So this is Paul Verschure with Olof Sporns. And he's part of our Olof's Also
Wanted speakers at our BCBT summer school.
And Olof has been introducing this notion of, if you want, the connectome of the human brain.
So Olof, maybe you can say something about what this actually means.
Well, it needs the set of structural connections between regions or individual
neurons that exist in the human brain.
Connectome is a structural description of the connectivity of the network that
makes up the human brain.
Now, I mentioned individual neurons, and that's going to be challenging to get to.
All right, so all of you were at the Connectome and were telling me what it means.
It's a description of the network of the human brain.
And network means it's got nodes and connections.
The nodes could be as small as individual neurons, or they could be a little
larger and could be regions of the brain that we can distinguish from each other.
And when we proposed that for the first time years ago, really it was motivated
because we didn't have such a description for the human brain at all.
And it seemed to me that we needed one, because everybody out there is doing
functional brain recordings and dynamic measurements of brain activity,
but we don't really know, in my view, where all this is coming from and how
it's actually generated unless
we have an understanding of what the connectivity looks like underneath.
And it was a challenging goal to have because, you know, we don't have really
good methods yet to map the connectome, certainly not at the level of individual neurons.
That's really challenging because we've got so many of them.
And we can't do it in humans in a non-invasive fashion.
What we can do now is we can do diffusion imaging,
which allows us to infer connectivity in live human beings without invasively
going in there and destroying So that's good,
and people are starting to do this, and it's beginning to give us a picture
of what this network might look like.
But in some sense, you could argue, well, this idea that connectivity is important
to explained function is with us for some time.
And also, anatomists have developed many different methods to do this.
Maybe not at this massive scale. Absolutely. So….
I don't think it's a new idea in the sense, you know, I don't want to claim
that, you know, this is the first time people have proposed connectivity is important.
No, no. I mean, it goes back all the way to certainly Ramon y Cajal and perhaps earlier than that.
And he, of course, was perhaps the greatest anatomist of all time and had these
intuitive insights that are
really fascinating to look at even now about how the brain was a network,
even though he didn't – I'm not sure he used the word network.
In fact, the opposing theory was a particular theory by Golgi,
and he, in some sense, was more of a networker.
Nevertheless, I mean, he – and also to some extent, Golgi gave us exquisite
descriptions of how things were connected. And so that notion has very,
very deep roots in the history of neuroscience.
So I'm not saying that that was the first idea about it, but it is the case
that until about a few years ago, we had a very sketchy understanding of,
especially in the human brain, how things were connected.
We had a much better understanding in primates, in non-human primates,
in macaques and a few other species where people had done invasive anatomical studies with tracers.
And so we had some picture of how that actually hung together as a network.
And that was very informative, certainly for me and also, I think,
for others to study those network diagrams.
And so we felt like we wanted to push for, you know, doing something similar for the human brain.
And really, it's been proposed before. I mean, when you go back in literature,
you find people saying this, you know, going back certainly 10, 15, 20 years.
So there's no claim here this is in any way original. In fact,
the only original part of it is, in some sense, that we were aiming to derive
this description and then make it a basis for understanding in a dynamic computational sense,
how do we get from the structural network to the dynamic, the changing dynamic
picture of activations, coactivations, all that rich repertoire of dynamic states
that we have available to us?
How do we get from this anatomical network to this rich dynamics that we're
generating every instant in our lives?
But another contributing factor, it seems that there are also new quantitative
methods to look at these kinds of networks.
Yeah, that's something that I think we have only had for a few years now.
Uh the fact that we can go in and and take
a network and actually say something about it not just describing it
you know this is connected that and it's not connected so not not not just
give a narrative description of what's connected to what but also say something
about the organization of it and it's driven actually by theoretical developments
that are in other that have occurred in other fields than neuroscience you know
people have been studying social networks for a long time going back all the
way to the 1930s, really,
and then blossoming really in the 60s and 70s.
And people coming from the physics community have increasingly applied network
metrics and graph theory measures to complex networks ranging all the way from
the web to transportation and power grid and collaboration networks, citation networks.
And it's been, I think, on the whole, very informative about,
it sort of allows you to unpack something about the structure of these networks
and also compare them and say, how is this differently organized from that?
How these networks come to be, it's driven by human activity,
it's driven by technology, it's driven by social developments in social systems.
And in the brain, it's driven by development and perhaps evolution.
