IEEE Trans Biomed Eng. 2020 Sep;67(9):2572-2584. doi: 10.1109/TBME.2020.2964724. Epub 2020 Jan 7.
Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ).
We start with estimating and visualizing links within and among extracted multimodal data features using a Gaussian graphical model (GGM). We then propose a modularity-based method that can be applied to the GGM to identify links that are associated with mental illness across a multimodal data set. Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality. We use functional MRI (fMRI), diffusion MRI (dMRI), and structural MRI (sMRI) to compute the fractional amplitude of low frequency fluctuations (fALFF), fractional anisotropy (FA), and gray matter (GM) concentration maps. These three modalities are analyzed using our modularity method.
Our results show missing links that are only captured by the cross-modal information that may play an important role in disconnectivity between the components.
We identified multimodal (fALFF, FA and GM) disconnectivity in the default mode network area in patients with SZ, which would not have been detectable in a single modality.
The proposed approach provides an important new tool for capturing information that is distributed among multiple imaging modalities.
对同一现象进行多模态测量可提供互补信息并突出不同视角，尽管每种方法都有其局限性。仅关注单一模态可能会导致不正确的推断，当研究的现象是疾病时尤其如此。在本文中，我们介绍了一种利用多模态数据解决精神分裂症（SZ）中连接缺失和功能障碍假说的方法。
我们首先使用高斯图模型（GGM）来估计和可视化提取的多模态数据特征内和特征间的连接。然后，我们提出了一种基于模块性的方法，可应用于 GGM 来识别与整个多模态数据集的精神疾病相关的连接。通过模拟和真实数据，我们表明我们的方法揭示了有关疾病相关网络中断的重要信息，而仅关注单一模态则会忽略这些信息。我们使用功能磁共振成像（fMRI）、扩散磁共振成像（dMRI）和结构磁共振成像（sMRI）来计算低频波动的分数幅度（fALFF）、各向异性分数（FA）和灰质（GM）浓度图。我们使用模块化方法分析这三种模态。
我们的结果显示仅通过跨模态信息捕获到缺失的连接，这些信息可能在组件之间的连接缺失中起着重要作用。
我们在 SZ 患者的默认模式网络区域中识别出了多模态（fALFF、FA 和 GM）连接缺失，这在单一模态中是无法检测到的。
所提出的方法为捕获分布在多个成像模态中的信息提供了一种重要的新工具。
