Basics of fMRI

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En provenance du cours de Johns Hopkins University

Fundamental Neuroscience for Neuroimaging

385 notes

Johns Hopkins University

Fundamental Neuroscience for Neuroimaging

385 notes

Neuroimaging methods are used with increasing frequency in clinical practice and basic research. Designed for students and professionals, this course will introduce the basic principles of neuroimaging methods as applied to human subjects research and introduce the neuroscience concepts and terminology necessary for a basic understanding of neuroimaging applications. Topics include the history of neuroimaging, an introduction to neuroimaging physics and image formation, as well as an overview of different neuroimaging applications, including functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy, perfusion imaging, and positron emission tomography imaging. Each will be reviewed in the context of their specific methods, source of signal, goals, and limitations. The course will also introduce basic neuroscience concepts necessary to understand the implementation of neuroimaging methods, including structural and functional human neuroanatomy, cognitive domains, and experimental design.

À partir de la leçon

Principles and Methods of Neuroimaging

This week will introduce the principles of neuroimaging and applications in structural and functional neuroimaging.

Approaches to Neuroimaging16:09

Basics of MRI14:23

Basics of fMRI15:13

Rencontrer les enseignants

Arnold Bakker
Assistant Professor
Psychiatry and Behavioral Sciences

[SOUND] In the previous module,

we discussed the basis of the signal

that leads to fMRI images.

In this particular module, we'll discuss the basis of the fMRI signal,

the signal that is used to create functional MRI images.

So as we discussed in the previous module, spins or

protons precess around their axis.

As well as around the axis of the magnetic field, the static magnetic field.

And they have a position either parallel, low energy, or anti-parallel,

high energy along the axis of the static magnetic field.

We then use a radio-frequency pulse to essentially knock that spin

out of the alignment of the static magnetic field,

after which precession occurs back to its original or resting state, if you will.

And by using a radio frequency receiver,

we can measure the energy that is sent out by this precession process,

and measure either the longitudinal or transverse relaxation time.

The time that is necessary for

that spin system to go back to its relaxed or low energy state.

By measuring that, we can create a T2 signature and as we've seen,

different types of tissue have different relaxation times.

And we can use that information to create

an image of the structure that we're trying to create an MRI image of.

So here, again, we see an example of a T2 weighted image showing

the CSF in bright colors and the gray and white matter in shades of gray.

Now how does a functional MRI scan get generated?

Well functional MRI is based on very similar principles but

it focuses on a slightly different aspect.

In its basic resting state, the brain has,

obviously capillaries and arteries and veins that manage the blood supply to

the brain as we have discussed extensively in one of our previous modules.

During the resting state there's a certain ratio of oxygenated vs

deoxygenated hemoglobin present, that provides a resting state situation.

When neurons are active, or when a particular area of the brain is active,

oxygen is necessary to replenish the activity there,

to replenish the oxygen consumption there.

So there is more deoxygenated hemoglobin present locally than there is

oxygenated hemoglobin.

Finally, in the activated state an influx

of additional oxygenated blood is supplied to the area for that replenishment,

increasing and changing again the ratio of oxygenated vs deoxygenated hemoglobin.

Now oxygenated and deoxygenated hemoglobin have different effects on dephasing

with deoxygenated hemoglobin causing more dephasing than oxygenated hemoglobin does.

Because of this,

the technique is referred to as block Blood Oxygen Dependent Level MRI.

It measures the change in the homogeneity in the magnetic field within a particular

volume, which is referred to as T2*.

So, in the top left image, you can see an example of oxygenated blood creating

a larger signal on MRI imaging and deoxygenated giving a slightly smaller

signal following that same precession method.

So when we measure the T2 relaxation, we can differentiate

the oxygenated versus deoxygenated blood for a particular area in the brain.

Now, what's the difference between T2 and T2*?

When we've discussed in the previous module T2, is the transverse magnetization

decay of a spin after the radio frequency pulse has introduced excitation.

As you can see in the green line at the top.

T2* refers to the transverse magnetization decay from local magnet field variations.

So the magnetic field is not perfectly homogeneous and

it's also effected by the local situations.

By neurons that are influencing the magnetic field locally and by molecular

interactions or cell interactions that are causing slight distortions.

So within the spin frequency that happens after removing the radio frequency

pulse there is also variations that are dependent

on the specific local environments.

If you can see in the blue line in the top left image, there's a slight change

in the frequency that is measured at each precession individually.

And by focusing on that variation, which is a measure of the phase decay

that's happening, we can take an estimate of the local distortion of

the magnetic field which is the result, or thought to be the result,

of a change in the oxygenated verses deoxygenated blood ratio.

So, how does this work then?

The oxygenated blood is paramagnetic and it introduces inhomogeneity.

It distorts the local magnetic field that is measured by the T2* measurement.

Oxygenated hemoglobin is weakly diamagnetic and

has very little effect on that situation.

So essentially does not distort the signal there, if you will.

So when oxygen is absorbed by the astrocytes to replenish oxygen and

glucose metabolism in the cell that has been firing,

it causes hemoglobin-induced dephasing as you can see on the top righthand side.

Which causes a change in the MRI signal that one is measuring.

After a certain period of time, deoxygenated blood

will cause more distortions locally in that area than oxygenated does.

And by picking up that difference we can draw a conclusion that brain activity must

occur in that area.

So let's look at a little bit more carefully about what happens

in an individual situation.

Usually the idea is that a stimulus result in brain activation, for

example I'm asking a person to tap their finger and

they start tapping their finger very specifically.

Initially, oxygen is removed from the blood that is necessary for

that brain activation to occur.

