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

医学成像是医疗保健领域增长最快的专业之一，占整个医疗设备市场的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。无所不在的成本效益越来越高的非侵入性成像技术使患者数据采集率达到前所未有的水平。到2010年，世界范围内记录的调查超过50亿次，由于人口老龄化、诊断成像应用范围的扩大、安全成像模式的持续技术进步以及对预防保健的日益重视，医学成像的扩散继续受到推动。例如，在加拿大，仅2012年就获得了170多万份磁共振成像（MRI）扫描；这类扫描的数量是2003年的两倍。**然而，医学成像革命是有代价的；大量的多维高分辨率数据构成了严重的卫生信息学挑战。新兴的趋势是不同模式的融合，这需要非常有效地分析大量的成像数据，用于诊断和治疗目的。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，该市场将以超过7%的速度增长，达到24亿美元。**据估计，全球有超过10亿人（即六分之一的人）受到某种大脑或神经系统疾病的影响，神经成像是最重要的应用领域之一。脑部疾病，如阿尔茨海默病（AD）和其他痴呆症、情绪障碍、精神分裂症、癫痫、帕金森病（PD）、多发性硬化症（MS）、注意力或感觉障碍等，在许多其他疾病中，对人类健康造成严重影响。这类疾病的发病率继续增加，特别是随着世界人口老龄化和预期寿命延长。事实上，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（世卫组织）将神经系统疾病确定为对公共卫生的最大威胁之一。**捕获的人类大脑解剖和功能的固有复杂性使得神经成像严重依赖于先进的计算分析。在2013年的报告中总结了NSF（美国国家科学基金会）大脑测绘和工程研讨会的成果，神经成像被确定为需要新的支持技术，更好的知识转移以及多学科和跨学科研究的重大挑战之一。因此，我提出的研究非常及时，并建立在我在计算神经成像领域的强大记录之上。我的计划侧重于解决与多模态脑图像协同使用相关的具有挑战性的计算处理和分析问题。具体来说，我将开发分析高空间分辨率结构和功能神经成像数据（主要是MRI）的新范式，并将这些分析与高时间分辨率脑信号（如脑电图（EEG））相结合。我的具体目标包括开发多模态神经成像数据的新型综合计算模型，使用机器学习方法捕获和量化大脑的复杂结构和功能并阐明它们的相互作用，在单一统一的分析框架内整合解剖和生理信息，发现基于图像的神经系统疾病生物标志物，以及针对阿尔茨海默氏症、帕金森氏症和中风等现实生活中特定的神经系统疾病的实用工具和软件应用的创建。

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.