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

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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.