Translator: Sara, English-Persian (Farsi) Reviewer: Denise RQ
I really, really like complicated things.
I love complicated flavors,
like putting avocados on coffee ice cream,
It's a little bit bizarre, but actually kind of good.
I also really like complicated music,
so give me Mahler over Mozart any day.
I love complicated plots.
Sit me down with a good Thomas Pynchon novel.
In fact, nerd alert, I actually have a computer named Pynchon.
But, we are all actually part of multiple complicated things.
And the complicated thing
that I like to think about the most is networks.
So we are all part of multiple, ever changing networks.
For example a network of colliding particles in the Universe,
a network of interacting flows in the Earth's atmosphere,
a network of highways and city streets on the Earth's surface,
and a network of family, friends, coworkers, and neighbors.
But probably the most complicated network that you engage with in your everyday life
is the network inside of your head: your brain
what makes you who you are.
So how your brain is a network, and what is a network to begin with?
Well, a network is a way
of looking at a system composed of two different kinds of parts.
The nodes of a network are the sub-parts of the system,
and then the edges of a network are the interactions between those sub-parts.
So what we do as network scientists
is that we take a system that's pretty complicated,
and we separate it out
into these nodes, the little bits, and the edges, the interactions,
put them together, and try to understand
how that combination creates a structure that enables a system to function.
So we take these representations, and we try to understand
where are the parts of the system
that are connected up to a lot of other parts.
Those are hubs of the system,
and are often really important in the functioning of the system.
We also ask, where are there a bunch of nodes
that are densely interconnected with one another.
Those are called modules or communities,
and they can often perform a single function themselves.
We can also identify lonely nodes that are not very well connected.
They're actually called peripheral regions,
and they tend to have very peripheral functions
inside of the network as well.
So, if this is what a network is, then how is your brain a network?
Well, first we have to think about what are the network nodes?
If I say your brain is a network you might be a little confused
because the brain is actually a thin piece of tissue, right?
It's an organ, it's a volume.
It's not composed of little pieces, at least by eye.
But actually, if you look at the surface of the brain,
it's composed of these peaks and valleys.
And the peaks and valleys are called sulci and gyri,
and they help to define particular parts of the brain
that have different kinds of neurons and different distributions of neurons,
and therefore, can have very specific functions.
So what we can actually do is we can use the surface morphology of the brain
to identify independent pieces that may perform different functions.
Then the question is, if those are the nodes in our network
how do those pieces interact?
What are the connections
that underlie the passage of information between those areas?
To answer that question what we have done
is used a new technique called diffusion imaging.
So, diffusion imaging is something that you can take with an MRI machine,
and it actually watches water molecules bounce around in your brain.
You might have not known
water molecules are constantly bouncing around in your brain,
but they actually are.
And what they do
is that they bounce against walls, they bounce up against these walls.
The walls are actually big bundles of neuronal axons.
So by watching where the water molecules bounce,
you can infer where the bundles of neuronal axons are.
Those neuronal axons are actually the highways
along which information can propagate in your brain.
So these are, in fact, edges to some degree.
These are interactions, these are pathways.
So when we initially saw this kind of data it looked really messy and really tangled,
and we were wondering,
"How could we actually understand this structure,
and how can we compare it between different individuals?"
Network science actually provides us with a way of quantifying that
in a really beautiful summary statistic.
So we were able to quantify individual nodes
that have specific functions,
and then map their interactions using diffusion imaging.
So then the question is,
is network science something that is actually intuitive to us
and can we learn something new about the brain using it?
The answer is yes.
So when I talk about networks, it might actually bring you back
to grade school or maybe preschool, actually,
where you played with toys that linked up together.
Here's an image of tinker toys.
So, tinker toys are created from disks and rods,
and it's actually the combination of these rods and these disks
that make a network.
So if you watch a little kid play with one of these,
what happens is that they put the little disks and the rods together,
and they build up a beautiful structure - a sculpture, to them -
that is a network sculpture.
