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2013, NeuroImage. DOI: 10.1016/j.neuroimage.2012.12.054
Track-weighted functional connectivity (TW-FC): A tool for characterizing the structural–functional connections in the brain
Fernando Calamante , Richard A.J. Masterton , Jacques-Donald Tournier , Robert E. Smith , Lisa Willats , David Raffelt , Alan Connelly
Abstract:
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.
2022-09-30 14:31:00
#paper doi.org/10.1016/j.neuroimage.2012.12.054 Track-weighted functional connectivity (TW-FC): A tool for characterizing the structural–functional connections in the brain. NeuroImage. 2013. MRI 为无创研究大脑中的功能和结构连接提供了强大的工具。功能连接 (FC) 技术利用缓慢自发信号波动的内在时间相关性来表征大脑功能网络。此外,弥散 MRI 纤维追踪可用于研究白质结构连接。近年来,人们对结合这两种技术以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 数据相结合以生成轨迹加权 (TW) FC 图FC 网络。该方法应用于来自 8 名健康志愿者的数据,并用 ( i ) 使用基于种子连接的分析获得的 FC 网络(在楔前叶/后扣带回皮层,PCC 中播种,已知是默认模式网络的一部分)进行说明,和(二) 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)。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,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层和楔前叶/后扣带皮层之间的连接中起主要作用;类似地,上纵束在注意力网络、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中都有很好的表现。TW-FC 地图突出显示与给定 FC 网络相关的白质连接,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连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(和新颖的)图像对比度。本研究中显示的结果说明了 TW-FC 方法在将结构和功能数据融合为单一的定量图像。因此,这种技术可以在神经科学和神经学中具有重要的应用,例如基于体素的比较研究。
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