2018
DOI: 10.3389/fninf.2018.00074
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Neural Reconstruction Integrity: A Metric for Assessing the Connectivity Accuracy of Reconstructed Neural Networks

Abstract: Neuroscientists are actively pursuing high-precision maps, or graphs consisting of networks of neurons and connecting synapses in mammalian and non-mammalian brains. Such graphs, when coupled with physiological and behavioral data, are likely to facilitate greater understanding of how circuits in these networks give rise to complex information processing capabilities. Given that the automated or semi-automated methods required to achieve the acquisition of these graphs are still evolving, we developed a metric… Show more

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Cited by 13 publications
(12 citation statements)
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“…Graph integrity was measured based on neural reconstruction integrity (NRI) [9], a metric that measures graph similarity according to precision and recall of intracellular paths. Incorrect edges (additions or deletions) are penalized and used to create a precision and recall score reflective of graph quality.…”
Section: Experiments and Resultsmentioning
confidence: 99%
“…Graph integrity was measured based on neural reconstruction integrity (NRI) [9], a metric that measures graph similarity according to precision and recall of intracellular paths. Incorrect edges (additions or deletions) are penalized and used to create a precision and recall score reflective of graph quality.…”
Section: Experiments and Resultsmentioning
confidence: 99%
“…Neuroscience questions may be reformulated as analyses on a graph, and the neuroscientist may add graph theory to the toolbox of strategies with which to understand the brain [6,9]. Such questions include searching for specific subgraph structures, investigating the connectivity of specific neuron cell types or categories, proofreading connectomes for accuracy, and generating summary statistics on the graph as a whole [10,11,2,12,13,14,8].…”
Section: Introductionmentioning
confidence: 99%
“…We will show later that evaluating segmentation around synapses more directly results in less optimistic scoring compared to traditional metrics like (Meilă, 2003 ). Recent work in Reilly et al ( 2017 ) also introduced a metric that more appropriately weighs the impact of synapses on segmentation, though it does not explicitly consider connectivity correctness between neurons.…”
Section: Introductionmentioning
confidence: 99%
“…One potential solution is to define S and G in Equation 1 over a set of exemplar points representing synapses, instead of all segmentation voxels as done in Plaza ( 2016 ) and Plaza and Berg ( 2016 ). A similar strategy of measuring groupings of synapses was introduced in Reilly et al ( 2017 ), which additionally breaks down results per neuron making the results more interpretable. While these metrics better emphasize correctness near synapses, it is not obvious how to interpret error impact to connectivity pathways.…”
Section: Introductionmentioning
confidence: 99%
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