Cell Rep: 3D-printed brain structure reveals neural circuit structure

, June 10, 2020 /--- PRNewswire/ -- In a recent study, the authors provided a more reliable and standardized analysis of spatial tissue of complex neural circuits by developing automated 3D brain imaging data analysis techniquesKAIST researchers have developed a new algorithm for brain imaging data analysis that accurately and quantitatively maps complex neural circuits to standardized 3D modelsbrain imaging data analysis is essential in neuroscience researchHowever, the analysis of the obtained brain imaging data depends to a large extent on manual processing, which does not guarantee the accuracy, consistency and reliability of the results(photo source:routine brain imaging data analysis usually begins with the discovery of a two-dimensional brain image that is visually similar to the brain image obtained in the experimentThe area of interest (ROI) of the atlas image is then manually matched with the resulting image and the number of neurons marked in the ROI is calculatedThis visual matching process between brain images obtained byexperiments and two-dimensional brain map images has become one of the main sources of errors in brain imaging data analysis, as it is highly subjective, sample-specific and susceptible to human errorThe process of manually analyzing brain images is also laborious, so it is a daunting task to study complete 3-D neuronal tissue across the brainto address these issues,, the KAIST research team, led by Professor Se-Bum Paik of the Department of Biological and Brain Engineering, developed a new brain imaging data analysis software called AMaSiNe (Single Neuron Auto-3-D Mapping), the results of which were published in the recent journal Cell ReportsAMaSiNe automatically detects the location of a single neuron from multiple brain images and accurately maps all data to a common standard 3-D reference spaceThe algorithm automatically matches similar features in images and calculates image similarity scores, so that brain data from different animals can be compared directlythis function-based quantitative image comparison technique uses only a small number of brain-sliced image samples to improve the accuracy, consistency and reliability of the results, and help standardize brain imaging data analysisdifferent from other existing brain imaging data analysis methods, AMaSiNe can also automatically find alignment conditions in never aligned and deformed brain images and draw accurate ROI without any hassle of manual verificationfurther, the authors demonstrate that AMaSiNe produces consistent results with brain slice dyed images using various methods, including DAPI, Nissl, and spontaneous fluorescence( Bioon.com)
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Automated 3D brain imaging data analysis enables precise mapping of complex neural circuits
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Song, J.H., et al(2020)Precise Mapping of Single Neurons by Calibrated 3D Reconstruction of Brain Slices Reveals Topographic Projection in Mouse Visual Cortex.
Cell Reports.
doi.org/10.1016/j.celrep.2020.107682.