) Human brain connectomics and imaging genomics are two emerging research fields enabled by recent advances in multi-modal neuroimaging and high throughput omics technologies. Integrating brain imaging genomics and connectomics holds great promise for a systematic characterization of both the human brain connectivity and the connectivity-based neurobiological pathway from its genetic architecture to its influences on cognition and behavior. Rich multi-modal neuroimaging data coupled with high density omics data are available from large-scale landmark studies such as the NIH Human Connectome Project (HCP) and Alzheimer's Disease Neuroimaging Initiative (ADNI). The unprecedented scale and complexity of these data sets, 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 bioinformatics approaches to human brain genomics and connectomics, and has three aims.
Aim 1 is to develop a novel computational pipeline for a systematic characterization of structural connectome optimized for imaging genomics, where special consideration will be taken to address important issues including reliable tractography and network construction, systematic extraction of network attributes, identification of important network components (e.g., hubs, communities and rich clubs), prioritization of network attributes towards genomic analysis, and identification of outcome-relevant network measures.
Aim 2 is to develop novel bioinformatics strategies to determining genetic basis of structural connectome, including novel approaches for analyzing graph-based phenotype data and learning outcome-relevant associations, and an ensemble of effective learning modules to handle a comprehensive set of scenarios on mining genome-connectome associations at the genome-wide connectome-wide scale.
Aim 3 is to develop a visual analytic software system for interactive visual exploration and mining of fiber-tracts and brain networks with their genetic determinants and functional outcomes, where new visualization and exploration methods will be implemented for seamlessly combining human expertise and machine intelligence to enable novel contextually meaningful discoveries. This project is expected to produce novel bioinformatics algorithms and tools for comprehensive joint analysis of large scale genomics and connectomics data. The availability of these powerful methods and tools is critical for full knowledge discovery and exploitation of major connectomics and imaging genomics initiatives such as HCP and ADNI. In addition, they can also help enable new computational applications in many other biomedical research areas where integrative analysis of connectomics and genomics data are of interest. Via thorough test and evaluation on HCP and ADNI data, these methods and tools will be demonstrated to have considerable potential for a better understanding of the interplay between genes, brain connectivity and function, and thus be expected to impact biomedical research in general and benefit public health outcomes.
) Integrating human connectomics and brain imaging genomics offers enormous potential, allowing us to perform systems biology approaches of the brain to better understand the interplay between genes, brain connectivity, and phenotypic outcomes (e.g., cognition, behavior, disorder). This proposal seeks to develop novel bioinformatics methods and software tools for integrative study of human connectomics and brain imaging genomics. These methods and tools can be applied to: (1) study normal brain functions to impact biomedical research in general, and (2) study brain disorders to improve public health outcomes by facilitating diagnostic and therapeutic progress.
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