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: Abugharbieh, 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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587645
• 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.

医学成像是医疗保健领域增长最快的专业之一，占医疗器械市场总量的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]。越来越多的成本效益的无创成像技术的普及使前所未有的患者数据采集率。到2010年，全球记录的调查超过50亿次[2]，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护理的日益重视继续推动医学成像的扩散。例如，在加拿大，仅在2012年就进行了超过170万次磁共振成像（MRI）扫描，是2003年的两倍[3]。 然而，医学成像革命是有代价的;多维高分辨率数据的泛滥带来了严重的健康信息学挑战。新兴的趋势是融合不同的模态，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年将以超过7%的速度增长，达到24亿美元。 据估计，全世界有超过10亿人（即六分之一的人）受到某些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。诸如阿尔茨海默病（AD）和其他痴呆、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脑疾病对人类健康造成严重影响。特别是随着世界人口老龄化和预期寿命延长，这类疾病的流行率继续上升。事实上，由于其重大的疾病负担，包括巨大的相关经济和社会成本，世界卫生组织（WHO）将神经系统疾病列为对公共卫生的最大威胁之一。 所捕获的人脑解剖结构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年总结NSF（美国国家科学基金会）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，我提出的研究是非常及时的，并建立在我在计算神经成像这一领域非常强大的记录。我的计划侧重于解决具有挑战性的计算处理和分析问题，这些问题与多模态大脑图像的协同使用有关。具体来说，我将开发新的范例，用于分析高空间分辨率的结构和功能神经成像数据（主要是MRI），以及联合收割机这样的分析与高时间分辨率的大脑信号，如脑电图（EEG）。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，将解剖学和生理学信息整合在一个统一的分析框架内，发现新的基于图像的神经疾病生物标志物，以及为特定的真实的生活神经系统疾病环境（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。