And so these are interesting ways by which we can explore
networks and they haven't been around for very long and so that's i
think maybe another new ingredient here and with those
it seems to suggest or in some sense you take
a more an anti-reductionist stance in
this perspective right because sometimes you're saying also if you're going
to say about the social social networks um social networks are more than their
parts yeah and in some sense you make a similar statement then on brain organization
i personally do yes i think that when it comes to I like to use the economy as a metaphor.
In a trivial sense, the economy at a large scale is the sum total of all the
individual transactions that go on between people.
If I purchase an item or take out an insurance policy or a mortgage,
those are interactions that involve me as a single agent and perhaps a few other
agents that are representing banks or what have you.
But the entirety of the economy, you know, the global state that it's in,
perhaps, you know, whether economies globally are growing or contracting,
whether they are doing well or not so well, it's hard to reduce that to me as
a, you know, it's not really, doesn't exist at my level. I contribute to it.
I'm an atom in the economy.
But what the economy is cannot be reduced to my actions, really.
I think the same thing it is in the brain. Neurons are obviously the building blocks of what goes on.
They each contribute in various ways to the overall pattern of activity that's there.
But the pattern as a whole is not just the sum total of what goes on in individual
neurons. There's more to it. There's organization.
There's emergence, if you wish. It's a very difficult term to use,
perhaps, but I'll use it anyhow.
And so I think the network perspective also takes you away from trying to reduce things to,
you know, reducing memory or attention to properties of single neurons and rather
view them as properties of a system.
Right. But there are also some new terminologies coming in, right?
Because now, for instance, you would talk about hubs, where you would say,
well, these would be areas that play a special role in these larger dynamical systems.
So how should we think about this notion of a hub? Yeah, that's a good question.
In fact, it gets us back, in some sense, it inverts my statement I just made.
Network approaches also allow you to analytically decompose a network again
into elements, and then ask questions about these elements.
How are they embedded in the network? What is the contribution they make to the network?
What happens if they are deleted, if they are damaged? What are the consequences
for the network as a whole? And in other disciplines, people have looked at
these questions. For instance, in,
protein networks, interaction networks, genomic networks. The question is,
you know, if I have a protein here and this protein is deleted from the cell,
it disappears, what are the consequences for the integrity of the other,
of the remaining interactions in the cell?
Do they all become disrupted as well? This is completely inconsequential.
Similar questions can be asked in the brain.
They're beginning to be asked. I think we have no good answers yet.
I think it's a good question. What does it mean to be a hub,
okay, in the brain? Is that a meaningful question?
I believe it is, but we haven't got a lot of empirical evidence yet.
What happens when we delete certain regions of the brain when there's a lesion
happening in a primary sensory area versus an area that's perhaps involved in,
quote, high-level processing?
Can we, does graph theory, does network theory give you some handle on understanding
what the consequences are of that? that, make predictions that you can empirically test.
I think these are open questions that are beginning to be asked.
But in a recent paper, if you want, your co-authors did put your finger on a
potential hub, which was actually also a neglected piece of the brain.
Yeah, we discovered really, in a sense that we didn't know beforehand anything
about it, we discovered that there was a region in the medial parietal cortex
that was really quite prominent.
It stuck out when we looked at the network. It stuck out in terms of the number
of connections that it maintained,
the number of connections that were attached to it, the centrality of it,
which means how central it is relative to the different paths that go through the system.
And only at that point did we then read the literature and actually discovered
in the literature that people had been describing this piece of brain before in the context.
The default mode network, where it is a central component, but also brain activation
studies that have to do with self and with memory retrieval,
with the imagined in the future,
lesion studies, where if you do have damage in that area, it's very disruptive.
And so it had already been noted as an extraordinary brain region before, in some sense.
It came up in the network study as being also rather prominent and that perhaps provides predictions.
One of the things that people have noted about this is that it's very highly
activated when you are at rest.
This is the part of the brain that uses up the most energy.
Now is that a cause or is that a consequence of it being a hub region?
Perhaps hub regions are just busier. Perhaps they are more engaged in the ups
and downs of what goes on in the brain and therefore for a brown more energy.
So that's a prediction one could test, perhaps in an empirical setting.
But now the kind of network data on which you base this interpretation is in
some sense fairly abstract, right?
It's not like a direct anatomical measure of connections.
It's a derived measure. Like in this case, you do this tensor diffusion imaging. Yeah.
So what are the problems that you're facing using these really abstract methods
in inferring connectivity?
I think that's another good question. I mean, I like to point this out to people all the time.
The graph description or network description is an abstract model.
It is not the real brain. There are many assumptions that go into constructing
it, starting with defining what your nodes are and then finding some way of measuring the edges.
What is that really that we get from diffusion imaging? Is that the number of
axons that go through a particular volume?
