Since each nerve cell in the main part of mammalian brains, the so-called cerebral cortex, communicates with about 1,000 other nerve cells via synapses placed along these cables over long distances, one expects a total of about 5 million kilometers of wires packed into our skulls - more than 10 times longer than all highways on our planet, in each of our brains.
The resulting cable convolute is of such density and magnitude, that for more than 100 years, researchers have been able to only map connectivity between a miniscule fraction of neurons in a given piece of brain. Only the development of faster electron microscopic techniques ("3D EM") and of more efficient image analysis routines has made the dense mapping of neuronal networks possible. The novel field of "connectomics" has been pursuing the dense mapping of ever larger circuits in several species and brain regions.
In the work published in Science, a team around Max Planck Director Moritz Helmstaedter imaged and analyzed a piece of tissue from the cerebral cortex of a 4-week old mouse, obtained via biopsy from the somatosensory cortex, a part of the cortex occupied with the representation and processing of touch.
Here, the researchers applied optimized AI-based image processing and efficient human-machine interaction to analyze all of the about 400,000 synapses and about 2.7 meters of neuronal cable in the volume. With this, they produced a connectome between about 7,000 axons and about 3,700 postsynaptic neurites, yielding a connectome about 26 times larger than the one obtained from the mouse retina more than half a decade ago.
Importantly, this reconstruction was at the same time larger and about 33-times more efficient than the one applied to the retina, setting a new benchmark for dense connectomic reconstruction in the mammalian brain.
Fueled by this this methodological breakthrough in connectomics, the researchers analyzed the connectome for the patterns of circuitry present. In particular, they asked what fraction of the circuit showed properties that were consistent with the growth of synapses, mechanisms known to contribute to circuit formation and "learning".
Alessandro Motta, first author of the study and an electrical engineer by training, used particular configurations of synapse pairs to study the degree to which they were in agreement with activity-related learning processes ("LTP"). "Because some models of synaptic plasticity make concrete predictions about the increase in synaptic weight when learning, say, to identify a tree or a cat, we were able to extract the imprint of such potential processes even from a static snapshot of the circuit", explains Motta.
Since the mouse had had a normal laboratory life until the brain biopsy at 4 weeks of age, the scientists argue that the degree to which circuits are shaped by learning in "normal" sensory states can be mapped using their approach.
"We were surprised how much information and precision is found even in a still relatively small piece of cortex", says Helmstaedter, and adds "The goal of mapping neuronal networks in the cerebral cortex is a major scientific adventure, also because we hope to be able to extract information about how the brain is such an efficient computer, unlike today’s AI".
The researchers conclude: "We think that our methods, applied over a large range of cortical tissues from different brain areas, cortical layers, developmental time points, and species will tell us how evolution has designed these networks, and what impact experience has on shaping their fine-grained structure".
"Moreover, connectomic screening will allow the description of circuit phenotypes of psychiatric and related disorders - and tell us to what degree some important brain disorders are in fact connectopathies, circuit diseases."
MEDICA-tradefair.com; Source: Max-Planck-Institute for brain research
