That works beautifully provided that you know how to do it appropriately and that you're looking at a structure that's very homogeneous in the distribution of cells. It works perfectly for small parts of the brain; you want to count how many cells you have in the thalamus and motor nuclei, that's fine.
If you want to apply that to the whole brain, you run into the problem of how different the distribution of neurons is from one millimeter to the next. It's like taking a poll without knowing what you're doing, how people are distributed, or how they're concentrated You'd get a result but that doesn't necessarily mean that it's a good result, that it really represented the truth, let's say what you're going for.
It made it very difficult for people to actually get estimates of how many neurons composed whole brains of different species, much less a human brain, which is really large I think it was a mixture of different factors that got this magic number propagated so long and along with it, that story that we have 10 times as many glial cells as neurons in the human brain, which is just so not true.
Narration: What made you question that number? Herculano-Houzel: What made me realize that we didn't know the first thing about what brains are made of was a survey that I ran at a science museum in Brazil where I started working after I got my PhD. I ran a survey with people who visited the museum on a number of things about the brain like Great right?
I still don't know where that myth came from, but I started looking around and one of the possibilities was that you open textbooks and there it was. Do you know whoever actually counted and found that there are billion neurons in the brain, in the human brain, and 10 times as many glial cells? Everybody was like, "Um. I actually don't, but those are the numbers, aren't they? It's just hearsay. I went digging through the literature and that's when I realized that everybody thought that everybody else had already figured this out but nobody actually had.
Narration: Herculano-Houzel came up with an ingenious way to test how many neurons were actually in the human brain. The number she came up with? Herculano-Houzel: The average that we have so far is a total of 86 billion neurons and just as many non neuronal cells which includes not just glial, but also the endothelial cells. That's something that we're working on now.
That still leaves less than one glial cell per neuron in the brain as a whole. The thing is that this ratio between how many glial cells and how many neurons you have, that's highly variable across different parts of the brain. You can have two or maybe even three glial cells per neuron in some parts of the cortex, and less than 0. Getting those numbers for the first time was really exhilarating. Before that we had mice and rats, which you know, they're just mice and rats. From time to time, it spontaneously unleashes a wave of electric current that travels down its length.
If you deliver pulses of electricity to one end of the cell, the neuron may respond with extra spikes of voltage. Bathe the neuron in various neurotransmitters, and you can alter the strength and timing of its electrical waves.
Join together billion neurons—with trillion connections—and you have yourself a human brain, capable of much, much more. Already a subscriber? Sign in. But the mouse-brain cubic-millimetre project will be looking at , neurons, and other, similar programmes are also under way. The mouse-brain project will therefore enable scientists to explore complete local circuits, rather than single neurons with a sparse network of connections.
Its progress has led some to predict that the nanoscale connectome of a complete mouse brain — likely to produce around one exabyte one billion gigabytes of data — could be mapped in the next decade.
Others remain cautious. He thinks that the field should target intermediary-scale projects before tackling something as complex as the mouse brain. Plaza manages one such project.
Called FlyEM, it aims to produce a connectome of the central nervous system of the fruit fly Drosophila melanogaster. His team expects to release data on roughly one-third of the D. Meanwhile, Lichtman is working on the zebrafish Danio rerio connectome, as well as analysing a small piece of the human brain — a sample of the medial temporal gyrus obtained from a person who was undergoing brain surgery for epilepsy. That piece is also roughly one cubic millimetre in volume, but to capture the full thickness of the human cortex, the sample is shaped like a slab, rather than a cube.
Denk and his colleagues are mapping portions of the connectome in the zebra finch Taeniopygia guttata , a small bird whose process of song learning can yield insights into human speech. And Kasthuri has a number of projects in progress. To that end, Kasthuri aims to map the visual part of the brain in non-human primates, as well as in an octopus Octopus bimaculoides.
Kasthuri is also working on the full connectomes of young mice and octopuses; comparing these immature connectomes to those of adult animals could offer insights into how the brain learns from experience. Owing to its small size, he hopes to map the young-octopus connectome in about one year. Now that the researchers at the Allen Institute have finished imaging their cubic millimetre of mouse brain, they have passed on the data to Sebastian Seung, a neuroscientist and computer scientist at Princeton University.
Segmentation has long been the rate-limiting step in connectomics. It can take weeks to trace by hand the path of a single neuron through a stack of electron micrographs. But now, artificial intelligence is getting involved. Computers can perform segmentation faster than the human eye, which cuts down the time it takes to trace neurons to a matter of minutes or hours.
People are therefore still needed to check the reconstruction. Seung is tackling this requirement through crowdsourcing and, specifically, an online game called Eyewire, in which players are challenged to correct mistakes in the rough draft of a connectome.
Launched in , Eyewire has , registered users who have collectively put in an effort that is equivalent to 32 people working full time for 7 years, says Amy Robinson Sterling, executive director of Eyewire. The Developing Human Connectome Project is imaging nerve fibres in the brains of newborns. So far, players have been tracing cells in the mouse retina. Sterling and her team are preparing a new version of the game, called Neo, that will be used with the mouse visual-cortex data set.
Many nanoscale connectome-mapping efforts use the program to visualize data. Google has also developed an algorithm for neuron segmentation. A team led by Viren Jain at Google AI, in Mountain View, California, has designed a machine-learning algorithm called a flood-filling network, which builds structures from a point in an image, rather than trying to define the boundaries of all neurons at once.
His team is applying the technique to FlyEM data and has constructed a rough-draft connectome of a whole fly brain that was imaged by another team at Janelia Research Campus. They are also working with data from the labs of Denk and Lichtman.
Jain strikes a more cautious note, and points out that as scientists take on ever larger projects, segmentation algorithms have to become more accurate to keep feasible the amount of human checking that is required. Meanwhile, scientists are honing microscopy techniques to produce sharper, more-detailed images at a much quicker pace, in anticipation of taking on the nanoscale connectomes of large, mammalian brains.
The conventional approach to microscopy in connectomics is a type of electron microscopy known as serial-section electron microscopy.
Researchers embed neural tissue in plastic, and cut it into slices that are a fraction of the thickness of a human hair. They then mount the slices on a specialized tape and feed the result — which looks remarkably similar to film on a reel — through the microscope. The advantage of this method is that the sample is preserved and can be re-imaged, if needed.
But no matter how precisely it is done, cutting the sample inevitably results in distortions that make it difficult to align the images. A newer approach, known as focused ion beam scanning electron microscopy FIB-SEM , uses a beam of charged ions to shave away a thin layer of a tissue sample. The microscope captures an image of the freshly exposed surface, and then the process is repeated.
The FlyEM sample represents the first substantial volume to be imaged by this method. Although it lacks speed, one advantage of FIB-SEM is that the resolution of the images produced is the same in all three dimensions, rather than being coarser along the vertical axis.
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