neuroimage. 2013 Apr 15;70:199-210. doi: 10.1016/j.neuroimage.2012.12.054. Epub 2013 Jan 5.
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 FC 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.
MRI 提供了一种强大的工具，可用于无创地研究大脑的功能和结构连接。功能连接（FC）技术利用缓慢自发信号波动的固有时间相关性来描述大脑功能网络。此外，扩散 MRI 纤维追踪可用于研究白质结构连接。近年来，人们对将这两种技术结合起来以提供大脑整体结构-功能描述产生了浓厚的兴趣。在这项工作中，我们应用了最近提出的超高分辨率轨迹加权成像（TWI）方法，展示了如何将全脑纤维追踪数据与 FC 数据相结合，生成 FC 网络的轨迹加权（TW）FC 图。该方法应用于 8 名健康志愿者的数据，并通过（i）基于种子的连通性分析（在已知为默认模式网络一部分的后扣带回/顶下小叶（PCC）中进行种子）获得的 FC 网络，以及（ii）使用独立成分分析（特别是默认模式、注意力、视觉和感觉运动网络）生成的 FC 网络进行了说明。TW-FC 图在连接 FC 网络节点的白质结构中显示出高强度。例如，在基于种子的 PCC 分析中，扣带束显示出最强的 TW-FC 值，这是由于它们在额内侧皮质和后扣带回/顶下小叶之间的连接中的主要作用；同样，在注意力网络中很好地表示了上纵束，在视觉网络中表示了视辐射，在感觉运动网络中表示了皮质脊髓束和胼胝体。TW-FC 图突出了与特定 FC 网络相关的白质连接，并且给定体素中的强度反映了穿过该体素的结构连接所连接的网络节点部分的功能连接。因此，它们包含与生成它们的图像不同（且新颖）的图像对比度。本研究中显示的结果说明了 TW-FC 方法将结构和功能数据融合到单个定量图像中的潜力。因此，该技术在神经科学和神经病学中可能具有重要的应用，例如用于基于体素的比较研究。
