The dense circuit structure of the mammalian brain remains unknown. The roughly 86 billion neurons use extremely densely packed circuit networks for communication. Developments in three-dimensional electron microscopy (3D EM) have made possible the imaging of brain regions rich in synaptic connections, but dense reconstruction of connectomes has been challenging.

Now, a team from the department of connectomics at the Max Planck Institute for Brain Research in Germany have imaged and analyzed a piece of tissue from the cerebral cortex of a four-week-old mouse, obtained via biopsy from the somatosensory cortex, a part of the cortex occupied with the representation and processing of touch.

The results are published in Science, in a paper titled, “Dense connectomic reconstruction in layer 4 of the somatosensory cortex.

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. This reconstruction was both 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.

“We were surprised how much information and precision is found even in a still relatively small piece of cortex,” said Moritz Helmstaedter, PhD, director of the department of connectomics at the Max Plank Institute.

 

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, a PhD student in the lab and first author of the study, used particular configurations of synapse pairs to study the degree to which they were in agreement with activity-related learning processes. “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,” explained Motta. Since the mouse had a normal laboratory life until the brain biopsy at four 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.

The reported methods may have substantial implications for the transfer of insights about biological intelligence to artificial intelligence. “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,” stated Helmstaedter.

“Being able to take a piece of cortex, process it diligently, and then obtain the entire communication map from that beautiful network is what we have been working for over the last decade,” described Helmstaedter.

The researchers concluded: “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.”

 

 

 

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