Worldwide, over 285 million people are visually impaired, and 39 million of those are blind. Visual impairments (blindness and low vision) severely and negatively impact the quality of life. New sight-restoration treatments have arrived for some blinding diseases. An electronic retinal prosthesis is approved for sight restoration in advanced retinitis pigmentosa. Gene therapy trials have shown promise for Leber's congenital amaurosis. Stem cell treatments for age-related macular degeneration are starting clinical trials. In some cases, remarkable results have been demonstrated, suggesting that at least partial vision restoration is possible after prolonged blindness. However, psychophysical data from clinical trials often show large variance in outcomes. Preconditions in brain structure and function associated with the central visual pathway (CVP) may underlie some of the variance. The effect of complete blindness upon the brain is well studied, but most ophthalmologic disease produces effects that vary from point-to-point across the retina. How does the precise topography of eye disease map onto changes in brain? Is acquired retinal damage related to local effects upon the structure and function of the CVP? Are any of these effects of vision loss on the CVP reversible? We propose to answer these questions, which we believe are crucial to the on-going efforts in developing sight- restoration treatments, by combining advanced retinal imaging with the brain-mapping techniques developed in the Human Connectome Project (HCP), using novel yet robust analytical methods. We will reveal the relationships between retinal pathology and their downstream impact on the CVP in unprecedented detail over the natural courses of blinding diseases and their treatments. We will make available to the research community the raw and processed data on retinal pathology for a range of blinding diseases, along with neuroimaging data collected with the HCP protocol. We will provide the computer codes used to extract relevant features from the retinal and neural datasets and the statistical models that relate the two. The extensive datasets and the associated data-analysis tools will form a transformative foundation for studies of neural plasticity associated with visual impairments and sight restoration.
More than 285 million people are visually impaired worldwide (WHO, 2014), including 39 million who are blind. In the United States, 6.6 million adults and 0.66 million children have significant vision loss (American Community Survey, 2012). Visual impairments (blindness and low vision) severely and negatively impact the quality of life. Major blinding diseases include age-related macular degeneration, diabetic retinopathy, glaucoma and retinitis pigmentosa. The long-term impact of blindness and visual impairments on the brain structures and functions associated with visual processing is not known. This knowledge gap has impeded our ability to predict which patients may benefit from sight restorative therapies and then their outcomes after a prolonged period of blindness, which in turn limits our ability to develop new treatments. The proposed research will fill this knowledge gap by amassing retinal imaging and vision assessment data, then correlating each local region of the disease-affected visual field with the retinotopically associated brain anatomic structures and functions, inferred from detailed neuroimaging data collected from the same patients using protocols specified in the Human Connectome Project. The resulting database and the newly developed data- analysis tools will transform our understanding of neural plasticity associated with vision loss and sight restoration.
|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|
|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|
|Tang, Yuchun; Sun, Wei; Toga, Arthur W et al. (2017) A probabilistic atlas of human brainstem pathways based on connectome imaging data. Neuroimage 169:227-239|
|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|
|Kammen, Alexandra; Law, Meng; Tjan, Bosco S et al. (2016) Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis. Neuroimage 125:767-779|
|Aydogan, Dogu Baran; Shi, Yonggang (2016) Probabilistic Tractography for Topographically Organized Connectomes. Med Image Comput Comput Assist Interv 9900:201-209|
|Gahm, Jin Kyu; Shi, Yonggang (2016) Riemannian Metric Optimization for Connectivity-driven Surface Mapping. Med Image Comput Comput Assist Interv 9900:228-236|
|Morgan, Jessica I W (2016) The fundus photo has met its match: optical coherence tomography and adaptive optics ophthalmoscopy are here to stay. Ophthalmic Physiol Opt 36:218-39|