Inria Sophia Antipolis-Meditérranée, Université Côte d'Azur, France.
École de Technologie Supérieure, Montréal, Canada.
Neuroimage. 2019 Nov 1;201:116017. doi: 10.1016/j.neuroimage.2019.116017. Epub 2019 Jul 15.
The human brain can be described as a network of specialized and spatially distributed regions. The activity of individual regions can be estimated using electroencephalography and the structure of the network can be measured using diffusion magnetic resonance imaging. However, the communication between the different cortical regions occurring through the white matter, coined information flow, cannot be observed by either modalities independently. Here, we present a new method to infer information flow in the white matter of the brain from joint diffusion MRI and EEG measurements. This is made possible by the millisecond resolution of EEG which makes the transfer of information from one region to another observable. A subject specific Bayesian network is built which captures the possible interactions between brain regions at different times. This network encodes the connections between brain regions detected using diffusion MRI tractography derived white matter bundles and their associated delays. By injecting the EEG measurements as evidence into this model, we are able to estimate the directed dynamical functional connectivity whose delays are supported by the diffusion MRI derived structural connectivity. We present our results in the form of information flow diagrams that trace transient communication between cortical regions over a functional data window. The performance of our algorithm under different noise levels is assessed using receiver operating characteristic curves on simulated data. In addition, using the well-characterized visual motor network as grounds to test our model, we present the information flow obtained during a reaching task following left or right visual stimuli. These promising results present the transfer of information from the eyes to the primary motor cortex. The information flow obtained using our technique can also be projected back to the anatomy and animated to produce videos of the information path through the white matter, opening a new window into multi-modal dynamic brain connectivity.
人脑可以被描述为一个由专门的、空间分布的区域组成的网络。可以使用脑电图来估计单个区域的活动，并且可以使用扩散磁共振成像来测量网络的结构。然而，通过白质发生的不同皮质区域之间的通信，即信息流，不能通过这两种模态独立地观察到。在这里，我们提出了一种从联合扩散 MRI 和 EEG 测量中推断大脑白质中信息流的新方法。这是通过 EEG 的毫秒级分辨率实现的，这使得从一个区域到另一个区域的信息传递变得可以观察到。构建了一个特定于主体的贝叶斯网络，该网络捕获了不同时间大脑区域之间可能的相互作用。该网络对通过扩散 MRI 束追踪检测到的大脑区域之间的连接进行编码，以及它们的相关延迟。通过将 EEG 测量作为证据注入到这个模型中，我们能够估计出其延迟由扩散 MRI 衍生的结构连接所支持的有向动态功能连接。我们以信息流图的形式呈现结果，该信息流图追踪了功能数据窗口中皮质区域之间的瞬时通信。我们使用模拟数据上的接收者操作特征曲线评估了我们的算法在不同噪声水平下的性能。此外，我们使用特征明确的视觉运动网络作为测试我们模型的基础，提出了在左或右视觉刺激后进行伸手任务时获得的信息流。这些有希望的结果展示了从眼睛到初级运动皮层的信息传递。我们的技术获得的信息流也可以被投影回解剖结构，并被动画化，以生成通过白质的信息路径的视频，为多模态动态大脑连通性开辟了一个新窗口。
