[ExI] The Most Complete Brain Map Ever Is Here: A Fly's 'Connectome'

[Dave Sill]

https://www.wired.com/story/most-complete-brain-map-ever-is-here-a-flys-connectome/

It took 12 years and at least $40 million to chart a region about 250
micrometers across—about the thickness of two strands of hair.

When asked what’s so special about Drosophila melanogaster, or the common
fruit fly, Gerry Rubin quickly gets on a roll. Rubin has poked and prodded
flies for decades, including as a leader of the effort to sequence their
genome. So permit him to count their merits. They’re expert navigators, for
one, zipping around without crashing into walls. They have great memories,
too, he adds. Deprived of their senses, they can find their way around a
room—much as you, if you were suddenly blindfolded, could probably escape
through whichever door you most recently entered.

“Fruit flies are very skillful,” he appraises. And all that skill, although
contained in a brain the size of a poppy seed, involves some neural
circuitry similar to our own—a product of our distant common ancestor.
That’s why, as director of Janelia Research Campus, part of the Howard
Hughes Medical Institute, he’s spent the last 12 years leading a team
that’s mapping out the fly brain’s physical wiring, down to the very last
neuron.

Janelia researchers announced a major step in that quest on Wednesday,
releasing a wiring diagram of the fly brain that contains 25,000 neurons
and the 20 million connections between them. The so-called “connectome”
corresponds to the fly’s hemibrain, a region that’s about 250 micrometers
across—the size of a dust mite, or the thickness of two strands of hair.
It’s about a third of the total fly brain, and contains many of the
critical regions responsible for memory, navigation and learning.


Rubin hopes wiring diagrams such as this one, showing neurons involved in
navigation, will give researchers a better sense of how brain circuits
work.ILLUSTRATION: FLYEM/JANELIA RESEARCH CAMPUS
Researchers like Rubin believe a physical blueprint of the brain could
become a foundational resource for neuroscientists—doing for brain science
what genome sequences have done for genetics. The argument is that to get
anywhere with understanding brain circuits, you first need to know what the
circuits are, and what kinds of cells they join. That physical schematic
becomes a roadmap for all kinds of inquiries, Rubin says—anything from
understanding the role of the brain’s wiring in psychiatric disorders to
how our brains store memories.

Obviously, it would be nice to pursue those questions with a complete human
connectome. But that’s a long way off. Fully analyzing even the tiniest
amount of brain matter requires an enormous amount of time and treasure.

Hence, the brain of the humble fruit fly, with one millionth the number of
neurons of our own. Drosophila is only the second adult animal to have its
brain circuitry mapped at this level of detail, following the nematode C.
elegans, back in 1986. The task was far more modest. The entire nervous
system spanned 302 neurons and 7000 connections—small enough for
researchers, with enough effort, to get the job done by physically shaving
off layers of cells, printing off images taken with an electron microscope,
and tracing them with colored pencils. The complexity of the fly brain is
two orders of magnitude greater—thus the three decade gap in getting it
done.

“It’s a landmark,” says Clay Reid, a neuroscientist at the Allen Institute
in Seattle who has been working to create a similar map for a cubic
millimeter of mouse brain. For the small community of researchers who have
spent decades building connectomes, the emergence of these first
large-scale datasets feels like vindication, he says. “In the beginning
people thought we were certifiable. And if we weren’t nutty, we were
boring.”

Reid and Rubin’s fellow neuroscientists questioned whether, given the great
number of unknowns about how neurons work, such schematics would be useful.
You might end up with the physical structures, but little insight into the
neural activity that happens there. The rest found the whole enterprise
unfeasible. In 2004, researchers at the Max Planck Institute in Germany had
demonstrated automated methods that could analyze images of neurons
produced by electron microscopes—a process known as segmentation. It was a
vast improvement over tracing neurons by hand. But even then, completing
the whole fly brain connectome would have taken 250 people working for two
decades, Rubin estimates.


