Data-driven approaches to identify biomarkers from multimodal imaging big data

从多模态成像大数据中识别生物标志物的数据驱动方法

• 批准号: 10473657
• 负责人: Jing Sui
• 金额: $ 38.59万
• 依托单位: GEORGIA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-20 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10473657
• 关键词: Address; Affect; Age; Algorithms; Anterior; Antidepressive Agents; Area; Attention; Back; Behavioral; Benchmarking; Big Data; Biological; Biological Markers; Bipolar Disorder; Brain; Brain Diseases; Brain imaging; Cell Nucleus; Classification; Clinical; Clinical Data; Cognitive; Cognitive deficits; Collaborations; Communities; Complement; Complex; Corpus Callosum; DSM-IV; Data; Data Pooling; Data Set; Diagnosis; Diagnostic; Disease; Disease remission; Electroconvulsive Therapy; Engineering; Functional disorder; Gender; Goals; ICD-9; Image; Impaired cognition; Impairment; Individual; International; Intervention; Joints; Judgment; Knowledge; Magnetic Resonance Imaging; Major Depressive Disorder; Manic; Mathematics; Measures; Medical Care Costs; Mental disorders; Methods; Mining; Modality; Modeling; Moods; Multimodal Imaging; Outcome; Patients; Pharmaceutical Preparations; Pharmacology; Play; Precision Medicine Initiative; Probability; Prognosis; Psychiatric Diagnosis; Psychiatry; Psychoses; Psychotic Disorders; Recording of previous events; Records; Recurrence; Relapse; Reporting; Research Personnel; Role; Schizophrenia; Severities; Site; Structure; Supervision; Symptoms; Syndrome; System; Techniques; Testing; Therapeutic; Time; Translations; Treatment Efficacy; Treatment outcome; Validation; Work; base; biomarker identification; cingulate cortex; clinical application; clinical care; clinical practice; clinical predictors; cognitive ability; cohort; common symptom; convolutional neural network; data mining; data sharing; deep field survey; deep learning; demographics; depressed patient; diagnostic tool; diagnostic value; disease classification; effective intervention; feature selection; flexibility; gray matter; high dimensionality; improved; innovation; insight; learning strategy; multimodal data; multimodal neuroimaging; multimodality; neuroimaging; neuroimaging marker; novel; open source; optimal treatments; outcome prediction; patient subsets; personalized care; personalized predictions; predicting response; supervised learning; tool; translational impact; translational medicine; treatment response; white matter

1. PROJECT SUMMARY/ABSTRACT The study of translational biomarkers in brain disorders is a very challenging and fruitful approach, which will empower a better understanding of healthy and diseased brains. This project will promote the translation of advanced engineering solutions and mathematic tools to novel neuroimaging applications in psychiatric disorders including major depression disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ), allowing sophisticated and powerful analyses on highly complex datasets. To date, the unifying syndrome classification (ICD-9/10;DSM-IV/5) for these mental disorders obscures our knowledge of underlying pathophysiology and cannot guide optimal treatments. For example, there is no biomarker that is able to precisely predict response of MDD to some treatments. One reason for this is that most neuroimaging prediction studies to date have used a single imaging measure or reported simple correlation relationships, without considering multimodal cross- information, nonlinear relationships, or multi-site cross-validation. Hence, developing novel data mining techniques such as deep learning, fusion with reference, and sparse regression can complement and exploit the richness of neuroimaging data, providing promising avenues to identify objective biomarkers and going beyond a descriptive use of brain imaging as traditionally used in studies of brain disease to individualized prediction. We will facilitate the translational biomarker identification by developing 3 novel data-driven methods: 1) A supervised fusion model that can provide insight on how cognitive impairment may affect covarying brain function and structure in mental disorder, by using different clinical measures as a reference to guide multimodal MRI fusion; 2) A cutting-edge prediction framework with aggregated feature selection techniques that is able to estimate clinical outcome more precisely, e.g., remission/relapse status of individual MDD patient after electroconvulsive treatment(ECT) using baseline brain imaging and demographic measures of 3) We will draw on advances and ideas from deep learning combined with layer-wise relevance propagation (LRP) or attention modules, to classify multiple groups of psychiatric disorders by incorporating dynamic functional measures. The proposed (Deep/Recurrent/Convolutional Neural Network, DNN/RNN/CNN) models will have enhanced interpretability that is able to trace back and discover the most predictive functional networks from input. All above proposed methods will be applied to big data containing both multimodal imaging and behavioral information (n~5000) pooled from existing studies, and our developed open-source toolboxes will be shared publicly. This pioneering study may provide an urgently-needed paradigm shift in the treatment and diagnosis of psychiatric disorders, thereby guiding personalized clinical care. Accomplishment of this project has great potential to discover neuroimaging biomarkers that have been missed by existing approaches, leading to earlier and more effective interventions, and laying the groundwork for a significant translational impact.

1.项目总结/摘要 翻译生物标记物在脑疾病中的研究是一种非常具有挑战性和卓有成效的方法， 将使人们更好地了解健康和患病的大脑。该项目将促进翻译 先进的工程解决方案和数学工具在精神病学中的新的神经成像应用 包括重度抑郁症(MDD)、双相情感障碍(BD)和精神分裂症(SZ)在内的疾病，允许 对高度复杂的数据集进行复杂而强大的分析。到目前为止，统一的证候分类 (ICD-9/10；DSM-IV/5)这些精神障碍使我们对潜在的病理生理学和 不能指导最佳治疗。例如，没有生物标志物能够准确地预测反应 对MDD的某些治疗。其中一个原因是，迄今为止大多数神经成像预测研究都使用了 单一成像测量或报告的简单相关关系，而不考虑多模式交叉 信息、非线性关系或多站点交叉验证。因此，开发新的数据挖掘 深度学习、参考融合和稀疏回归等技术可以补充和利用 丰富的神经影像数据，为识别客观生物标志物和超越 对脑部疾病研究中传统使用的脑成像的描述性使用，以进行个体化预测。 我们将通过开发3种新的数据驱动方法来促进翻译生物标志物的识别：1)A 有监督的融合模型，可以洞察认知障碍如何影响共同变化的大脑功能 以不同的临床措施为参照指导多模式磁共振成像 融合；2)具有聚合特征选择技术的尖端预测框架，能够 更准确地评估临床结果，例如，治疗后个别MDD患者的缓解/复发状态 使用基线脑成像和人口统计学测量的电惊厥治疗(ECT)3)我们将抽出 深度学习与层级关联传播(LRP)或注意相结合的研究进展和思路 模块，通过结合动态功能测量对多组精神障碍进行分类。这个 提出的(深度/递归/卷积神经网络，DNN/RNN/CNN)模型将得到增强 可解释性，能够从输入中追溯并发现最具预测性的功能网络。全 以上提出的方法将应用于包含多模式成像和行为的大数据 来自现有研究的信息(N~5000)和我们开发的开源工具箱将被共享 公开的。这项开创性的研究可能提供治疗和诊断方面亟需的范式转变。 精神障碍，从而指导个性化的临床护理。这项工程的完成有很大的意义 发现现有方法遗漏的神经成像生物标记物的可能性，导致更早 和更有效的干预措施，并为产生重大的翻译影响奠定基础。