About this Research Topic
Neurocartography, the emerging field of high-resolution brain mapping, is now exploring volumes exceeding a cubic millimeter at sub-micron resolution. Advances in instrumentation, infrastructure, and processing methods are among the catalysts for these improvements, and researchers are rapidly advancing across many related fronts within the imaging-to-processing-to-inference pipeline.
This Research Topic is dedicated to highlighting:
(1) Recent imaging methods and resulting datasets that demonstrate the ability to create and analyze large, high-resolution volumes of brain tissue for scientific inquiry
(2) Solutions for addressing computational challenges in big data neuroscience
Tools and methods for processing brain image volumes (e.g., computer vision, segmentation, pipelining tools)
(3) Multimodal techniques to facilitate large scale analysis
(4) Analysis methods to understand the resulting brain networks, including graph inference and evaluation metrics
Research to create and explore dense biofidelic maps of synaptic cortical circuitry is still in its infancy. The field of “big data neuroscience” represents a paradigm shift from the hand-tracing of individual cells, dating back to the research of Santiago Ramon y Cajal, to rapid, machine-driven analysis of large image volumes leveraging automated or semi-automated techniques.
To advance understanding of these high resolution datasets, we aim to collect inputs from a variety of laboratories and research institutions to develop best practices and common processing workflows that accelerate scientific discovery. Understanding the ultrastructure of the brain will provide important new insights into models of brain computation and function, and refine our understanding of typical neuronal circuits. Additionally, comparing the anatomy of typical brain circuits to similar circuits in dysfunctioning brains may lead to new insights into the fundamentals of brain pathology and, therefore, potential targets for future interventions.
This Research Topic is focused primarily on imaging methodologies or other techniques that collect information at the sub-micron scale (e.g., electron microscopy, X-ray microtomography, array tomography, CLARITY), and is appropriate for experimental questions involving structural and/or functional data. Research articles may discuss diverse topics ranging from a survey of challenges and best practices in the field to state-of-the art algorithmic solutions to a key problem or demonstration of a novel imaging technique.
This Research Topic is dedicated to highlighting:
(1) Recent imaging methods and resulting datasets that demonstrate the ability to create and analyze large, high-resolution volumes of brain tissue for scientific inquiry
(2) Solutions for addressing computational challenges in big data neuroscience
Tools and methods for processing brain image volumes (e.g., computer vision, segmentation, pipelining tools)
(3) Multimodal techniques to facilitate large scale analysis
(4) Analysis methods to understand the resulting brain networks, including graph inference and evaluation metrics
Research to create and explore dense biofidelic maps of synaptic cortical circuitry is still in its infancy. The field of “big data neuroscience” represents a paradigm shift from the hand-tracing of individual cells, dating back to the research of Santiago Ramon y Cajal, to rapid, machine-driven analysis of large image volumes leveraging automated or semi-automated techniques.
To advance understanding of these high resolution datasets, we aim to collect inputs from a variety of laboratories and research institutions to develop best practices and common processing workflows that accelerate scientific discovery. Understanding the ultrastructure of the brain will provide important new insights into models of brain computation and function, and refine our understanding of typical neuronal circuits. Additionally, comparing the anatomy of typical brain circuits to similar circuits in dysfunctioning brains may lead to new insights into the fundamentals of brain pathology and, therefore, potential targets for future interventions.
This Research Topic is focused primarily on imaging methodologies or other techniques that collect information at the sub-micron scale (e.g., electron microscopy, X-ray microtomography, array tomography, CLARITY), and is appropriate for experimental questions involving structural and/or functional data. Research articles may discuss diverse topics ranging from a survey of challenges and best practices in the field to state-of-the art algorithmic solutions to a key problem or demonstration of a novel imaging technique.
Keywords: connectomics, big data, pipeline, nanoscale, mesoscale, graph, imaging, computer vision, machine learning, data archive
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