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FOCUS ON MAPPING THE BRAIN PERSPECTIVE
Cellular-resolution connectomics: challenges of dense neural circuit reconstruction
Moritz Helmstaedter
npg 2013 Nature America, Inc. All rights reserved.
Neuronal networks are high-dimensional graphs that are packed into three-dimensional nervous tissue at extremely high density. Comprehensively mapping these networks is therefore a major challenge. Although recent developments in volume electron microscopy imaging have made data acquisition feasible for circuits comprising a few hundreds to a few thousands of neurons, data analysis is massively lagging behind. The aim of this perspective is to summarize and quantify the challenges for data analysis in cellular-resolution connectomics and describe current solutions involving online crowd-sourcing and machine-learning approaches.
Brains are unique not in the number of cells they comprise (about 85 billion neurons in the case of the human brain1,2) but in the extent of direct and specific communication between their cells via synaptic connections (each neuron has on the order of 1,000 synaptically coupled partner neurons). Mapping the resulting complex connectivity graph is the goal of connectomics. At the coarse level, inter-areal projections are tracked either noninvasively using variants of diffusion imaging in humans3 (see Review4 in this issue) or invasively using tracer injections combined with high-throughput imaging in mice (Mouse Brain Architecture Project5 (http://brainarchitecture.org/
Web End =http://brainarchitecture.org/ )
and Allen Brain Connectivity Atlas (http://connectivity.brain-map.org/
Web End =http://connectivity.
http://connectivity.brain-map.org/
Web End =brain-map.org/ ) among other initiatives; see Review6
in this issue). However, the mapping coverage achieved using these approaches is still not sufficient to resolve the complete set of neuronal networks contained in the tissue. One voxel of magnetic resonance imaging data with typical one-cubic-millimeter resolution, for example, contains millions of neuronal cell bodies and several kilometers of neuronal wires.
At the other end of the resolution spectrum, the aim of connectomics is to image and analyze neuronal circuits densely in sufficiently large volumes but at
single-cell and single-neurite resolution. Neuroscience is very data-poor in this respect, contrary to the assumptions made by contemporary simulation initiatives (The Human Brain Project7; http://www.humanbrainproject.eu/
Web End =http://www.human http://www.humanbrainproject.eu/
Web End =brainproject.eu/ ), and it is not known whether there are cases in which the measurement of cellular-resolution connectivity graphs can in fact be replaced by simplified low-order statistical assumptions about neuronal wiring. To date, only a few...