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，占整个医疗器械市场的35%。诊断成像的全球市场在2010年估计超过200亿美元，预计到2016年将超过260亿美元[1]。无处不在的对成本效益越来越高的非侵入性成像技术的访问使患者数据采集的速度达到了前所未有的水平。到2010年，全球共记录了超过50亿例研究[2]，随着人口年龄的增加、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，医学成像的激增继续得到推动。例如，在加拿大，仅2012年就获得了170多万次磁共振成像(MRI)扫描；是2003年此类扫描数量的两倍[3]。**医学成像革命是有代价的；多维高分辨率数据的泛滥给健康信息学带来了严重的挑战。新出现的趋势是不同模式的融合，这需要非常有效地分析海量成像数据以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，全球医学图像分析软件市场将以超过7%的速度增长，达到24亿美元[1]。**全球估计有超过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[4]，神经成像是最重要的应用领域之一。阿尔茨海默病(AD)和其他痴呆症、情绪障碍、精神分裂症、癫痫、帕金森氏病(PD)、多发性硬化症(MS)、注意力或感觉障碍等脑部疾病对人类健康构成严重影响。这类疾病的流行率继续增加，特别是随着世界人口老龄化和预期寿命的增加。事实上，由于其巨大的疾病负担，包括相关的巨大经济和社会成本，世界卫生组织(WHO)将神经性疾病列为公共卫生的最大威胁之一[5]。**捕捉到的人脑解剖和功能的内在复杂性使得神经成像严重依赖于先进的计算分析。在2013年总结NSF(国家科学基金会)大脑绘图和工程研讨会成果的报告中，神经成像被确定为需要新的使能技术、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[6]。因此，我提出的研究非常及时，并建立在我在这一计算神经成像领域非常出色的记录上。我的计划专注于解决与多模式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。具体地说，我将开发新的范式来分析高空间分辨率的结构和功能神经成像数据(主要是MRI)，并将此类分析与高时间分辨率的脑信号(如脑电)相结合。我的具体目标包括开发新颖的多模式神经成像数据的综合计算模型，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它们的相互作用，在单一的统一分析框架中整合解剖学和生理学信息，发现神经疾病的新型基于图像的生物标记物，以及针对特定现实生活中的神经疾病，如阿尔茨海默氏症、帕金森氏症和中风，创建实用工具和软件应用程序。

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.