Data_Sheet_1_Unraveling Diagnostic Biomarkers of Schizophrenia Through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data.PDF

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.

融合不同模态的互补信息，有助于发现精神疾病领域更精准的诊断生物标志物（diagnostic biomarkers）。然而，通过数据融合开展生物标志物发现工作颇具挑战，因为需要从兼具共享/非共享模式且阶数各异的数据集中提取可解释且可复现的模式。例如，多受试者的多通道脑电图（electroencephalography, EEG）信号可表示为以受试者、时间、通道为模态的三阶张量，而功能磁共振成像（functional magnetic resonance imaging, fMRI）数据则可表示为受试者-体素矩阵。传统数据融合方法会将脑电图这类高阶张量重排为矩阵，以采用基于矩阵分解的方法。与之相对，基于耦合矩阵与张量分解（coupled matrix and tensor factorizations, CMTF）的融合方法能够利用高阶张量的潜在多模态结构。研究表明，CMTF方法能够更精准地捕捉潜在模式，且无需对潜在神经模式（即生物标志物）施加严苛约束。本文中，我们将一组包含精神分裂症患者与健康对照者的受试者在听觉Oddball任务（auditory oddball task, AOD）中采集的脑电图、功能磁共振成像及结构磁共振成像（structural MRI, sMRI）数据，按照沿受试者模态耦合的矩阵与高阶张量形式进行组织，并采用聚焦于在共享/非共享模式下精准识别潜在模式的结构揭示型CMTF方法[亦称高级CMTF（advanced CMTF, ACMTF）]开展联合分析。我们证实，相较于仅分析脑电图张量，采用ACMTF对脑电图张量与功能磁共振成像矩阵开展联合分析，能够在区分精神分裂症患者与健康对照者的层面揭示出具有显著生物学意义的组分，同时还能提供高分辨率的空间模式，并提升聚类性能。此外，我们证明了这些模式具备可复现性，并针对不同模型参数开展了可复现性研究。与联合独立成分分析（joint independent component analysis, jICA）数据融合方法相比，ACMTF能够通过为每个组分生成单一的脑电地形汇总图，实现更直观的脑电图数据解释。进一步而言，通过ACMTF模型将结构磁共振成像数据与脑电图、功能磁共振成像数据进行融合，可得到结构性模式；但我们同时也发现，在融合来自不同模态、本质差异极大的数据集时，预处理步骤发挥着至关重要的作用。