Volumetric optical connectome microscopy of human cerebellar circuitry

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Project Summary/Abstract The goal in seeking a K99/R00 Pathway to Independence Award is to establish myself as an independent principal investigator to study the structural-functional relationship of the brain circuitry in normal and brain disorders. The proposed project, driven by the need for understanding the human brain with high-resolution high-throughput tools and my extensive experience in biomedical optics for neuroimaging, aims to establish a versatile tool to reconstruct the circuitry and neuronal architecture in human cerebellum, understand the disruptive impacts of cerebellar degenerative disease, and combine with MRI models to seek novel biomarkers that will potentially influence the clinical assessment. Despite the tremendous advances of light microscopy since Santiago Ramón y Cajal's pioneering work in drafting axonal tracts, our knowledge on how the 80-100 billions of neurons connect together to form complex functions in human brain is still limited. Presently, there is no volumetric microscopy technique that can map the circuitry and architecture of human brain with high integrity. Here I propose to develop a volumetric optical connectome microscopy (VOCM), for reconstructing the human cerebellum with unprecedented resolution and scales, and mapping the connectivity and neuronal architectures from a global perspective. VOCM is based on a polarization sensitive optical coherence tomography and a vibratome slicer to image large-scale ex vivo brain at a micrometer-scale resolution. Importantly, this technology allows volumetric reconstruction preserving an ultra-high accuracy without tissue distortions; therefore overcomes the 100 years challenge of all histology based methods in tracing long fiber tracts and inspecting sophisticated cortical folding in the human brain. The high-quality data generated by VOCM will be fit into MRI models to construct an ultra-high resolution atlas of human cerebellum to provide anatomical labels that are not available in current MRI tools. By applying VOCM, the project further explores 3D pathological patterns of cerebellar disorder. Multiple system atrophy cerebellar type (MSA-C) is a fatal neurodegenerative disease manifested by severe cerebellar and brainstem atrophy. Despite its rare incidence, MSA-C shares common phenotypic characteristics with other neurological diseases. Studying the neuroanatomical substrates and pathological trajectory of MSA-C could advance our understanding of the impact of cerebellar disorders and cerebellar affected diseases. Particularly, the project will characterize the architecture and circuitry disruptions associated with neurodegeneration in MSA-C. We will then use the high-resolution ex vivo dataset to make predictions in vivo, and allow an MRI assessment that would not be possible otherwise. The proposed research is conducted at Martinos Center, Massachusetts General Hospital (MGH), which is an ideal environment developing cutting edge biomedical technologies and interacting with a large community of experts with multidisciplinary background. I have formed a strong mentoring team: Dr. Bruce Fischl, director of the Computational Core at Martinos Center; Dr. David Boas, director of the Optics Division at Martinos Center; and Dr. Jeremy Schmahmann, director of the MGH Ataxia Unit. I will leverage formal trainings in MRI modeling and analysis, cerebellar related brain diseases and neuropathology. The coursework on neuroanatomy, neurobiology and central nervous diseases complement my biomedical optics background and advance my knowledge on important neuroscience questions. The proposed trainings and research will prepare me with necessary skills, new tools, and intriguing data to launch an independent research program and writing further research grants after the completion of the R00 phase. I expect that the research will dramatically advance our current knowledge on brain science, have an impact on clinical revolutions, and advocate public awareness of brain health in greater populations.