It was about 12 years ago
that I first heard of this machine.
It's a brain slicer,
but not just any old brain slicer
that you'd find at Home Depot
or your local serial killer hobby shop.
This is the best brain slicer in the world.
And it lives at Harvard University.
This machine may sound medieval,
but it's at the heart of one of the grand scientific
and computing challenges of our time.
Researchers here want to use it
to help them build a three-dimensional map
of the human brain.
They want to photograph a brain slice by slice,
stitch all the pictures together into a computer model,
and then go find every neuron and every synapse
and the trillions of pathways that connect them.
Scientists call this brain map the connectome,
and some of them are convinced it could unlock the secrets
of how the mind works.
Other people think this whole project
is a colossal waste of money and time.
Luckily for scientists and for you, I have come to Harvard
to settle this matter once and for all.
To the connectome.
Whenever I'm at Harvard,
thinking about the origins of human consciousness
and the mechanics of life, I visit this man.
I'm Jeff Lichtman.
I'm a professor in the molecular
and cellular biology department at Harvard University
and a member of a group called The Center for Brain Science.
Jeff is also
one of the world's leading connectome experts
and runs a laboratory here.
This idea that there is a connectome,
a wiring diagram that is worth knowing,
it remains controversial to this day. To think about a map,
a physical map of the brain's connections
very much the way people in genetics thought of the genome
as a physical map of all the sequence information
for every gene in a body.
Genomics at its early stages,
there was also some controversy over whether
it was really a good idea.
It was also considered a huge data set.
It was about 1.4 gigabytes. Amazing.
1.4 gigabytes, and people were thinking,
"What are we going to do with so much data?"
But the idea here is that the technologies now exist
to get wiring diagrams down to the level
of every single connection of every single nerve cell
to every other nerve cell.
You're basically talking about finding every neuron,
every synapse, all these connections between them.
And so we're talking about on the order
of trillions and trillions of wires.
Oh yeah. Yeah.
It's almost inconceivable.
To get a wiring diagram of the brain,
you'd need enough resolution to see every single synapse.
And that resolution is lower
than the resolution of light microscopes.
It's controversial.
What's the great pro case for why we want to do this?
You see the United States has put tons of money
in trying to do a connectome.
Europe has put tons of money,
so obviously there's a group of people
that believe this is a big deal.
What is the big hope?
Maybe the one that is most generally interesting
is that there are a number of disorders of brains
where we still have a very poor idea
of what actually is wrong.
And a number of people believe
that the reason these diseases are so difficult
is that they go to the structure of the brain.
And we have really no idea what those pathologies are.
Diseases that are not uncommon, autism spectrum disorder,
schizophrenia, Tourette's syndrome.
I don't think we're going to get very far
until we have some physical evidence of what's wrong.
Memory in its true sense,
in the sense of a neuroscientist thinking about it
or the way a normal person thinks about what they remember,
where is that? What is that?
What does it look like? How is it configured in the brain?
It's almost certainly in the wiring diagram,
but until we start getting wiring diagrams,
we're not going to have a good idea what they are.
A brain schematic
sounds like an easy enough thing to build,
but in the decade plus that I've been following
connectome projects around the globe,
progress has proven tough to come by.
Researchers have managed to build models of tiny worm brains
and slightly larger fly brains.
The goal these days is to image an entire mouse brain.
And then one day far in the future, a human brain.
It turns out that our brains are so complex
that they require an almost unprecedented amount
of computing to analyze.
You guys recently did a cerebral cortex in a human.
But a cubic millimeter.
A cubic millimeter of that.
But that's how hard this is, right?
How long has it, how long have you--
That took several years. Several years.
And two teams, a big team at Google
and a big team in my lab working full-time on this.
The problem here is that the wires are not ordered.
They're not all running like train tracks
in the same direction.
They're running in three-dimensional space
more the way spaghetti in a bowl would run.
One way of generating a map of every wire in every direction
is to cut that bowl of spaghetti, piece of brain,
into very thin slices, take each of those pictures,
and then align them, and then find piece of spaghetti,
and track it through the volume,
and then start with another piece of spaghetti
and track it through the volume
and keep doing that until you have all the spaghetti.
These are 30 nanometer sections typically,
which is about a thousandth the thickness of a human hair.
Each section.
The first step in building a connectome
is to get your hands on some brains and then cut them up.
It all sounds very grim,
but Harvard turns out to be quite efficient
with its brain harvesting.
Jeff's lab can do a year's worth of research
with a single mouse brain.
