Brain anatomy revealed in startling detail
The mammalian cerebral cortex is an enormously complex network of neuronal processes that are long and thin, branching, and extremely densely packed. This high packing density has made the reconstruction of cortical neuronal networks challenging. Motta et al. used advanced automated imaging and analysis tools to reconstruct with high spatial resolution the morphological features of 89 neurons and their connections in the mouse barrel cortex. The reconstruction covered an area more than two orders of magnitude larger than earlier neuroanatomical mapping attempts. This approach revealed information about the connectivity of inhibitory and excitatory synapses of corticocortical as well as excitatory thalamocortical connections.
Science, this issue p. eaay3134
The brain of mammals consists of an enormously dense network of neuronal wires: the axons and dendrites of nerve cells. Their packing density is so high that light-based imaging methods have so far only been able to resolve a very small fraction of nerve cells and their interaction sites, the synapses, in mammalian cortex. Recent advances in three-dimensional (3D) electron microscopy allow researchers to image every nerve cell and all chemical synapses in a given piece of brain tissue, opening up the possibility of mapping neuronal networks densely, not just sparsely. Although there have been substantial advances in imaging speed, the analysis of such 3D image data is still the limiting step. Therefore, dense reconstructions of cortical tissue have thus far been limited to femtoliter-scale volumes, keeping the systematic analysis of axons, neuronal cell bodies and their dendrites of different types, and the dense connectome between them out of reach.
Image analysis has made decisive progress using artificial intelligence–based methods, but the resulting reconstructions of dense nerve tissue are still too error-prone to be scientifically meaningful as is. To address this, human data analysis has been integrated into the generation of connectomes and it is the efficiency of this human–machine data analysis that now determines progress in connectomics. We therefore focused on efficiency gains by: (i) improving the automated segmentation quality, (ii) analyzing the automated segmentation for locations of likely errors and directing the human work to these locations only, and (iii) optimizing human data interaction by helping annotators to immediately understand the problem to be solved, allowing fast, in-browser parallel data flight, and by minimizing latency between annotator queries. With this, close to 100 student annotators solved hundreds of thousands of reconstruction problems within just 29 s each, including all preparation and transition time.
We reconstructed 2.7 m of neuronal wires densely in layer 4 of mouse somatosensory cortex within only ~4000 invested human work hours, yielding a reconstruction ~300 times larger than previous dense cortical reconstructions at ~20-fold increased efficiency, a leap for the dense reconstruction of connectomes. The resulting connectome between 6979 presynaptic and 3719 postsynaptic neurites with at least 10 synapses each, comprising 153,171 synapses total, was then analyzed for the dense circuit structure in the cerebral cortex. We found that connectomic data alone allowed the definition of inhibitory axon types that showed established principles of synaptic specificity for subcellular postsynaptic compartments, but that at scales beyond ~5 μm, geometric predictability of the circuit structure was low and coarser models of random wiring needed to be rejected for dense cortical neuropil. A gradient of thalamocortical synapse density along the cortical axis yielded an enhanced variability of synaptic input composition at the level of single L4 cell dendrites. Finally, we quantified connectomic imprints consistent with Hebbian synaptic weight adaptation, obtaining upper bounds for the fraction of the circuit that could have undergone long-term potentiation.
By leveraging human–machine interaction for connectomic analysis of neuronal tissue, we acquired the largest connectome from the cerebral cortex to date. Using these data for connectomic cell-type definition and the mapping of upper bounds for the learned circuit fraction, we establish an approach for connectomic phenotyping of local dense neuronal circuitry in the mammalian cortex, opening the possibility for the connectomic screening of nervous tissue from various cortices, layers, species, developmental stages, sensory experience, and disease conditions.