gigantic and faraway planet with 100 billion cities and hundreds of trillions
of routes connecting them. Imagine too that this vast planet can be viewed only
a few square feet at a time, and only with the most sensitive and expensive
Making a map
of that planet with all its routes and cities would seem a near-impossible task.
Yet it would probably be a walk in the park, compared to the stupendous
challenge of mapping the human brain’s trillions of connections—mapping that
must be done in three dimensions, at scales too tiny for ordinary microscopes,
and in fragile, wet tissue.
brain-mapping research, also known as connectomics, is currently one of the
hottest fields in neuroscience, indeed in all science. Funding for connectomics-related
efforts such as the EU’s Human Brain
Project and the US-supported Human
Connectome Project and BRAIN
Initiative amounts to well over $100 million annually.
Much of this
research has been aimed at gathering relatively low-resolution structural and
functional data on the brain, such as from magnetic resonance imaging. But
arguably the most exciting connectomics research now is aimed at producing very
high-resolution maps (“reconstructions”) of brain tissue—maps that are detailed
enough to reveal the actual synapses, or connection-points between neurons.
Lessons from a “saturated
a Harvard professor and member of
the Dana Alliance for Brain Initiatives, runs one of the leading high-resolution
connectomics laboratories. In a landmark paper in Cell on July 30, he
colleagues described an automated method for “probing the structure of neural
tissue at nanometer resolution.”
nanometer scale, photons of visible light heave about like sea waves—they are
far too large and bendy to be useful in imaging. So traditional light
microscopy is out, and researchers instead must rely on electron microscopy
(EM), which images targets by peppering them with tiny subatomic particles—electrons.
EM is much
more expensive and difficult to use than light microscopy, so connectomics
researchers would stick with the latter, and its lower resolution and wider
imaging field, if they could. In fact, connectomics as a whole has relied heavily
on light-resolution imagery up to now, believing that it is good enough.
neocortex, for example, there have been very few EM reconstructions so far,”
microscopy works down to a resolution of a few hundred nanometers, corresponding
to the shortest wavelengths of visible light. That is sufficient to image the
structures of neuronal cell bodies, their output stalks (axons), and their
input branches (dendrites). It cannot resolve the fine details that indicate
actual synapses, but many scientists have believed, with some experimental
support, that synapses’ locations essentially
can be inferred—to a degree sufficient for understanding and modeling the brain—wherever
axons and dendrites are seen in close proximity.
studies in recent years have provided evidence that imaging axon/dendrite
crossings with light microscopy is not
enough to infer true connectivity—in other words, that axons tend to seek out
specific partner dendrites; they don’t just hook up with those that happen to
be nearby. One such study
determined this from EM images of
the mouse retina; another did
it using EM images of the mouse
hippocampus, an important memory region.
team, including first author Narayanan
Kasthuri, then a postdoc in the Lichtman lab,
focused its study on the mouse cortex, the evolutionarily most advanced part of
the brain, and in humans also the largest part. The researchers found enough of
the same kind of evidence, in their words, to “refute the idea that physical
proximity is sufficient to predict synaptic connectivity.”
to discovering that axons don’t simply make synapses with dendrites at a
predictable percentage of physical crossings, they found that individual axons commonly
make multiple synapses with a given dendrite—which probably helps explain why some
pairs of neurons seem more strongly connected than others, in terms of signal
Axon (blue) making multiple synapses with the same dendrite (green). Credit: Lichtman
Lab / Harvard University
that it’s simply not true that the vast majority of connections between neurons
are formed by singleton synapses,” said Christof Koch, president and chief scientist at
the Allen Institute for Brain Science, which is heavily involved in
connectomics research. “The sophisticated simulations of brain activity that we
and others are working on are going to have to take this into account.”
most impressive outcomes of Lichtman’s six-year study were the technical
advances. To an unprecedented degree, Lichtman’s team automated the process of
slicing, with a diamond knife, extremely thin (29-nanometer) wafers of mouse
cortex, and gently guiding them with a tiny, sticky conveyor belt to the EM
chamber, for two-dimensional imaging (at three by three nanometers per pixel).