Small chunks of the brain are fed into the slicer
where a diamond blade carves off a thin sliver of tissue.
The tissue floats along some liquid
and onto a piece of tape.
The brain slices are then put into this very large machine
where they're photographed in the finest of detail.
Software helps stitch the pictures back together
into a single 3D image
and algorithms are applied to help make sense
of what's going on inside of the brain.
This summer, Lichtman's lab along with Google
announced their major breakthrough
on the human connectome front.
They created a 1.4 petabyte visual model
from one cubic millimeter of human cortex.
Within that tiny sample were millions of neurons
and 130 million synapses.
All of these parts were then labeled by an obedient
and hardworking AI algorithm.
Whose brain sample is it?
Was there something--
Oh, it's a woman who had had epilepsy.
And she had surgery or something.
She had surgery, and to remove the epileptic focus,
the neurosurgeons made an incision in the temporal lobe
to get access to the disease site
so they could extirpate or in some way remove
the part of the brain that was causing the epilepsy.
If we zoom up,
every one of these objects
is now a uniquely identified object in the data.
There's hundreds of millions of objects in here.
If I take an object like this
that has a sort of triangular shape
that looks like a pyramidal neuron,
and I just double click on it.
There it is.
It almost looks like a cartoon, but that's the actual--
This is a real cell.
And if I do that for this cell, there is it.
And if I do it for this cell,
I'm going to have to unzoom a little bit
to give you a sense of this.
You're making the bowl of spaghetti.
And everything in there is traceable.
That's a place where a cell receives a synapse.
And then I'm going to zoom up on that spot
in the electron micrograph.
Here's an axon making a synapse on that dendritic spine.
And I can do that for every one of these.
Let's look at this.
This is something I saw with my own eyes
and then asked my colleagues
if they'd ever seen anything like this,
and no one had.
So here is a axon.
That's something that's talking, where the dendrites
of the cell are in red.
That's the cell that's listening.
And this axon comes to this position on the cell
and makes something that looks sort of like a turbine.
It's a massive wiring weirdness.
If you follow this axon, it keeps doing this.
Here's the same axon, another whirl.
And that's the thing about large data.
These things that are called unicorns,
these weird things that there's one of,
when you have 1.4 petabytes of data,
you can discover that they're rare,
but they're not one-offs.
Do we have any speculation about why this happens?
One person in my lab, Richard Schaleck,
suggested that maybe this is a magnetic coil,
that the electricity travels here
and it generates a magnetic charge.
I think that's a crazy idea, but he took offense.
He said, "You got a better idea?"
Not everyone at Harvard
is a full-on connectome believer.
So, for another view on the matter,
may I please present to you the bad boy of brain science,
Florian Engert.
This man knows how to make an entrance,
Ashlee! Hello.
Good to see you.
How are you? I'm well.
Good.
It's a lovely day. It is a nice day.
Is that how you usually cruise around?
That's how I commute.
Sitting here slotted between two rhinos,
I got right into it and asked this man
in a purple muscle tee what he makes of brain maps.
There's controversies around the connectome
to some degree of some people,
there's people who think it will be useful
in different ways, so there's some debates there.
Then there's a camp of people who think
this is maybe not a waste of time,
but it's not going to lead, yeah, so where do you fall?
Is the connectome a worthwhile pursuit?
Well, it's not going to be sufficient.
Maybe that's the most important statement.
Just having the connectome by itself
will tell you basically nothing.
I think it is extremely useful,
once you have a model, a theory of how it works, the brain,
and you want to use the connectome to falsify
or test or constrain your model.
Florian's lab specializes
in researching the brains of zebra fish.
And he has a glorious collection
of tens of thousands of them.
These fish turn out to be ideal for brain research.
Just after they're born, they have translucent skin.
This means that scientists can peer directly
into the animals' minds and watch their neurons fire.
Florian and his team of over-educated fish farmers
have altered the genes of these animals
so that their neuronal activity
really lights up under a microscope.
We have maybe a few thousand
different transgenic fish lines,
the different populations of fish that we need
to monitor and manipulate neural activity
in individual parts of the brain.
And that's what you see here.
And so each case would maybe have a different property?
You need more than one row.
So from here to here,
they would all express a fluorescent indicator
in a very specific part of the brain.
And other fish would express in different areas of the brain
or a different indicator.
Who keeps track of what happens?
You just have them all numbered and somebody's--
Oh no, we have a bar coding system.
