Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA.
Department of Physics and Astronomy, University of Rochester, Rochester, New York, USA.
Hum Brain Mapp. 2021 Aug 1;42(11):3481-3499. doi: 10.1002/hbm.25447. Epub 2021 May 6.
There has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. Previous connectome integration studies almost exclusively required predefined atlases. However, there are many potential atlases to choose from and this choice heavily affects all subsequent analyses. To avoid such an arbitrary choice, we propose a novel atlas-free approach, named Surface-Based Connectivity Integration (SBCI), to more accurately study the relationships between SC and FC throughout the intra-cortical gray matter. SBCI represents both SC and FC in a continuous manner on the white surface, avoiding the need for prespecified atlases. The continuous SC is represented as a probability density function and is smoothed for better facilitation of its integration with FC. To infer the relationship between SC and FC, three novel sets of SC-FC coupling (SFC) measures are derived. Using data from the Human Connectome Project, we introduce the high-quality SFC measures produced by SBCI and demonstrate the use of these measures to study sex differences in a cohort of young adults. Compared with atlas-based methods, this atlas-free framework produces more reproducible SFC features and shows greater predictive power in distinguishing biological sex. This opens promising new directions for all connectomics studies.
人们对联合研究来自弥散张量成像和功能磁共振成像的结构连接（SC）和功能连接（FC）越来越感兴趣。以前的连接组学整合研究几乎完全需要预定义的图谱。然而，有许多潜在的图谱可供选择，而这种选择会严重影响所有后续的分析。为了避免这种任意的选择，我们提出了一种新的无图谱方法，名为基于表面的连接整合（SBCI），以更准确地研究皮质内灰质中 SC 和 FC 之间的关系。SBCI 以连续的方式在白质表面上表示 SC 和 FC，避免了对预定义图谱的需求。连续的 SC 表示为概率密度函数，并进行平滑处理，以更好地促进其与 FC 的整合。为了推断 SC 和 FC 之间的关系，我们推导出了三组新的 SC-FC 耦合（SFC）度量。使用来自人类连接组计划的数据，我们引入了 SBCI 产生的高质量 SFC 度量，并展示了这些度量在研究年轻成年人队列中的性别差异方面的应用。与基于图谱的方法相比，这种无图谱框架产生了更具可重复性的 SFC 特征，并在区分生物性别方面显示出更大的预测能力。这为所有连接组学研究开辟了有希望的新方向。