They also modified
existing pattern-recognition software to automatically “annotate” the thousands
of slice-images by recognizing and labeling every object of significance in the
images. These objects included not only the larger structures such as neuronal
bodies, astrocytes and other support cells, neuronal axons, and dendrites, but
also the tiny structures on axons and dendrites where synapses form. The
software even identified and labeled synapse-related objects inside axons and dendrites, such as
of energy-producing mitochondria, and the vesicles that carry neurotransmitter
Neurons and their dendrites traced back from imaged volume (pink arrow). Credit: Lichtman Lab / Harvard University
had to include the “stitching” together of objects from adjacent slice-images,
and the tracing of synapses to their host neurons, to form a true
three-dimensional picture. All the stitching and tracing, and analysis of
axon/dendrite crossings, took up most of the time spent on the project, largely
because the pattern-recognition algorithms, advanced as they were, just
couldn’t do the job accurately without some human editorial help. This aspect
of connectomics projects is “currently the main rate-limiting step,” Kasthuri
Neurons and their processes, color-coded to distinguish one from another. Credit: Lichtman Lab / Harvard University
The scale of the problem
end of that project, Lichtman’s lab has improved further the automation of imaging
and analysis, and now, Lichtman says, could reconstruct the same-sized volume
of brain tissue (0.0000015 cubic millimeters, or roughly one cell-width across)
in a matter of months, not years.
Still, that is
only a very tiny volume, on the order of a billionth of a whole mouse brain—or less
than a trillionth of a human brain.
of the connectomics challenge is also apparent when one looks at the data storage
requirements. The annotated imagery generated in Lichtman’s project required
about 3 trillion bytes (terabytes) of storage. According to Kasthuri, mapping a
full human brain connectome would require more than 1021 bytes (one zettabyte), which
represents a large
fraction of the total storage capacity on Earth at present.
research clearly is not for the faint of heart.
In fact, far
from being worried, researchers in the field seem convinced that the required
technologies will keep improving, obstacles will be overcome, and
connectome-mapping projects will run ever-faster.
lot of people working on the problem,” Kasthuri said. “The algorithms are
getting close to as good as people can do.”
of the art is always moving,” said Lichtman.
more the challenge is shifting from the acquiring of the data to the analyzing
of the data—developing the machine-learning techniques to align, to stitch, and
to annotate,” said Koch.
At the Allen
Brain Institute, a connectomics team led by Koch’s colleague Clay Reid is trying to do essentially what
Lichtman’s team just did, but on a much larger scale—a dense reconstruction of
a full cubic millimeter of human cortex.
“In terms of
taking all the image data and annotating it, we’re still about a factor of a
thousand away,” Koch said. “But there’s good reason to hope that advances in
machine vision and machine learning—and we have an ongoing collaboration with
Google on this—will improve these algorithms so that over the next five years,
we can speed all this up and acquire a complete cubic millimeter.”
noted that his lab also is involved in multiple projects aimed at
reconstructing brain volumes of different animals, on scales up to a cubic millimeter.
“One of my colleagues is even beginning an effort to do a whole brain of a
mouse,” he said.
Koch, like other connectomicists, have been particularly emboldened by the success
of a previous grand project in biology, sequencing the human genome.
Researchers in that field struggled for years with very slow, labor-intensive
and expensive DNA-sequencing methods, but eventually made those methods many
times faster and cheaper.
“Now you can
drop a tissue sample in the lab and a day later have the complete genome,” said
Koch. “I suggest that in another ten years, you’ll be able to put a cubic
millimeter of brain tissue in a box, and an hour later get a beautifully
reconstructed three-dimensional circuit diagram.”
