Project Summary Human brain connectomics and imaging genomics are two emerging research fields enabled by recentadvances in multi-modal neuroimaging and high throughput omics technologies. Integrating brain imaginggenomics and connectomics holds great promise for a systematic characterization of both the human brainconnectivity and the connectivity-based neurobiological pathway from its genetic architecture to its influenceson cognition and behavior. Rich multi-modal neuroimaging data coupled with high density omics data areavailable from large-scale landmark studies such as the NIH Human Connectome Project (HCP) andAlzheimer's Disease Neuroimaging Initiative (ADNI). The unprecedented scale and complexity of these datasets, however, have presented critical computational bottlenecks requiring new concepts and enabling tools. To bridge the gap, this project is proposed to develop and validate novel integrative bioinformaticsapproaches to human brain genomics and connectomics, and has three aims. Aim 1 is to develop a novelcomputational pipeline for a systematic characterization of structural connectome optimized for imaginggenomics, where special consideration will be taken to address important issues including reliable tractographyand network construction, systematic extraction of network attributes, identification of important networkcomponents (e.g., hubs, communities and rich clubs), prioritization of network attributes towards genomicanalysis, and identification of outcome-relevant network measures. Aim 2 is to develop novel bioinformaticsstrategies to determining genetic basis of structural connectome, including novel approaches for analyzinggraph-based phenotype data and learning outcome-relevant associations, and an ensemble of effectivelearning modules to handle a comprehensive set of scenarios on mining genome-connectome associations atthe genome-wide connectome-wide scale. Aim 3 is to develop a visual analytic software system for interactivevisual exploration and mining of fiber-tracts and brain networks with their genetic determinants and functionaloutcomes, where new visualization and exploration methods will be implemented for seamlessly combininghuman expertise and machine intelligence to enable novel contextually meaningful discoveries. This project is expected to produce novel bioinformatics algorithms and tools for comprehensive jointanalysis of large scale genomics and connectomics data. The availability of these powerful methods and toolsis critical for full knowledge discovery and exploitation of major connectomics and imaging genomics initiativessuch as HCP and ADNI. In addition, they can also help enable new computational applications in many otherbiomedical research areas where integrative analysis of connectomics and genomics data are of interest. Viathorough test and evaluation on HCP and ADNI data, these methods and tools will be demonstrated to haveconsiderable potential for a better understanding of the interplay between genes, brain connectivity andfunction, and thus be expected to impact biomedical research in general and benefit public health outcomes.
|Effective start/end date||8/1/16 → 4/30/20|
- National Institutes of Health: $500,696.00