Brain Scans



PART 3: Principles and Methods of Neuroimaging

Approaches to Neuroimaging

In this module, we will be discussing some different approaches to neuroimaging methods. Neuroimaging uses various techniques to directly, or indirectly image the Central Nervous System. The approach can either focus on Structural Imaging, creating an image of the anatomy and the pathology or injury or focus on Functional Imaging, where one tries to create an image of the metabolic or pharmacologic response of the brain or cognitive functioning. There are many techniques that can be employed to obtain either structural or functional images of the nervous system. EEG is a very commonly used method for functional registration of brain function, CT or Computed Axial Tomography, Single-photon Emission Computed Tomography or SPECT, Positron Emission Tomography or PET, Magnetic Resonance Imaging, Functional Magnetic Resonance Imaging, Diffusion Tensor Imaging or Spectroscopy Imaging. Now, I will very briefly step through some of these techniques, and some of these techniques will be explained in greater detail in future modules. For EEG, it measures the electrical activity in the brain, through electrodes that are placed on top of the scalp, as you can see in the top image here. There are standard methods, or standard places for these electrodes to be placed on the scalp so that studies can make comparisons between one another. It measures the activity at rest, or in response to the presentation of a stimulus, depending on the research question or clinical question, and it assesses localized brain responses or brain network responses, through temporal and spatial correlations of activation. On the middle-right image, you can see an example of the location of the various electrodes that are placed on the scalp, and at the bottom, you can see an example readout of what that EEG or electrical activity of the brain looks like. Each line represents one of the electrode placements on the brain. This method is very commonly used in epilepsy research when clinicians or researchers are trying to figure out where the locus of an epileptic seizure is. For Computed Axial Tomography, the system, as you can see on the top-right, essentially uses a series of x-rays. The x-