But then they don't stop there, right?
They actually usually take a piece off and maybe put it somewhere else,
or maybe they completely disassemble the structure, and then reconfigure it.
And then maybe they disassemble it again, and reconfigure it again, right?
So actually what they're making
is many different networks out of the same underlying components.
And, in fact, that's exactly what your brain does as well.
So your brain takes the pathways that exist in there,
and uses different pieces of it in different network configurations
to enable you to perform the functions that you perform in everyday life.
So every task that you perform, every thought that you think,
every thing that you do,
requires a different set of patterns, a different set of connections,
and that's how your brain actually distinguishes
between the different tasks that you have to perform.
What's interesting about this
is that this is actually different between each of us,
each of us has a different kind of reconfiguration pattern.
And how did we discover that?
Well, we discovered that in the context of learning.
Learning is a scenario where we have to actually change our brains
in order to change our behavior.
So traditionally, if I was to say that I wanted to understand learning,
what I would probably do is take two portraits, two still photographs.
I'd take a picture of your brain before you learned,
and then I'd let you learn,
and then I'd take a picture of your brain after you learned.
And then I'd compare the two pictures and say, "Are they different?"
If they are different, great.
That mean that your brain actually did change and you were able to learn.
But that never really felt very fulfilling to me.
What I really wanted to know
was not just whether or not the brain changed,
I wanted to know how it changed.
So instead of two still photographs,
I want to be able to have a series of snapshots
that follow the brain as it reconfigures.
So can we do that? The answer is yes.
What we do is that we use MRI scanning techniques.
We put healthy individuals inside of the MRI scanner,
and have them learn new skills.
The skill that we love to use is actually a set of finger sequences.
So, they see a pseudo-musical staff, similar to a guitar hero,
and then it tells them which buttons to press,
and they press that series of buttons, similar to playing piano arpeggios.
And as they're doing this, we're imaging their brain
and watching exactly which patterns occur, and how they transition over time.
So, what this gives us instead of two still photographs,
is a series of snapshots that quantify that reconfiguration.
But again, it really wasn't quite what I wanted.
Because when you look at this kind of data it looks a little bit like a documentary
or, a slide show, similar to the one I'm giving now.
What I really wanted was to have a video,
a way of seeing the smooth transition in the brain as it reconfigured.
Can we do that? Well, yes.
We use a mathematical trick,
that is, actually, to link up the networks in individual time slices.
We take two minutes of your learning and link it up to the next two minutes,
and the next two minutes, and the next two minutes.
In network speak, this is actually using a linked adjacency tensor,
which quantifies, mathematically,
the object that is a smooth video of your reconfiguration over time.
So, this critical change in an underlying object
enabled us to see how people were adapting and changing their behavior.
What did we learn?
Well, we learned that there are two parts of the brain that are critically important
in these kinds of motor skills.
Number one is the motor cortex, highlighted in red,
which is what's necessary for actually moving your fingers.
Secondly, the visual cortex, which is highlighted in yellow,
is important for actually processing the guitar hero pseudo-musical staff.
And what happens is that initially during learning,
these to parts of brain are densely interconnected.
Why? Well, because they have to share information with one another,
and they have to communicate with one another.
They have to take in the visual information,
and then process it into a response in our fingers.
So they have to communicate.
But as time goes on, and as people learn, we found a surprising fact:
that these two pieces of the brain stop communicating almost altogether.
They become almost autonomous from one another.
So, why is that?
Well, think about when you played an instrument or when you learned a sport,
or any type of motor skill that you have learned in your life.
Even walking, for example.
That's something that you have learned so well that you no longer think about it.
It's put down into motor memory,
and you can just do it automatically without thinking.
That's exactly what happens in learning these finger sequences.
And we can actually see that in your brain:
the two pieces of brain stop communicating with one another
and are able to just respond automatically.
