Transcriber: Tanya Cushman Reviewer: Rhonda Jacobs
Thank you for the introduction.
Can everyone hear me okay? Excellent.
If at any point the sound cuts out, please let me know.
So I'm very excited to be here today to discuss an upcoming project,
which should be launched this month:
the Cognitive Connectome.
You may be wondering, What is a connectome?
For this, I direct you
to the National Institutes of Health Human Connectome Project,
which is an attempt
to map all of the different connections that we have within the human brain.
You can find these images, as well as several other gorgeous images,
mapping out the structural connections within our brain.
Now, how do we actually go about getting this information?
My method of choice is functional MRI.
Here is an MRI scanner;
this is the first research-dedicated human MRI scanner in the state,
at University of Arkansas for Medical Sciences.
When we lie subjects on the bed here,
we can lift them up
and put them into the bore of the powerful magnet shown here.
And then we can present them visual stimuli,
such as through this screen you see off to the side.
We have a headset and a microphone
with which we can communicate with the participant,
and we also have a button box
with which we can record their motor responses.
So we can create a very rich environment in which to challenge cognition.
And just like a, you know, a cardiogram would measure your heart rate,
we can challenge the brain
and look at different networks of brain activity.
Now, it all starts -
to look at functional imaging with MRI,
it all starts with the hemoglobin molecule.
And this is a fallback to my days in chemistry.
Here's a color-coded hemoglobin molecule
of each of the heme groups indicated by a different color.
You may not be able to see this,
but in the middle of each of these heme groups
is an iron molecule,
which, as you may remember, is a magnetic element.
So what we do with functional imaging
is we look at the magnetic influence
that hemoglobin exerts upon surrounding tissue.
And what's interesting is that hemoglobin will carry oxygen through the brain,
but when oxygen binds to each of these four subunits,
the oxygen molecule effectively masks the magnetic influence
that the iron core can exert upon the surrounding tissue.
So in the bloodstream,
you have a series of oxy- and deoxygenated hemoglobin -
hemoglobin with and without oxygen bound to it -
and a set ratio of how much magnetic influence is being exerted
upon the surrounding tissue.
Now, following a neuronal event,
there is an initial consumption of oxygen
followed by an influx of oxygen-rich blood,
which offsets that ratio.
And through this offset,
which we can measure with a very powerful magnet
like the ones we currently use,
we can detect where brain activity is occurring -
corresponding to neuronal activity.
Here's one of many examples that you can find in the literature.
I picked this one because it's one of mine,
published in 2009,
in which - it was a project looking at the aftertaste response
to both sucrose solutions, a natural sweetener,
and aspartame solutions, a non-caloric sweetener.
And what we found here in the -
well, you can look between the two, and there's quite a bit of similarity,
especially for the insula, which is the C-shaped structure here,
which is the primary taste cortex.
So we were interested to find a similar taste response
to both the natural non-caloric sweeteners.
But this is old hat;
I mean, this is stuff we've been doing for two decades.
I will point out -
you may be wondering why there's a large visual cortex response here,
as you see in the posterior portion of the brain.
That's because we paired the juice delivery with a visual warning;
we didn't want subjects to start choking while in the scanner.
So that's why you see that there.
But the direction our field was really interested in
is looking at patterns of brain connectivity
and looking at networks of brain activity,
which again pulls us back toward the connectome.
This is one such network that's been highly replicated within our field;
this is the default-mode network.
It is a series of three to six regions
that show a great deal of correlated activity,
both during task and at rest.
This particular network was discovered almost by mistake,
as, I suppose, most great scientific discoveries are.
Early in our field, we were looking at brain networks
that became more activated during task than rest,
and then along the way,
people noticed that, well, you know, if we compare rest versus task,
we get this same network of brain regions
regardless, largely, upon what task you're performing.
And so this is just a depiction of the network
so you can see it in better view,
especially with the slice through the midline there.
But this is really where we want to be going with the field -
is looking at functional networks.
And not only just looking at the structural connections,
which are very critical,
but also the functional connections composing the brain.
I have to give a lot of credit to the Functional Connectomes Project,
which currently boasts over 12,000 resting-state fMRI data sets.
So that's - I'm sorry - 1,200 individuals engaging a resting-state scan
in which they passively look at a cross or have their eyes closed,
collected across 33 sites in the world.
Now, the data is anonymized
as information about age, sex and the imaging center
as well as the imaging parameters with which it was acquired.
But the best part is that it's freely available for public download.
So if you really want to explore
either inventing new methodologies
or seeing what we all have in common with our brain connectivity,
this is a great place to start.
Although, I will say - a downside of this is that, as noted,
the only demographic variables available
are age and sex.
So it limits the kind of nuanced analyses
that we can do.
Although, I will say there has been some pretty amazing work
just with those limited variables.
For example, Dosenbach, in the Science last year,
published an article
which could predict your age
based on the maturational connections within your functional networks -
so going just by resting-state data, predict how old you were.