Google's algorithms "paint" black-and-white images of neurons to give a
clearer view of where the cells begin and end—a process known as
segmentation.ILLUSTRATION: FLYEM/JANELIA RESEARCH CAMPUS
Rubin was undeterred, betting the technology could be sped up. The team
initially focused on improving methods of gathering the data using
electronic microscopy. To get the complete neuron-by-neuron map they hoped
for, the researchers needed to develop new computational techniques to
produce clearer, denser three-dimensional images. The process involved
slicing the brain into 20 nanometer slabs, and then continuously imaging
them for months in an undisturbed environment. A tiny error in one part of
the imaging might cause rippling effects across the entire connectome
dataset.

But the real bottleneck remained in the process of making sense of those
images—the segmentation problem. A former Janelia lab manager, Viren Jain,
had been working on that very problem at Google, using a machine learning
technique called flood-filling networks. Whereas previous methods had
involved detecting boundaries between neurons and then grouping together
related pixels, the new method combined those steps to fill in neurons one
at a time—“like creating a painting of the image,” Jain says.

To train its machine learning algorithms, Google needed data—images of
neurons filled in by humans—which Janelia could provide. It also needed a
human fact-check. Back at Janelia, after the computers filled in the neuron
images, a team of about 50 proofreaders went over the algorithm’s results,
looking for erroneous shapes and connections. “Computers can’t do all the
work,” Jain says.

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Now that the data is publicly available, it remains to be seen how
researchers will use the schematic. While researchers could previously zero
in on brain circuits of interest and map those neurons, doing so was
costly, says Reid, the Allen Institute researcher. He’s hopeful that a
complete map will help researchers see distant connections that might
otherwise be overlooked. It’s also potentially more efficient. “Before
this, each question required a difficult experiment. But now it’s a
computer query,” he says. “There’s no comparison.”

That’s an exciting prospect for researchers like Karla Kaun, a professor at
Brown University who studies the effect of alcohol and drugs on memory
formation. Having a detailed map of the hemibrain is important, she says,
for understanding the nuances between the circuitry involved in the
extremely long-lasting, intense memories, and more typical long-term
memory. She’d like to see that data married with other methods that can be
used to cheaply compare structures across brains within the same species.
That could potentially show how differences in physical structure
contribute to diseases and behaviors.

A few other connectome projects are grinding forward. Google has partnered
with researchers at Max Planck to analyze circuits in the brains of song
birds involved in learning songs, and with Harvard to study a tiny human
sample. “It’s the opposite of the fly project, just one millionth of a
whole human brain,” says Jain. Still, it will potentially involve a
petabyte of data. Later this year, Reid expects to release an even larger
dataset corresponding to the cubic millimeter of the mouse brain his team
has been imaging, part of a project funded by IARPA.

An advantage shared by independently funded places like Allen Institute and
HHMI is they can make these long-term gambles. “I was sort of the venture
capitalist here, keeping the money flowing for 12 years, making sure no one
kills anyone,” Rubin says. Janelia has spent $40 million on the project,
not including Google’s contribution, for which the cloud computing budget
alone would tally in the millions. Janelia has an ongoing budget of $5
million per year to map the full nervous system of both a male and a female
fly.

Rubin hopes that effort will pay off in time. “I lived through the genome
projects,” he says. “I can remember in the 1980s, when people said all
you’ll get is a string of AGCTs and you won’t know how to interpret the
data.” We still don’t know how to read that sequence, not even close, but
we’re making progress. And the cost of sequencing genomes has come down
significantly in the process. “Everyone who thought the genome project was
a dumb thing now admits it was worth every penny,” he says.

Still, it’s unclear who will pick up the tab on future connectomes,
especially with the most interesting brains orders of magnitude bigger than
the fly’s. Rubin is rooting for a full-fledged effort to map the mouse’s 75
million neurons. The cost is perhaps $500 million, he muses, assuming the
tools speed up by perhaps two or three orders of magnitude. But such was
the case when he started on his own brain mapping journey. “We’ve shown
people that it’s feasible,” he says.
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