Is that a measure of their myelination status, how myelinated they are?
Is it a measure of their thickness or their integrity?
So these are good questions. For example,
we have no access right now to physiological efficacy or strength.
We could be finding axons that actually are physiologically very weak.
We could find axons that are physiologically very strong, and we would never
be able to tell the difference right now using just this one technology.
So that is clearly a limitation of that particular technique.
My sense is, though, that as we move along in time, we're going to get deeper insights into this.
Other methods will come along. Perhaps this method will be more refined.
There's also the combination of doing diffusion imaging and perhaps a functional
MRI or perhaps EEG or MEG, combining different modalities, you know,
getting a better handle on what goes on dynamically,
because that can give you a clue as to how strong these connections really are.
So it's, it doesn't really, um.
While all these limitations are certainly in place, and I always like to point
out that there are limitations, to me it doesn't detract from the value of the approach itself.
Because the approach is ultimately motivated by a theoretical argument,
namely networks are really important.
They're not everything, but they're really important for understanding the brain.
Technologies will always be turning over. I don't think in five or ten years,
who knows, we will be doing this much fMRI anymore. We might be doing something different.
But the questions will remain the same as they have since our style, right? Sure.
But now there's a problem, right? Because in some sense, the methods you use
to quantify a network structure are neutral to their interpretation.
And in some sense, there are multiple ways in which you can generate complex
data sets in which you can now discover network-like organizations.
But maybe 95% of these are spurious networks.
Like, for instance, the case you just described. Indeed, we might have myelinated
fibers going somewhere with synapses that are not effective at all.
So aren't you, in some sense, adding more noise to the debate?
Or how do we prevent adding more noise to the debate that is already there?
I think the only way to really come to a consistent picture in the end is to
really do multiple different studies, come to some sort of agreement between
investigators who use slightly different methodology.
It would be wonderful if we had a totally different way of measuring brain,
a connectivity anatomy, in a way that's not invasive.
And it might happen one day. I don't know what it will be, but I'm sure it'll happen.
And then this will be applied again,
and we have a way of going back and distinguishing signal from noise.
Remember, there's always classical anatomy. We always can cut up brains and
look at them histologically, and that's by many considered to be the ground truth.
I think we will see new methods in that realm pretty soon for humans,
for human brain, postmodern brain, to go back and really slice them very thinly,
to try to reconstruct using microscopy
the direction of fiber pathways at least
and perhaps something about their density that'd be a totally different way
of looking at it i would hope that we can then come to an overlap between these
two approaches but what is interesting is that you don't seem to refer at all
to any kind of animal model why is that.
Well, I like animals and I like animal models even more, but a lot of work has
been done in mice, obviously, for the genetics, and we have a lot of data there.
It'd be very important to get some good connectivity data in the mouse.
I think people are working on that.
The macaque has been for a long time a preferred model for primates.
So that's all good. I think if we had even a much wider range of animal species,
we could get into comparative studies and that would be very exciting to figure
out, you know, so how is the, what are the architectural principles that vary
across different animal species, perhaps within the mammalian,
the animal kingdom, perhaps even including invertebrates, you know?
How are these brains different in terms of the architectural principles?
So that's all great. I personally am interested in humans, and I think humans
deserve that interest because we are interested also in figuring out what goes
wrong in brain diseases that are very common.
And it turns out that my hunch is
that a lot of the conditions that we're struggling with in human beings,
from Alzheimer's to schizophrenia to other forms of brain damage and so forth,
have ultimately something to do with connectivity.
Disturbances of connectivity that manifest themselves in disturbances of mental processing.
And that's one of the things that I personally am interested in,
you know, understanding that better. I think it's very difficult to get to animal
models of any of these conditions.
People have tried very hard, and it's not easy going.
If we study humans directly, I think we might have a better way to go.
But I could imagine that some sort of control experiments might be deployed,
where we actually look at calibration with specific animal models,
where at least we have some sort of understanding of the physical connectivity and their activity.
So that's really, I totally agree with that. I mean, people have actually,
myself included, have tried to do that with macaque.
Where we have vesicle track tracing and still being done by some laboratories.
And we can also do the fusion imaging on the macaque brain.
We can take a post-mortem brain and put it in a scanner and actually scan at
very high resolution because post-mortem, you don't have to worry about keeping
it in there for a long time. It doesn't move and so forth.
No, she waits long enough. She waits long enough, it moves. And it turns out
that in the... Now, that tissue is very hard to come by.
It's very precious, obviously, and you don't want to be working with that on any kind of large scale.
