Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Hum Brain Mapp. 2017 Oct;38(10):5019-5034. doi: 10.1002/hbm.23711. Epub 2017 Jun 30.
Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc.
脑功能连接（FC）从静息态 fMRI（RS-fMRI）提取已成为诊断各种神经退行性疾病，包括阿尔茨海默病（AD）及其前驱阶段，轻度认知障碍（MCI）的一种流行方法。目前的研究主要构建脑灰质（GM）区域之间的 FC 网络基于血氧水平依赖（BOLD）信号的时间协变，它反映了同步的神经活动。然而，很少有研究探讨在白质（WM）内检测到的 FC 是否能为诊断提供有用的信息。受最近提出的功能相关张量（FCT）的启发，该张量是从 RS-fMRI 中计算出来的，用于描述 WM 中局部 FC 的结构化模式，我们在本文中提出了一种基于 GM 区域之间的 FC 和 WM 区域内的 FC 传递的信息的新型 MCI 分类方法。具体地，在 WM 中，首先基于 FCT 计算基于张量的度量（例如，各向异性分数[FA]，类似于基于扩散张量成像[DTI]计算的度量），然后沿连接每一对脑 GM 区域的每一大 WM 纤维束进行总结。这可以在与为 GM 构建的 FC 网络相似的网络结构中捕获 WM 中的功能信息，仅基于相同的 RS-fMRI 数据。此外，还进一步使用滑动窗口方法将体素-wise BOLD 信号分割成多个短重叠段。然后，分别基于 GM 和 WM 中的 BOLD 信号段，计算每对脑区之间的 FC 和 FCT。通过这种方式，我们的方法可以生成动态 FC 和动态 FCT，以更好地捕获 GM 和 WM 中的功能信息，并通过使用我们开发的特征提取、选择和集成学习算法进一步将它们整合在一起。实验结果验证了动态 FCT 可以提供 WM 中的有价值的功能信息；通过将其与 GM 中的动态 FC 相结合，即使仅使用 RS-fMRI 数据，也可以显著提高 MCI 受试者的诊断准确性。人脑映射 38:5019-5034,2017。©2017 Wiley Periodicals, Inc.