So it's pretty exciting stuff,
which, you know, wouldn't have been possible five years ago
if not for highly collaborative multi-site studies.
Now, but why would we want to include more variables?
Why would we want to start exploring individual differences?
Well, one reason is to improve our understanding
of neuroimaging biomarkers of mental illness.
For example, there's been a large number of studies
looking at emotional reactivity in the amygdala.
The amygdala is an almond-shaped structure;
you have one in each hemisphere.
It is especially attentive to pertinent stimuli in your environment,
especially those that carry with them an emotional component.
And there's been many, many studies looking at brain responses
to both happy faces and sad faces and fearful faces
as well as pleasant environments - or pleasant scenes,
non-pleasant scenes, etc.
And what we have found
is that the amygdala can be sort of a biomarker for mental illness,
like major depressive order.
We see that the amygdala
tends to be blunted in its response to happy and positive emotional images
and exaggerated in its response to negative images.
But in the past decade - actually dating back to 2001 -
people started looking at individual differences.
For example, personality measures.
And we know - there's a consensus
that if you look at people who are highly introverted,
they also have a blunted emotional response to happy images.
If you look at people that are highly neurotic
on a neuroticism scale,
they'll also have an exaggerated response to negative emotional images.
And if you think about the construct of depression,
typically those two syndromes - high introversion, high neuroticism -
accompany the disorder.
So we have to take a step back and ask,
Are we identifying the biomarkers for mental illness
or are these biomarkers for introversion
or biomarkers for neuroticism
or biomarkers for what have you?
Really, the only way to get to the crux of this issue
is to start looking at individual differences.
We need to just take a step back from looking at how we're all similar
and really start looking at the nuanced differences
that make us all unique.
And I believe this is a critical step, which our field is starting to accept.
As of this year,
we now have the International Neuroimaging Datasharing Initiative,
or the INDI.
I jokingly say it was named after me:
INDI - Andy.
But for legal reasons, I have to clarify:
no, it is not named after me.
This is a collaboration across many sites.
The current contributors of data are listed here.
It's actually the brainchild of Michael Milham at NYU.
And like the previous connectomes,
the INDI is a data repository
for resting-state data and structural data,
but also a wide variety
of neuropsychological and behavioral measures,
things like verbal intelligence and picture IQ - things like that.
Although, the new work I'm developing will take this a step forward.
In addition to having resting-state scans and structural scans,
we're going to add on even more neuropsychological assessments
and more assessments of cognition.
And I've dubbed this "The Cognitive Connectome."
Although "Cognitive Connectome" -
it's all about branding these days; you need a catchy name.
I've also toyed with "The Cog-Conn."
That sounds too much like a comic book convention though.
You know: "I'm going to Cog-Conn this weekend.
It should be lots of fun. You should come."
Or perhaps, to be even trendy, it could be "The Cognectome."
Sorry - I couldn't resist.
Now, the cognectome
will consist of 24 well-validated neuropsychological assessments
spanning eight cognitive modalities -
things like attention, language, reward valuations,
executive function/working memory,
motor ability, visual-spatial ability, memory and affect.
It will also include 11 canonical fMRI paradigms
that have been replicated time and time and time again within our field
but in small, poorly characterized samples of 20 to 30 people, tops.
And this is largely a limitation, a historical limitation, in our field.
When I first started using brain imaging, functional neuroimaging with MRI,
we had one scanner that was jointly shared
by the VA Medical Center of central Florida,
Shands Hospital in Gainesville, Florida,
and the University of Florida.
So if I wanted to get in research time,
we had the sweet time of 5 PM to midnight in which to do all of our work.
Now the scanner's a bit more ubiquitous;
they're more widespread and much more accessible,
which allows large samples
derived through multiple site collaborations possible.
Now, some of these paradigms, again,
will test the different cognitive modalities.
For example, a Tower of London task
in which you see three pits of different depths
and three colored balls in those pits.
And then you have a starting point and an ending point,
and moving the balls, one at a time,
try to get the starting point to the end state.
The goal would be to characterize this within a large normative sample
of 100 or more subjects
so that we can both generate a large, well-characterized database
in healthy control subjects,
who also undergo clinical testing
to make sure there are no underlying mental illnesses.
So, we can only not only inform
how individual differences affect these brain networks
that we see, time and time again, in the literature
but also add to it a normative sample
for future testing with clinical populations,
like depression or schizophrenia,
what have you.
So that is what we're doing.
We have some work coming up.
In about a year, we will have more data to share with you.
It should be really exciting to actually be able to map out
how the networks we see are affected by things like handedness.
I mean, really, if we had 1,200 subjects and just a handedness measure,
that would be amazing to deal with.
So I will like to leave you with a special thanks
to the Psychiatric Research Institute
and UAMS,
and my many, many collaborators at the Brain Imaging Research Center
depicted here and here.
And I believe that's all for me, so thank you very much.
(Applause)