So there's only very few brains that become available more or less by accident.
And in the one case where I myself have looked at a macaque hemisphere,
diffusion imaged, and then compared that to a classical Pocomac database description
coming from track tracing.
I forget the exact numbers now, but overall about 5% of the fibers that were
found in the diffusion imaging were in places where we know fibers not to exist in the track tracing.
So we had relatively few false positives.
However, we also know that diffusion imaging is notorious for struggling with
crossing fiber pathways, with inter-hemispheric connections,
with very long connections.
And I think we are dropping out connections there at a rate that we need to improve on.
So I think where we find fibers, I think we have them, but we also are missing
some, and that's something that we need to work on further. But now if you find
fibers, how can you say something about their causal organization?
Right, what the input-output structure really is.
Yeah, and that's, again, classical track tracing allows you to say much,
much more about that, because you have directionality.
You actually know where you've injected, and then you know how the tracer is transported.
And that tells you something about the direction of the pathway itself.
And not every pathway is bidirectional. There are definitely pathways that go
only in one direction. and they could be extraordinarily important by setting
up the dynamics, it turns out.
People have been studying this. Now, we have no information on that coming from
these non-invasive diffusion imaging methods at the moment.
I think there are some people out there who are working on this,
but I have not seen a single study where people have been able to determine directionality.
It's a big gap in our knowledge.
Okay. But there may be also another interest of yours could come in, right?
Because certainly, I mean, you're around in this field now for some time and
also with a strong theoretical interest and having worked and published on a
number of models of the brain.
But in some sense now you seem to be more in a descriptive mode you're going
to describe these complex networks it's difficult to get access to causality
so how do you think are you going to bring this back into a more theoretical realm,
Putting together, let's say, a more broad picture on brain function.
How are you going to achieve that? I think I personally do want to do that, yes.
It's true right now. I mean, making a map of the connections of the human brain,
let's say, is primarily a descriptive task. You're not testing hypotheses.
You're not really interrogating the architecture. You are trying to make a map.
Now, I happen to believe that having a good map really helps you if you want to get places, right?
Because, you know, now you know something about how things are organized,
and then perhaps you can ask good questions that are open to experimentation
or open to modeling, which is not just descriptive,
but also serves to test hypotheses.
So, one of the questions that I have in my mind is we've been describing connections
and we've been making maps.
I want to bring this back to human cognition, right?
We haven't talked about that yet, but this network in there is doing something, right?
It's doing something right now. It's making me speak and making you listen and
ask the next question and this is all happening, right?
So how do we get from a description, these fibers go here and these fibers go
there, to an account what is going on in the architecture as we're engaged in
this interaction, dynamically engaged?
That cannot be reduced to the anatomy. The anatomy is like a skeleton that maybe
enables all this to happen, but it isn't going to make it do by itself.
And so my hope is that when we understand the map a little bit better,
maybe we also see some relationships that this map has to broad categories of
human mental processing.
You know, things like memory and attention, planning, movement.
But in a correlational sense, or...
Do you see yourself build models, large-scale models?
Hopefully more than correlation, because if you can make models that are structurally
based and that get you into the realm of neurodynamics, but also perhaps cognitive processing,
engagement with the external world, in a modeling context, you can now manipulate the model, right?
It's a little bit like mutating a protein and seeing how the mutation affects
what it actually does in the cell.
You can manipulate it. That's a perturbation of the system.
Something that you can now do to ask questions about, if I delete this pathway
over here or if I make it stronger, what is the effect of that?
That, not just on that pathway, because you've deleted it, but also on the rest of the brain.
How does it reorganize? How does it reshape itself to deal with that disturbance
that you've just included?
But is it going to be like a brain in a vet model, or it's going to be a model
that will be instantiated in some form in the real world?
Well, I sure hope it has to be embodied. I mean.
To be more conservative might be happening more in a closed setting where you're
just having a big network and
you are studying dynamics within it and it's actually not doing anything.
That's what we've been doing in the past couple of years and that can be extraordinarily
informative about what the capacities of that network are, you know,
what kind of dynamics can it actually generate by itself.
But it doesn't tell you how it reacts to a perturbation coming from the outside,
a stimulus. And even that's not enough, because you also have to allow the brain to act on the world.
Otherwise, if I took your brain out, forgive me for suggesting that,
put it into a vat, I don't think you would be you anymore.
And not just in your physical appearance, but also I think your cognition would shrink dramatically.
You wouldn't be able to do anything. I might even be more boring.
