Integrative Computational Models for Multi-Modal Analysis of Structural and Functional Neuroimaging Data

用于结构和功能神经影像数据多模态分析的综合计算模型

• 批准号: RGPIN-2014-04169
• 负责人: Garbi, Rafeef
• 金额: $ 3.72万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=690251
• 关键词: Integrative; Computational; Models; Multi; Modal

Medical imaging constitutes one of the fastest growing specialties in healthcare accounting for 35% of the total medical devices market. The global market for diagnostic imaging was estimated at over $20 billion in 2010 and is projected to exceed $26 billion by 2016 [1]. Ubiquitous access to increasingly cost effective non-invasive imaging technologies enabled unprecedented rates of patient data acquisition. With over 5 billion investigations recorded worldwide by 2010 [2], the proliferation of medical imaging continues to be fueled by increasing population age, widening range of diagnostic imaging applications, continuing technical advancements in safe imaging modalities, and increased emphasis on preventive care. For example, in Canada, over 1.7 million magnetic resonance imaging (MRI) scans were acquired in 2012 alone; double the number of such scans in 2003 [3]. **The medical imaging revolution however came at a price; a deluge of multi-dimensional high-resolution data posing serious health informatics challenges. The emerging trend is the fusion of different modalities that requires very efficient analysis of massive amounts of imaging data for diagnostic and therapeutic purposes. Fusion technologies and automated image analysis are the major drivers of the global medical image analysis software market, which is expected to grow at a rate of over 7% from 2012 to 2017 to reach $2.4 billion [1]. **With over one billion people worldwide (that is one in six humans) estimated to be affected by some brain or nervous system disorder [4], neuroimaging is one of the most important application areas. Brain diseases such as Alzheimer's disease (AD) and other dementia, mood disorders, schizophrenia, epilepsy, Parkinson's disease (PD), multiple sclerosis (MS), attention or sensory disorders, among many others, pose serious effects on human health. The prevalence of such diseases continues to increase especially with the world's ageing demographic and increased life expectancy. In fact, due to their significant disease burden, including the huge associated economic and societal costs, the World Health Organization (WHO) pinpoints neurological disorders as one of the greatest threats to public health [5]. **The inherent complexity of captured human brain anatomy and function renders neuroimaging heavily reliant on advanced computational analysis. In the 2013 report summarizing the outcomes of the NSF (National Science Foundation) Workshop on Mapping and Engineering the Brain, neuroimaging was identified as one of the grand challenges requiring new enabling technologies, better knowledge transfer, and multi- and trans-disciplinary research [6]. My proposed research is thus very timely and builds on my very strong record in this area of computational neuroimaging. My plan focuses on solving challenging computational processing and analysis problems associated with the synergistic use of multi-modal brain images. Specifically, I will develop novel paradigms for the analysis of high spatial resolution structural and functional neuroimaging data (mainly MRI) as well as combine such analyses with high temporal resolution brain signals such as Electroencephalography (EEG). My specific objectives include the development of novel integrative computational models of multi-modal neuroimaging data, use of machine learning approaches to capture and quantify the brain's complex structure and function and elucidate their interplay, integration of anatomical and physiological information within a single unified analysis framework, discovery of novel image based biomarkers of neurological disease, and creation of practical tools and software applications for specific real life neurological disease contexts such as Alzheimer's, Parkinson's and stroke.

医疗成像构成了医疗保健中增长最快的专业之一，占医疗设备总市场的35％。 2010年，全球诊断成像市场估计超过200亿美元，预计到2016年将超过260亿美元[1]。无处不在获得越来越成本有效的非侵入成像技术的访问实现了前所未有的患者数据获取率。到2010年，全球范围内记录了超过50亿个调查[2]，医学成像的扩散仍会因人口年龄的增加，诊断成像应用的扩大范围，安全成像方式的持续技术进步以及对预防性护理的重点增加。例如，在加拿大，仅在2012年就获得了超过170万种磁共振成像（MRI）扫描。在2003年，这种扫描的数量两倍[3]。 **然而，医学成像革命的价格是一定的。大量的多维高分辨率数据提出了严重的健康信息学挑战。新兴趋势是不同方式的融合，需要非常有效地分析大量成像数据，以实现诊断和治疗目的。融合技术和自动图像分析是全球医学图像分析软件市场的主要驱动力，预计从2012年到2017年将以超过7％的速度增长，达到24亿美元[1]。 **全世界有超过十亿人（六分之一的人）受到某些脑或神经系统障碍的影响[4]，神经成像是最重要的应用领域之一。脑部疾病，例如阿尔茨海默氏病（AD）和其他痴呆症，情绪障碍，精神分裂症，癫痫，帕金森氏病（PD），多发性硬化症（MS），注意力或感觉障碍，以及许多其他人，对人类健康产生严重影响。这种疾病的患病率不断增加，尤其是随着世界老化的人口统计学和预期寿命的增加。实际上，由于其巨大的疾病负担，包括巨大的相关经济和社会成本，世界卫生组织（WHO）将神经系统疾病视为对公共卫生的最大威胁之一[5]。 **被捕获的人脑解剖结构和功能的固有复杂性使神经影像大大依赖于先进的计算分析。在2013年的报告中，总结了NSF（国家科学基金会）关于映射和工程大脑的结局，神经影像学被确定为需要新的启用技术，更好的知识转移以及多和跨学科研究的巨大挑战之一[6]。因此，我提出的研究是非常及时的，并以我在计算神经影像学领域非常有记录为基础。我的计划着重于解决与多模式大脑图像的协同使用相关的具有挑战性的计算处理和分析问题。具体而言，我将开发新的范式，用于分析高空间分辨率结构和功能性神经影像学数据（主要是MRI），并将这些分析与高时间分辨率的脑信号（例如脑电图（EEG））相结合。我的具体目标包括开发多模式神经影像数据的新型整合计算模型，使用机器学习方法来捕获和量化大脑的复杂结构和功能，并阐明其相互作用，并在单个统一的分析框架中对解剖学和生理信息的整合，在单个统一分析框架中的整合以及针对新型的基于图像的神经性疾病和创建工具的生物学疾病的生物标志物，并实现了实践疾病的生物疾病，并实现了实际的疾病，并创建了实践疾病的生物学工具，并实现了实践疾病的生物学疾病，并且阿尔茨海默氏症，帕金森氏症和中风。