Unraveling Diagnostic Biomarkers of Schizophrenia Through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data
Unraveling Diagnostic Biomarkers of Schizophrenia Through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data
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DOI：
10.3389/fnins.2019.00416
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发表时间：
2019-05-03
影响因子：
4.3
通讯作者：
Adali, Tulay
中科院分区：
文献类型：
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作者：
Acar, Evrim;Schenker, Carla;Adali, Tulay
融合来自不同模式的互补信息可以发现更准确的精神疾病诊断生物标志物。然而，通过数据融合发现生物标志物是具有挑战性的，因为它需要从数据集中提取可解释和可再现的模式，包括共享/非共享模式和不同的顺序。例如，来自多个受试者的多通道脑电图（EEG）信号可以表示为具有模式的三阶张量：受试者、时间和通道，而功能性磁共振成像（fMRI）数据可以是受试者体素矩阵的形式。传统的数据融合方法将高阶张量（如EEG）重新排列为矩阵，以使用基于矩阵分解的方法。相比之下，基于耦合矩阵和张量分解（CMTF）的融合方法利用了高阶张量的潜在多路结构。CMTF方法已被证明可以更准确地捕获潜在模式，而不会对潜在神经模式施加强约束，即，生物标志物。在本文中，EEG，fMRI和结构MRI（sMRI）的数据收集过程中的听觉oddball任务（AOD）从一组受试者组成的精神分裂症患者和健康对照，安排为矩阵和高阶张量耦合沿着主题模式，并使用结构揭示CMTF方法（也称为高级CMTF（ACMTF））进行联合分析集中于在存在共享/非共享模式的情况下对基础模式的唯一标识。我们表明，联合分析的EEG张量和功能磁共振成像矩阵使用ACMTF揭示了显着的和生物学上有意义的成分，在区分精神分裂症患者和健康对照，同时还提供了高分辨率的空间模式，提高聚类性能相比，只有EEG张量的分析。我们还表明，这些模式是可重复的，并研究不同的模型参数的再现性。在联合独立成分分析（JICA）数据融合方法相比，ACMTF提供了更容易的解释EEG数据，揭示了一个单一的总结地图的地形为每个组件。此外，融合sMRI数据与EEG和fMRI通过ACMTF模型提供了结构模式，但是，我们也表明，当融合数据集从多种形式，因此性质非常不同，预处理起着至关重要的作用。
Fusing complementary information from different modalities can lead to the discovery of more accurate diagnostic biomarkers for psychiatric disorders. However, biomarker discovery through data fusion is challenging since it requires extracting interpretable and reproducible patterns from data sets, consisting of shared/unshared patterns and of different orders. For example, multi-channel electroencephalography (EEG) signals from multiple subjects can be represented as a third-order tensor with modes: subject, time, and channel, while functional magnetic resonance imaging (fMRI) data may be in the form of subject by voxel matrices. Traditional data fusion methods rearrange higher-order tensors, such as EEG, as matrices to use matrix factorization-based approaches. In contrast, fusion methods based on coupled matrix and tensor factorizations (CMTF) exploit the potential multi-way structure of higher-order tensors. The CMTF approach has been shown to capture underlying patterns more accurately without imposing strong constraints on the latent neural patterns, i.e., biomarkers. In this paper, EEG, fMRI, and structural MRI (sMRI) data collected during an auditory oddball task (AOD) from a group of subjects consisting of patients with schizophrenia and healthy controls, are arranged as matrices and higher-order tensors coupled along the subject mode, and jointly analyzed using structure-revealing CMTF methods [also known as advanced CMTF (ACMTF)] focusing on unique identification of underlying patterns in the presence of shared/unshared patterns. We demonstrate that joint analysis of the EEG tensor and fMRI matrix using ACMTF reveals significant and biologically meaningful components in terms of differentiating between patients with schizophrenia and healthy controls while also providing spatial patterns with high resolution and improving the clustering performance compared to the analysis of only the EEG tensor. We also show that these patterns are reproducible, and study reproducibility for different model parameters. In comparison to the joint independent component analysis (jICA) data fusion approach, ACMTF provides easier interpretation of EEG data by revealing a single summary map of the topography for each component. Furthermore, fusion of sMRI data with EEG and fMRI through an ACMTF model provides structural patterns; however, we also show that when fusing data sets from multiple modalities, hence of very different nature, preprocessing plays a crucial role.