No i mean you know we are we are always we
always embodied every moment of our existence we are connected
to our body our body allows us to act on the
world it's the only chance we have to do anything um that
has got to be important i know it is important and mapping
the brain brain's connectivity should not distract us from realizing that exactly
so to switch a little bit i I also understand that this initial more intuitive
idea about the connectome has really been picked up at a much larger scale in the US.
And now NIH is supporting a number of projects in this area,
of which also you and your collaborators have got one.
So what's the objective of this project? It's really to get a ground-level map of the adult,
normal, that is clinically normal human brain over a wide, over a large population
of normal, healthy adult brains.
And the reason for doing it that way, I think, has to do with the fact that
brains are quite individual.
So if we just took one brain and we got a map for that brain, that'd be great.
But you know, we wouldn't know, well, we would know maybe with 80% certainty
how your brain looks like, but that's not really quite enough.
There's a lot of variation in individual brains, even normal,
healthy adult human brains vary tremendously in the way they are physically
organized. Which is, by the way, a real interesting fact in itself.
How can we be so alike cognitively and yet have such greatly varying architecture
that we're working with?
So part of that, the goal of what NIH wants to do, I think, is to establish
a foundation in healthy adults and also correlate that with behavioral and genetic data.
And then I think the next step for others will be to look and compare how these
normal brains compare with brains from people with certain conditions.
Another interesting question is development.
I happen to have done a little bit on that just recently, working with colleagues
at MGH and Patrick Hagman at EPFL.
And it turns out that the brain changes tremendously in terms of its connectivity
structure between ages 2 and 18, which is relatively late development in most
people's books. At age 2, we are already pretty autonomous.
And yet you see continuing changes to network changes, connections change,
there's different network metrics that systematically change across development.
And these are just some of the questions that can be asked and can be productively
hopefully addressed using these kinds of connectome data.
So to close off, two questions.
So you're a man with some experience in this field and outside this field.
So what's the Olaf Sporn's law that we should remember so what's Olaf's law
about what science life in general exactly I don't know I would say,
I can say one thing as someone who's working connectivity as you've pointed
out it made me feel old actually I've been doing this for a while,
10 years ago nobody wanted to hear about this really.
There were very few people who were doing this and were interested in it.
And neuroscientists, quite frankly, didn't understand why we needed any theoretical
analysis or modeling of this kind that we've just discussed.
I think with the arrival of these new methodologies that spit out enormous amounts
of data very quickly, there's a growing realization we have to have some insights here.
It's not just all about generating masses of data. we have to have some guiding
principles that actually.
Allow us to make sense of it and uh i'm
not sure that it's necessarily uh the spawns law
but i i would say you know um we do need
that and i'm very happy to see that we're beginning to
make progress in that direction so spawns law would
be something like less data more ideas no no let's say i think more more data
and more ideas okay data is good generous data now data data is good it's important
you know we if we didn't have this data that we're having now i think you know
it was sort of what we were talking about years ago, we didn't have any data like that.
Now we have some, but we also need simultaneously in neuroscience to be open
to theoretical ideas, which ultimately allow us to ask new questions and understand
the material that we're dealing with.
It's not possible to do science without theory, okay?
Some people seem to think if we just have more data in the end,
it'll just sort of fall out, you know? That's not going to happen.
All other sciences are involving theoretical ideas always, and we should do
the same in neuroscience.
Not to say that most of them aren't going to be false. That's fine.
Progress is made by rejecting ideas that are wrong, and we need to be open about that.
But we can't make progress without theoretical insights.
I don't mean just computation. I really mean theory that is having some understanding
of what really goes on at the fundamental level.
So my last question would then be what's what's
the one prediction you really want to stick your neck out for today so
i can come back to you five years from now say look olaf this was
your prediction was wrong now you owe me a beer what what's that one prediction
oh that's a difficult one because uh when you're you're gonna do that i know
that you're gonna be there in five years and you're gonna ask me about this
and then i'll remind you every time i meet you i don't know if I have a single prediction,
but I think that if the model that is currently being constructed,
that is the brain being a complex network with modules that are interconnected
through hub regions and perhaps engage in complex dynamics of the kind that
you find in critical systems,
if that were to be discarded five years from now, I'd be very unhappy because
because I wouldn't have a second option to go to.
That's not a single prediction, but the model that's sort of crystallizing right
now about the brain as a multistale hierarchical network with complex dynamics,
where the complexity really matters for how it operates, that I think I would
hope to see confirmed in five years or in 10 years.
I don't know. But if that goes out the window, then I'm back to not knowing how it works.
Very good. Olaf Sporns, thank you very much. Thank you.