The brain is by far our most complex organ. It’s fair to say there’s so much to learn that we don’t know what we don’t know. One new study involves a tiny piece of brain tissue sample taken from the cortex of an epilepsy patient. The cortex is where learning, sensory processing, and problem-solving happens.
Researchers sliced the sample into 5,000 ultra-thin tissue sections and stained them to “color identify” different cells and features which were then visualized with an electron microscope. They digitalized the images and used AI to analyze the one point 4 million gigabytes of data! The spectacularly colorful images of the sliced tissues were then assembled into a 3D rendering.
It allowed researchers to see communication networks and how different brain cells are organized and interact. They found more than 55,000 cells and 150 million synapses. Synapses are junctions between neurons where chemicals or electric signals flow to allow communication between the cells. Surprisingly, more than 90 percent of the neurons there had just one to two connections with other cells. But some had up to 50. They found some cells formed knots around themselves and some neurons paired with other neurons to form a mirror image. More studies could answer why.
By making all the data available to other researchers, this work will continue to yield a treasure trove of new information on the brain.
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Cubic millimetre of brain mapped in spectacular detail
Google scientists have modeled a fragment of the human brain at nanoscale resolution, revealing cells with previously undiscovered features.
A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution
Although the functions performed by most of the vital organs in humans are not very different compared with other animals, those performed by the human brain clearly separate us from the rest of life on the planet. However, detailed knowledge concerning the synaptic circuitry underlying human brain function is lacking. Connectomic imaging approaches are now available to render neural circuits of sufficiently large volume and high enough resolution to study the connectivity at the level of individual neurons and their synaptic connections but over a scale comprising thousands of neurons. Generating such a dataset was the goal of this project.