There's a whole database that needs to get updated.
And we have a staff of three people and a head fish mistress
who takes care of the whole facility.
And she keeps track of maintenance and housekeeping.
So when you're looking at this brain activity,
it's the first couple days of their life.
It's day five till day eight.
Day five or day six with zebra fish has a whole repertoire
of behaviors that they need to survive.
That's what they do, they interact with conspecifics.
They hunt, they escape competitors and they navigate.
Florian was too important
to explain how the actual microscoping part
of all this works.
And so poor post-doc Gregor stepped in to help.
I can get a fish in a dish
and then carry it into the microscope
and then kind of set up an experiment.
First Gregor embeds the fish
in the laboratory equivalent of jello.
Then he strips away bits of the jello
to let the zebra fish move its body and tail.
The idea is to see how the brain works
in a living animal going about its day-to-day activities.
And so you can see that when I tap the dish,
and it'll do this little tail flick.
To see the neurons, you would use a different microscope,
more high powered machine?
Yep. Okay.
So you do this and then you take it and then image deeper.
Exactly. Okay.
The scientists often use light patterns to trick the fish
into thinking that they're swimming in a stream
or running into an obstacle.
It's like fish VR.
Because the dots are changing which way they go,
so the fish is going to be kind of changing its pattern.
Yeah, exactly.
Fish are gonna swim left, right.
This is brain activity that you see.
All these purple and orange dots,
these are individual cell bodies.
And whenever they flash,
that means these cells become active.
In a way, Florian's fish study is the opposite
of the connectome approach.
Rather than trying to untangle the whole bowl of spaghetti,
his lab can zero in on the specific spots in the brain
that light up in response to stimuli,
and then start to develop a theory
of what fishy thoughts look like.
Can you give me an example along those lines?
A zebra fish does X,
and I could see that in the brain
and find out whether that's right or wrong.
For example, one of the things we are studying
is working memory.
So the fish sees a pulse of motion.
And it remembers that for a few seconds.
And now we've identified a brain region
where this information, this activity is being stored
in a recurrent circuit, so we can see by imaging,
by recording activity
that there's information in this ventral hindbrain circuit
of the fish where this information stays.
So we have sort of have found now a nucleus
for working memory where this information
is being stored transiently.
I think the critical part is you need to know already
how it works, and then you can use it to test.
Many of my friends and colleagues,
they won't like what I say now.
So if you don't know how it works,
then having the connectome is going to hurt you
more than it's going to help.
It's going to be much more confusing than illuminating.
Should we be spending money
to even try to do a human connectome just because we can?
Well, no.
I think we should absolutely.
It's about expectation management.
We should not do the human connectome,
the same we shouldn't have done the genome
for the purpose of using it to directly cure disease
or help the human condition
because that's not going to work.
But what it's useful for
is to help the existing science infrastructure
to constrain the models.
It's another additional tool that will help you
with incremental insight.
Most of the wealthiest people in the world these days
are tech people and they're interested in stuff.
It seemed like, well, we can do this
and we can do it a lot faster
if we just had 50 high-powered microscopes
and more brains slices.
I'm surprised no really wealthy person--
It's interesting where the brain stacks up
versus the body on some of the priorities of research.
It's not clear what you would do with it.
I think maybe that's the problem.
If you had a billionaire
and say, "I can give you a connectome."
Her question would be,
"What am I going to do with that?"
What you will you do once you have the connectome?
And I think one of the most prevalent applications,
which I think is batshit crazy is to upload.
Oh, to upload your mind?
Whatever connectome that that is,
the substrate of your mind,
that you can then actually have uploaded into a computer
and emulate your mind.
There's a lot of rich people who are after that,
I think an uploading of my brain
it's more like making a backup copy, right,
that is disjointed from my own identity.
There's really a philosophical flaw,
a wrong way of thinking,
that a copy is the same as a continuation
of my own life.
So that's why I think all of this is complete nonsense.
This idea of the connectome is a tool to upload my mind
into a computer and achieve immortality is crazy.
After spending a couple of days
talking about brains, I took a slow walk around Harvard
to feel what Ivy League life is like.
As I sauntered majestically,
I thought about Mark Zuckerberg building a dating site here,
and then ending up as our digital emperor.
I thought about how weird it is that we're totally dependent
on Google's huge computers and algorithms
to do so much cutting edge research.
And I thought about my director, David,
running with a rollerblading brain scientist.
I thought about a lot of things, and well,
I reckon it would be nice if we knew how that happens.