A promissory note
A full connectome
of a significant portion of a mouse brain would undoubtedly be a gold mine of
new neuroscientific knowledge. Even Lichtman’s project, limited to a tiny brain
volume, yielded unexpected, intriguing findings—such as the observation that energy-producing
mitochondria, visible in axons in close proximity to synapses, were for some
reason rare in the parts of dendrites (dendritic spines) where synapses occur.
connectomes from human brains could also, in principle, clarify the mechanisms
of major diseases—particularly developmental diseases such as autism and
schizophrenia where connection problems are strongly suspected. One could
compare the connectome of a diseased adult brain to that of a healthy adult
brain, and perhaps also to a connectome of a still-developing child brain, and
see precisely how they differ. “I think that’s the kind of investigation that
would allow us to make headway in understanding illnesses that we call
connectopathies,” said Kasthuri.
however, connectomics has not progressed far enough to generate findings with
direct clinical relevance, much less to enable the advanced brain-function
models that would revolutionize both neuroscience and artificial intelligence.
“It’s still very much a promissory note,” said Koch.
Beyond the connectome
As likely as
it seems that connectomics tech will eventually become fast and cheap enough to
deliver a whole human-brain connectome—computer storage capacity having
expanded accordingly—just having that connection map per se won’t tell scientists how the brain works.
Researchers have known for a while, for example, the 302-neuron
connectome of the worm C. elegans—an
animal frequently used for molecular biology and genetics studies—but as Koch
noted: “We still don’t have a good model for how those 302 neurons work
together to produce behavior, so that shows you that a connectome may be
necessary but by itself won’t be sufficient.”
will be needed? First, functional mapping data showing broadly where and how neurons
tend to work together—and many connectomics projects are already concerned with
gathering these functional data.
also will need, at least, more detail on brain cells, particularly neurons. In
their recent study, Lichtman’s team labeled neurons in the reconstructed volume
merely as “excitatory” or “inhibitory.” But as the Allen Brain Institute and
others have shown, there are throughout
the human brain
hundreds if not thousands of different kinds of neurons, distinguished by their
surface receptors, neurotransmitters, other secreted molecules, electrical
characteristics, gene expression patterns, and so on.
those data are included in the connectomes yet,” said Lichtman. “The challenge
is going to be to superimpose it, so that we know not only the connectivity of
nerve cells but also their [molecular] identities.”
don’t have methods for making such detailed molecular characterizations of
neurons, or other cells, all at once—they typically have to apply stains or
fluorescent beacons one at a time to reveal a given cell marker. “It’s not that
I’ll want to know where the cholinergic cells are, for example,” said Lichtman.
“I’ll want to know, in one dataset, where every
type of cell is. And that will be hard—it will require another kind of industrialization,
this time for molecular labeling. But it will happen.”
Connectomics and Immortality
excitement over connectomics has led some to suggest that it might ultimately
offer humans a path to practical immortality.
One idea would
be to map a person’s connectome after death, and later simulate it on a
computer. This, it is supposed, would enable a person’s “mind” to be brought
back to life, and it could then be left disembodied, like an app on a
smartphone, or perhaps put in control of a robot.
approach seems highly unlikely to succeed as intended. Even if a connectome did
encode a dead individual’s personality, “uploading” it to a new medium would
merely create a copy, instead of transferring the original.
out that a computer-simulated mind based on an uploaded connectome also
probably wouldn’t be conscious.
“A lot of
people seem to take for granted that if you could simulate my brain, for
example, by knowing its connectome, you would get consciousness too—you would
get me, including all my feelings,” he
shouldn’t take that for granted, he emphasized, because there is no
experimental or even halfway serious theoretical evidence for it. Indeed, the
most persuasive theory of
consciousness implies that the
architecture, the integrated activity, the bodily context, and the causal power
of a brain add up to consciousness in a way that a computer simulation could never
astronomer can simulate a black hole on a supercomputer,” Koch said, “but that
simulation won’t warp space time around the supercomputer. Similarly with a
brain simulation based on the connectome—if the computer doesn’t have the
causal power of the brain, you’re not going to get consciousness.”
knowing a person’s connectome would facilitate repairs, for example, if their
brain has been frozen in hopes of a future, high-tech resuscitation. Current
frozen-storage techniques—in the commercial/technological realm known as cryonics—are
thought to cause heavy damage to brains, presumably including damage to their
connectomes. The catch, for now, is that there is no way to map the connectome
of a brain without completely destroying that brain.