For the in vivo investigation of brain connectome, diffusion MRI (dMRI) is an important tool as it provides highly sensitive imaging markers and allows the examination of connection paths via tractography. With the success of the Human Connectome Project (HCP), high resolution, multi-shell diffusion imaging is emerging as the standard approach for dMRI data acquisition in connectome studies. To fully unleash the potential of multi-shell dMRI, in this project we will develop a suite of novel computational tools that jointly estimate fiber orientation distributions (FOD) and compartmental parameters. With FOD-based tractography, we can reliably resolve crossing fibers and reconstruct fiber bundles that faithfully follow known anatomy such as the retinotopy of visual pathways. Compartmental parameters provide sensitive imaging markers for studying local cellular environment surrounding the axons. Our tools are generally applicable for both human and mouse connectome research. One main challenge in diffusion tractography is the lack of rigorous validations with biologically meaningful ground truth. With large-scale tracer injection data of mouse brains from the Mouse Connectome Project (MCP) at USC and the Allen Mouse Brain Connectivity Atlas, we will perform a systematic validation and optimization of our FOD-based techniques from the denoising of imaging signals to the configuration of compartment models to the selection of tractography parameters. This will create a well-validated system for studying mouse connectome with multi-shell imaging, and provide intuitive guidelines for the design of human studies with FOD-based connectome. There are three specific aims in our project: 1. To develop a general computational framework for the joint estimation of fiber orientation and compartment models from multi-shell diffusion imaging. 2. To validate and optimize the computational tools using multi-shell dMRI data of mouse brains and axonal projection maps from tracer injections. 3. To develop a comprehensive toolkit for fiber bundle reconstruction from multi-shell dMRI data of human brains. In this project we will apply our software tools to analyze data collected in two disease studies. In the first study, we will focus on the cortico-striato- thalamo-cortical (CSTC) network and examine its connectivity changes in the BTBD3 mouse model of obsessive-compulsive disorder (OCD). In the second study, we will apply our tools to study the relation of retinal impairment and visual pathway integrity via the retinotopy-preserving connectivity between visual areas. All software tools developed in this project will be distributed freely to the research community.
The novel software tools developed in this project will greatly improve the mapping of brain connectome across species and diseases. This will produce highly sensitive imaging markers for the detection of early brain changes in neurological, psychiatric, and low vision disorders, which will ultimately improve the early diagnosis and treatment of these diseases.
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| Gahm, Jin Kyu; Shi, Yonggang; Alzheimer’s Disease Neuroimaging Initiative (2018) Riemannian metric optimization on surfaces (RMOS) for intrinsic brain mapping in the Laplace-Beltrami embedding space. Med Image Anal 46:189-201 |
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| Wang, Junyan; Shi, Yonggang (2017) Kernel-Regularized ICA for Computing Functional Topography from Resting-state fMRI. Med Image Comput Comput Assist Interv 10433:373-381 |
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| Gahm, Jin Kyu; Shi, Yonggang (2017) Holistic Mapping of Striatum Surfaces in the Laplace-Beltrami Embedding Space. Med Image Comput Comput Assist Interv 10433:21-30 |
| Sun, Wei; Amezcua, Lilyana; Shi, Yonggang (2017) FOD Restoration for Enhanced Mapping of White Matter Lesion Connectivity. Med Image Comput Comput Assist Interv 10433:584-592 |
| Wang, Junyan; Aydogan, Dogu Baran; Varma, Rohit et al. (2017) Topographic Regularity for Tract Filtering in Brain Connectivity. Inf Process Med Imaging 10265:263-274 |