But then my question was, if this is true,
and this reconfiguration process is happening continually
as we learn throughout our everyday lives,
could I actually determine
who was good at learning and who was not so good at learning?
Could I predict from your brain data whether or not you'd be a good learner?
And the answer is yes.
And this was really surprising to us.
So the people who are actually good learners are the people
whose brains reconfigure really swiftly.
And the people who are not particularly good learners,
are people who have really rigid brains,
and the functional connections between different brain areas
really don't change very much during the time period of the experiments.
This fascinated me, because I started asking the question about myself:
is there a way for me to change the flexibility in my brain
so that I can learn better?
Maybe if I had a little bit of coffee this morning,
or maybe if I slept better last night, would I be able to be more flexible?
That's an open question, and the one I care about,
but I think the implications for this finding is actually more broad.
So, let's imagine rehabilitation after a stroke, for example.
Would there be a way for us to determine
who and when to train based on their brain network flexibility?
Or let's think about interventions,
therapeutic interventions or stimulation interventions.
Is there a way for us to determine where in the brain we should stimulate
to enhance flexibility and therefore, enable enable learning and rehabilitation?
Then, let's get a little crazy and think ten years down the road.
In a classroom environment, would I be able to determine
which classroom environment is ideal based on the amount of flexibility
that classroom environment engenders in little developing brains in children?
So, would I be able to say this classroom environment
is really excellent because it engenders flexibility in brains
and this classroom environment is not as strong
because it does not engender flexibility in people's brains?
These are areas we're extremely excited to follow up on in the future.
But I actually have a more pressing problem today.
And that is, understanding and quantifying the structure
in these reconfiguring networks.
Most of the illustration that I've shown you today are just that,
illustrations of the data that we see; they aren't the data exactly.
And the reason that is, is because this data is really complicated.
It's very hard to understand the structure inside of it; it really is big data.
Traditionally, if I wanted to understand this,
I might turn to computer science or machine learning
to extract structure from that data.
But we've actually decided to take an alternative route,
and that is to speak with people
who are trained to identify structure in messy data, and those are artists.
So, we are actually formalizing collaboration with professional,
undergraduate, and other local artists in our community, to help us to understand
the networks that we are studying, and the data that we're trying to analyze.
Here are a few examples
of some of the pieces that have been created over the past year.
These are examples that quantify network organization
that is being addressed and trying to be understood over the entire Penn campus.
And what we've seen by this collaboration with the artists
is that we begin to identify new areas of research development.
So here's a piece that I really wanted to show you.
And that is a piece by a fashion designer, Katherine Khorassani.
What she did is that she quantified the reconfiguration of the brain networks
during epileptic seizures.
So what she did is she used Verizon telephone wire
to illustrate the communication patterns in the brain, and then she knitted them
so that you could see the undulating pattern of brain rhythms.
Following that, she sculpted this circular architecture
to illustrate the evolution of the network seizure.
What we were really inspired by, is to quantify the volume, or the energy,
or the burstiness of these networks, in new ways, mathematically speaking.
So this artistic representation directly informed new hypotheses in our group.
But we don't actually stop there.
We take these pieces into local classrooms,
to encourage children into interaction between the arts and the sciences,
and to illustrate the concepts of networks in their everyday lives.
And even in this scenario,
we've actually been able to take away new ideas for our research.
I like to highlight this one,
where an eighth grade student actually shows the structure
in his brain network on the black cardboard.
And then behind that, he shows many, many colorful squiggly lines,
which illustrate the chaos or the noise that he feels in his brain.
So, one of my questions is, does noise actually impact on flexibility?
Does it enhance flexibility or decrement flexibility?
And how can we empirically test that relationship?
Looking to what lies ahead; I think that what lies ahead
is the ability to tune our brain networks to optimize our cognitive processes.
And for me as a scientist, I'm curious, as I collaborate with artists,
is my brain becoming more flexible?
well, I don't know the answer, but it sure is an exciting adventure in the meantime.
Thank you.
(Applause)