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

精神分裂症（SZ）和双相情感障碍（BP）是人类痛苦和社会支出方面最具挑战性和最昂贵的两种精神疾病。在临床上，SZ和BP在急性精神病期间可以表现出相似的病理学，这引起了鉴别诊断、一线药物治疗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标志物，其诊断依赖于纵向症状评估。已经发表了几项研究，这些研究在单一模式（如fMRI，sMRI，EEG和DTI）中比较了SZ和BP，并确定了区分这两种情况的大脑改变。然而，这项工作受到了样本量小，有限的重新测试的可靠性和一般的可复制性。每种脑成像技术都提供了不同的脑功能或结构视图，而多模式融合则利用了每种技术的优势，并可能揭示出可以统一不同发现的隐藏因素。在这里，我们试图复制和扩展对生物标志物的搜索，以通过使用N向多模式融合来可靠地区分SZ和BP，例如，fMRI、DTI和sMRI数据，这有望提高超越任何单一模态的组区分能力。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，使人们能够自由地联合收割机多种模式，准确地探索联合信息，并检查大脑模式和临床措施之间的关系聪明地，如症状评分等，这项建议的另一个目的是研究的性状与状态的影响SZ和BP，使用纵向数据和3向fMRI-DTI-sMRI融合。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。这个时间段是临床医生区分SZ和BP最困难的时候。这样一个有价值的数据集沿着与使用一个先进的联合分析模型，将使我们能够调查多个组区分因素和性状，可能作为潜在的生物标志物的SZ或BP。此外，模态（及其组合）将根据其区分群体的能力进行排名，从而产生模态选择偏好。我们将进一步评估多模态数据中是否存在提供与临床诊断相容的证据的自然聚类，并尝试基于所选择的组区分特征和新颖的分类算法在个体精神病患者的水平上对患者进行分类。我们相信从3种模式中检索到的组区分信息将提高分类的灵敏度和特异性，并通过融合来自其他网站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.