May 28, 2024

Scientists have reconstructed a wiring diagram for a piece of human brain in unprecedented detail, revealing fresh idiosyncrasies and complexities in what many consider to be the most sophisticated object in the known universe.

Harvard researchers teamed up with experts in machine learning at Google to map the neural circuits, connections, supporting cells and blood supply in a piece of healthy tissue removed from the cortex of a 45-year-old woman who had undergone surgery for epilepsy.

The lump of brain amounted to a mere cubic millimeter of tissue, but working out the wiring still posed a major task for the team. Electron microscope images of more than 5,000 slices of the specimen revealed 57,000 individual cells, 150 m of neural connections and 23 cm of blood vessels.

The sample, which was a cubic millimeter in size, was taken from the brain of a 45-year-old woman who had undergone surgery. Photo: Google

“The goal was to get a high-resolution view of this most mysterious piece of biology that each of us carries around on our shoulders,” said Jeff Lichtman, a professor of molecular and cellular biology at Harvard. “The reason we haven’t done it before is that it’s damn challenging. It was really, really hard to do that.”

After cutting the tissue into slices less than 1,000 times thinner than the width of a human hair, the researchers took electron microscope images of each to capture details of brain structure down to the nanoscale, or thousandths of a millimeter. A machine learning algorithm then traced the paths of neurons and other cells through the individual sections, a painstaking process that would take humans years. The images comprised 1.4 petabytes of data, equivalent to 14,000 full-length, 4k resolution movies.

“We found a lot of things in this data set that are not in the textbooks,” Lichtman said. “We don’t understand those things, but I can tell you they suggest that there is a gap between what we already know and what we need to know.”

In one startling observation, so-called pyramidal neurons, which have large branches called dendrites protruding from their bases, showed a remarkable symmetry, with some facing forward and others facing backward. Other images revealed tight coils of axons, the thin fibers that carry signals from one brain cell to another, as if stuck at a roundabout before identifying the right exit and continuing on.

The researchers speculated that strong connections between neurons could explain well-learned behavior. Photo: D. Berger/Google Research & Lichtman Lab (Harvard University)

The map also revealed rare cases where neurons made extremely strong connections with other cells. Across the whole lump of brain tissue, more than 96% of axons made only one connection to a target cell, with 3% making two connections. But a handful made dozens of connections, and in one case more than 50, with a nearby cell. Details are published in the journal Science.

Lichtman speculated that such strong connections could help explain how well-learned behaviors—like taking your foot off the accelerator and slamming on the brakes at a red light—require almost no thought after enough practice. “I think these powerful connections may be part of the system of learned information and what learning looks like in the brain,” he said. The team makes the map freely available for other researchers to use.

For now, the researchers are not even thinking about mapping an entire human brain. The task is technologically too difficult, and healthy human brains do not grow on trees. Instead, the next project will be a multi-university collaboration with Google to reconstruct the wiring of an entire mouse brain. It could shed light on the brain circuits that make a mouse move toward Swiss cheese, and in turn, make a person pause outside a bakery. “You’ll gain some insight into how human will is guided by sensory experience,” Lichtman said. “There are really amazing opportunities, if you have a whole mouse brain, to even get insight into free will,” Lichtman said. “You know, a mouse is not a robot.”

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