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dc.contributor.advisorEdward S. Boyden, III.en_US
dc.contributor.authorDai, Peilunen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences.en_US
dc.date.accessioned2019-03-01T19:53:21Z
dc.date.available2019-03-01T19:53:21Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/120630
dc.descriptionThesis: S.M. in Neuroscience, Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 27-29).en_US
dc.description.abstractReconstruction of the 3D morphology of neurons is an essential step towards the analysis and understanding of the structures and functions of neural circuits. Optical microscopy has been a key technology for mapping brain circuits throughout the history of neuroscience due to its low cost, wide usage in biological sciences and ability to read out information-rich molecular signals. However, conventional optical microscopes have limited spatial resolution due to the diffraction limit of light, requiring a tradeoff between the density of neurons to be imaged and the ability to resolve finer structures. A new technology called expansion microscopy (ExM), which physically expands the specimen multiple times before optical imaging, enables us to image fine structures of biological tissues with super resolution using conventional optical microscopes. With the help of ExM, we can also read out molecular information in the neural tissues optically. In this thesis, I will introduce our study of the properties, via computer simulation, of a candidate automated approach to algorithmic reconstruction of dense neural morphology, based on simulated data of the kind that would be obtained via two emerging molecular technologies-expansion microscopy (ExM) and in-situ molecular barcoding. We utilize a convolutional neural network to detect neuronal boundaries from protein-tagged plasma membrane images obtained via ExM, as well as a subsequent supervoxel-merging pipeline guided by optical readout of information-rich, cell-specific nucleic acid barcodes. We attempt to use conservative imaging and labeling parameters, with the goal of establishing a baseline case that points to the potential feasibility of optical circuit reconstruction, leaving open the possibility of higher-performance labeling technologies and algorithms. We find that, even with these conservative assumptions, an all-optical approach to dense neural morphology reconstruction may be possible via the proposed algorithmic framework.en_US
dc.description.statementofresponsibilityby Peilun Dai.en_US
dc.format.extent29 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectBrain and Cognitive Sciences.en_US
dc.titleTowards optical connectomics : feasibility of 3D reconstruction of neural morphology using expansion microscopy and in situ molecular barcodingen_US
dc.title.alternativeFeasibility of 3D reconstruction of neural morphology using expansion microscopy and in situ molecular barcodingen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Neuroscienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences
dc.identifier.oclc1086612421en_US


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