Discriminating schizophrenia from bipolar disorder by N-way multimodal fusion of

就人类痛苦和社会支出而言，精神分裂症（SZ）和双相情感障碍（BP）是两种最具挑战性和最昂贵的精神障碍。临床上，SZ和BP在急性精神病期可以表现出相似的症状，这就提出了鉴别诊断、一线用药方案和治疗计划的问题。目前，这两种疾病都没有明确的生物学标志物，它们的诊断依赖于纵向症状评估。已经发表的一些研究将SZ和BP在fMRI、sMRI、EEG和DTI等单一模式下进行了比较，并确定了区分这两种情况的大脑改变。然而，这项工作受到小样本量、有限的重新测试可靠性和一般可重复性的阻碍。每种脑成像技术都提供了大脑功能或结构的不同视图，而多模态融合利用了每种技术的优势，并可能发现可以统一不同发现的隐藏因素。在这里，我们试图复制并扩展对生物标志物的搜索，通过使用n向多模态融合（例如，fMRI， DTI和sMRI数据）来可靠地区分SZ和BP，这有望提高任何单一模态之外的群体区分能力。我们将开发一种新的多变量模型，并发布一个用户友好的工具箱，使人们能够自由地组合多种模式，准确地探索联合信息，并巧妙地检查大脑模式与临床指标之间的关系，如症状评分等。本研究的另一个目的是利用纵向数据和fMRI-DTI-sMRI 3-way融合来研究SZ和BP的性状-状态效应。我们将访问出院后立即扫描和5-7周后再次扫描的患者的数据。这段时间是临床医生最难区分SZ和BP的时候。这样一个有价值的数据集以及尖端的联合分析模型的使用，将使我们能够研究多种群体区分因素和可能作为SZ或BP潜在生物标志物的特征。此外，模态（及其组合）将根据它们区分组的能力进行排序，从而产生模态选择偏好。我们将进一步评估在多模态数据中是否存在能够提供与临床诊断相一致的证据的自然聚类，并尝试基于所选择的群体判别特征和新的分类算法在个体精神患者水平上对患者进行分类。我们相信，从3种模式中检索到的群体区分信息将提高分类的敏感性和特异性，并允许通过融合其他站点的类似数据类型来识别更可靠和有效的生物标志物。该项目的成功完成将为n向多模态数据融合提供一个强大的工具，有助于表征SZ和BP的特征，这些特征可能作为潜在的生物标志物，并加快其在急性环境中的鉴别诊断，从而为两名患者提供更合适的治疗和改善的结果。

Schizophrenia (SZ) and bipolar disorder (BP) are two of the most challenging and costliest mental disorders in terms of human suffering and societal expenditure. Clinically, SZ and BP can present with similar symptomology during acute psychotic periods, raising issues of differential diagnosis, frontline medication regime, and treatment planning. Currently there are no definitive biological markers for either diseases, and their diagnosis relies upon longitudinal symptom assessment. Several studies have been published which compared SZ and BP within a single modality such as fMRI, sMRI, EEG, and DTI, and have identified brain alterations that discriminate the two conditions. However, this work has been hampered by small sample sizes, limited re-test reliability and general replicability. Each brain imaging technique provides a different view of brain function or structure, while multimodal fusion capitalizes on the strength of each and may uncover the hidden factors that can unify disparate findings. Here we seek to replicate and extend the search for biomarkers to reliably differentiate SZ from BP by using N-way multimodal fusion, e.g., fMRI, DTI, and sMRI data, which is expected to improve the group-differentiating ability beyond any single modality. We will develop a novel multivariate model and release a user-friendly toolbox, which enables people to combine multiple modalities freely, explore the joint information accurately and examine the relationship between brain patterns and clinical measures smartly, such as symptom scores etc. Another aim of this proposal is to study the trait versus state effect of SZ and BP, using longitudinal data and in a 3-way fMRI-DTI-sMRI fusion. We will access data from patients who were scanned immediately after discharge and again 5-7 weeks later. This time period is when clinicians have the most difficulties in distinguishing SZ from BP. Such a valuable dataset along with the use of a cutting-edge joint analysis model, will enable us to investigate multiple group-discriminating factors and the traits which may serve as potential biomarkers of SZ or BP. In addition, the modalities (and their combinations) will be ranked according to their ability to distinguish groups, resulting in a modal selection preference. We will further evaluate whether there are natural clusters in multimodal data that provide evidence compatible the clinical diagnoses and attempt to classify patients at the level of individual psychiatric patients based on the selected group-discriminative features and novel classification algorithms. We believe the group-differentiating information retrieved from 3 modalities will enhance the sensitivity and specificity of the classification and permit more reliable and valid biomarkers to be identified by fusing similar data types from other sites. The successful completion of this project will provide a powerful tool for N-way multimodal data fusion, help characterize the traits of SZ and BP which may serve as potential biomarkers and expedite their differential diagnosis in acute settings, leading to more appropriate treatment and improved outcomes for both patients.