Pascucci David, Rubega Maria, Rué-Queralt Joan, Tourbier Sebastien, Hagmann Patric, Plomp Gijs
Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Perceptual Networks Group, University of Fribourg, Fribourg, Switzerland.
Netw Neurosci. 2022 Jun 1;6(2):401-419. doi: 10.1162/netn_a_00218. eCollection 2022 Jun.
The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections. Despite this intrinsic relationship between structural connectivity (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited. Here, we propose a new adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. We show that, particularly under conditions of low signal-to-noise ratio, SC priors can help to refine estimates of directed FC, promoting sparse functional networks that combine information from structure and function. In addition, the proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new tool for multimodal imaging in the context of dynamic and directed FC analysis.
功能性脑网络的动态组成受结构连接的潜在拓扑结构限制。尽管结构连接性（SC）和功能连接性（FC）之间存在这种内在关系，但将两者结合的综合多模态方法仍然有限。在此，我们提出一种新的自适应滤波器，用于以结构连接信息为先验来估计动态和定向的FC。我们分别使用来自示踪剂研究的荟萃分析和扩散张量成像指标的SC先验，在大鼠颅顶记录和人类事件相关脑电图数据中测试了该滤波器。我们表明，特别是在低信噪比条件下，SC先验有助于细化定向FC的估计，促进结合结构和功能信息的稀疏功能网络。此外，所提出的滤波器可提供针对与SC相关的假阴性的内在保护，以及针对假阳性的稳健性，代表了动态和定向FC分析背景下多模态成像的一种有价值的新工具。