So there's an initial dip in the MRI signal,

a depletion if you will, of the oxygenation.

In response to this brain activation, the blood supply system creates an influx of

blood that then gives rise to the BOLD signal as it

does not distort the MRI signal locally, until it reaches a top.

At that point the activation or the stimulus is removed, for

example I'm asking the person to stop tapping their finger, so

at that point the oxygenation and MRI signal drop as the cognitive task ends.

It typically overshoots beyond the base line a little bit, and

why exactly this happens is unclear.

But it usually undershoots a little bit for a few seconds until it comes back and

the ratio of oxygenated and deoxygenated blood and

MRI signal are back to baseline and essentially back into its resting state.

So here you see what is called a hemodynamic response function.

A blood supply increase and drop in response to a cognitive

function or cognitive task that the brain is executing.

Essentially giving us the signal that we need

to measure brain activation in a particular area of the brain.

It's very important to note that BOLD fMRI does not measure neural activity directly,

rather it measures metabolic demands, oxygen consumption of active neurons.

The hemodynamic response function that I just showed you in the previous slides

represents the change in the fMRI signal triggered by this neural activity.

So then what is the physiological basis of this BOLD signal?

Well from some of our very first modules we know that an action potential in

a presynaptic terminal gives rise to the transmitter release

from the transmitter molecules into the synaptic cleft.

There they bind to post-synaptic ion channels and open those ion

channels to allow an influx of ion into the post-synaptic cell, thereby hopefully

triggering a post-synaptic current of an action potential if you will.

The re-uptake of those glutamate neurotransmitters

by the astrocytes triggers glucose metabolisms.

So if you recall, we have two major types of cells in the brain,

neurons and astrocytes.

Which are essentially housekeeping cells that provide everything we need for

metabolism and removal of waste products.

Those astrocytes are responsible for

taking the glutamate up back out of the synaptic cleft.

The astrocytes then pump out ions out of the cell to restore

the ionic gradients in the local area.

And this entire process uses glucose and oxygen that is supplied to the cell.

So these astrocytes are responsible for

removing oxygen from the blood to use that to maintain this type of activity.

Initially BOLD signal was thought to be correlated with these action potentials.

That the more action potentials were present the greater the BOLD signal.

And this is some of the earlier publications that focused on that

by studying both BOLD activation in a monkey brain

in combination with single cell recording, trying to record the number of

action potentials that are occurring during that BOLD activation.

But a little bit more recently Logothetis and others and colleagues in 2001, did

more extensive experiments using both BOLD signals and electrophysiological data.

They've recorded activation from a number of neuronal units from

a number of neurons, referred to as multi-unit activity.

And they also recorded local field potentials,

which reflect a summation of post-synaptic potentials.

Now if you recall from one of our previous modules, local field potentials

are measured by measuring directly from the extracellular space.

If there's a lot of activity in neurons,

they draw ions from the extracellular space causing a depolarization, or

a reduction in voltage in the extracellular space.

And thus a dip in the voltage signal that you're measuring.

That's obviously not generated by a single neuron, but

a group of neurons locally will draw in these ions.

So local field potential are essentially measuring an aggregate

of action potentials that are occurring in that area,

in that group of neurons where that electrode is located.

When they did this study, they looked at field potentials and

these multi-unit activity.

And what they saw is that

the BOLD activation is most closely correlated with the local field potential.

So you can see the BOLD signal in the right hand graph in this pinkish color,

the local field potentials are indicated by the black line,

and the MUA the multi unit activity is indicated by the green line.

And even though there's a temporal off set in that the BOLD signal is visible later

than the local field potentials,

obviously there's a delay that is caused by the influx of blood

to that particular location that is much slower than the local field potentials.

The correlation between the BOLD signal and

the local field potentials is most significant more so

than with the action potential number that can be measured there.

In a follow up study, as you can see here,

the blue colored areas indicate local field potential measurements.

The red line indicates the BOLD signal that was measured there.

And the grey lines are an estimated or a predicted BOLD signal that is

calculated on the basis of the local field potentials in blue.

So if you predict what a BOLD signal would look

like based on these local field potentials, you would get the grey line.

And as you can see the grey line very closely matches the red line which is

the actually observed BOLD response.

So these studies together show that BOLD activity's more correlated with local

field potentials than it is with multi unit activity or

other measures of neural activity.

And BOLD activity is thought to reflect the input to a neural population and

remember me talking about the post synaptic action potential

which is the input to a particular neural population.

And the information processing that happens in this post-synaptic or

receiving neural population more so than anything else.

Now as you can probably see from these graphs the correlations aren't perfect.

BOLD activation should never be taken as a direct

measurement of local field potentials.

It is still a derived measure that for, according to these studies,

is most closely related to local field potentials, but

by no means representative of actual local field potentials.

So just to summarize the basis of the fMRI signal, we have seen that at

the onset of neural activation oxygen is removed from the blood to support

the local cognitive processing that is happening in a particular area of a brain.

Which changes the magnetic properties of the blood in that particular area.

The activated state will cause an influx of oxygenated blood which again changes

the local magnetic properties.

By measuring that very carefully we can estimate or we can measure a hemodynamic

response function for that area which certainly seems to be relatively well

correlated with local field potentials, with local neuronal activity in that area.

By doing this in three dimensions, and

we're going to talk a little bit more about how that's done.

We can generate activation maps that represent brain activation

in response to a cognitive process or stimulus that we can localize to

a particular brain area and use for functional magnetic resonance imaging.

Now in the next module,

we'll discuss a little bit more about the basics of an fMRI experiment.

How do you elicit these cognitive processes, and

what factors do you need to keep in mind when you're designing an fMRI experiment?

[SOUND]

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