rays are sent through the volume or through the person, in this particular case, and the amount of x-ray absorption in the volume is measured. When you do this using a series of cross-sections, you can reconstruct a 3D volumetric image, following the methods that you see in the middle. An x-ray is sent through the volume, from many, many different angles, and over time, when you combine these images, you can see densities or lesser densities in the image, that can be used to create a 3D volume of the item, or the brain, or the nervous system, that you are trying to image. It is very fast, it is widely available, but it does involve a moderate dose of radiation, as these do contain x-rays that are being used to create these images. For Single Photon Emission Computed Tomography, a gamma-emitting tracer is injected intravenously. And therefore, it is considered a nuclear-imaging technique. It is injected either as a soluble ion, or it is attached to a ligand. A ligand is essentially a vehicle, that the gamma-emitting tracer is attached to, and the system that measures the uptake or the spread of that ligand, throughout the central nervous system, most commonly the brain. The SPECT scanner measures gamma rays in a series of 2D images, from multiple angles, to determine where in the brain the gamma emissions come from, which would represent the amount of uptake of those gamma-emitting tracers in the brain. In the middle-top, you can see a schematic representation of what that looks like. A gamma camera is essentially creating a series of 2D images of that uptake. At the bottom, you can see an example of a SPECT image, which represents increased activity or uptake in warmer colors, the red and the yellows, and decreased activity or less amount of uptake, in the blue or green colors. Now, note that the spatial resolution of these images is not fantastic. It is on the scale of about a couple of centimeters to about a centimeter. Positron Emission Tomography is essentially a refinement of the SPECT technique. It is also a nuclear-imaging technique, that uses a positron-emitting radionuclide, attached to a biologically-active molecule, just like SPECT. However, in this case, it's a positron which reacts with an electron, causing annihilation. The annihilation then sends out two gamma rays in opposite directions. The PET scanner is essentially a series of gamma cameras all the way around the subject, as you can see in the middle-top. It tries to detect the pairing of gamma photons, and the time delay between the measurement, and the measurement on the opposite side can give you an indication as to where in the volume, these gamma rays came from. As such, it gives you a slightly better resolution, or actually, a reasonably better resolution, compared to SPECT, because you're now measuring the difference between two gamma rays, rather than just one. It generates an image of active molecule-binding and depending on the modality that you are interested in, you can use different molecules and different binding ligands, to create an image of what you are interested in. For example, FDG-PET is a marker used for the uptake of glucose, very commonly consumed by the brain, obviously. This is very relevant for tumors, for Alzheimer's disease, where you can see significant differences in glucose uptake, which would be seen as differences in activation or brain activity, which obviously, requires glucose uptake. On the top-right you see an example of such a difference, where on the left-hand side you see normal glucose uptake in a normal aging person, and on the right-hand side you can see a different image, where there is less glucose uptake in the posterior aspect of the brain, indicative of early Alzheimer's disease. Similar, 18F-AV-45 PET images, use a marker that is binding to beta amyloid, a misfolded protein, that occurs in the brain of patients with Alzheimer's disease. By using F18-AV-45, you can create very similar images to what you can see on the bottom right, of a person who shows a beta amyloid positive brain scans or PET scans, with the red and yellow colors, whereas on the bottom hand, you see a healthy control subject, where there is not such binding to beta amyloid, and you can see the colder colors, there's the absence of the red and yellow colors. Finally, 11C-PBB3 is a tau-marker, which can be used for tau accumulation in the brain. Tau is a different misfolded protein, that occurs in patients with Alzheimer's disease, and this marker or this ligant can be used to create a three-dimensional image of the binding and the accumulation of that tau protein, in the brain of Alzheimer's disease, as well as healthy controls. Both of these are predominantly used for research purposes at this time, although the hope obviously, is that they become clinically-relevant with more research. Turning to Magnetic Resonance Imaging, which is also a nuclear-imaging technique, it uses both static and variable magnetic fields to perturb hydrogen atoms. The perturbation of these hydrogen atoms causes a magnetic resonance, that is then measured by radio-frequency coils. These radio-frequency receivers will convert this information into a three-dimensional image, and different pulse-sequences can be used to generate different contrasts, depending on the tissue type. On the top right, you see an example of an MRI scanner and a schematic representation of what that looks like on the inside. There's both a static magnetic field in the blue boxes, there are gradient coils, which are the variable magnetic fields that can be applied in different directions, and then the radio frequency coil is used to read out the magnetic perturbance that you're applying to the system, which can then be converted into an image. You see two examples of such images below. On the left-hand side, you can see that the CSF in the brain is shown as black, whereas in the right-hand image the CSF is predominately shown as white or lighter colors. This is a function of the type of image sequences that are being used or pulse sequences that are being used to visualize the brain. The magnetic field for an MRI scanner is very, very strong, and it's about 60,000 times the strength of the Earth's magnetic field. It's very dangerous to have metallic objects in the vicinity obviously. When participants participate in such studies, they have to complete an extensive screening form to make sure that it's safe for them to go in such an environment. For example, patients with pacemakers should never undergo an MRI, as the magnetic field will interfere significantly with the pacemaker and potentially could cause life-threatening damage. There are many other things that need to be considered. Again, there is a significant checklist that a person will have to go through, like shunt placements or aneurysm clips, that could be placed in the brain, that could interact with the scanner and could potentially cause harm. To give you an example of what could go wrong if you don't obey the rules, here's an image where a hospital bed is pulled into the MRI scanner because the person did not follow the safety guidelines. This image also gives you a very good idea of the strength of the magnetic field. That is an entire hospital bed that is suspended in the air, due to the magnetic force that is being exerted on it. So the safety precautions are very important in these types of environments. But, following those safety regulations, there is no radiation associated with Magnetic Resonance Imaging, and there are no known side effects, even with repeated exposure to the magnetic field. So it's a very safe procedure for people who can safely undergo MRI. It has a very good spatial resolution, and it has a reasonable temporal resolution, providing us with good information about the brain. Let's look a little bit more at the makeup of an MRI scanner. As you can see in the middle, there's a cut-through of the MRI scanner. You see the magnet indicated in yellow, which is always on. It's not something that is being turned on once the patient enters the scanner, it's always on. On top of that are gradient coils. These are these variable magnetic fields that can be applied in different directions, and at the top, you see highlighted, the radio frequency coils that are being used to read out the signal from the brain or from the central nervous system. On the bottom, you see a segment through the scanner. Again, to show you a little bit about how that magnetic field is created, the magnetic field or the magnet within the scanner, essentially consists of a wire that is looped around, very much the same as an electro-motor. In some cases, it can extend up to 20 kilometers' worth of wire that is looped around the scanner bore, and this is submerged in liquid helium, to cool down the metal to minus 270 Degrees Celsius. This makes the metal super-conducting, and allows for the generation of this magnetic field. Obviously, the helium has to be carefully handled and carefully managed, but it is contained within the scanner system and very safe. Overall it creates this magnetic field through the center of the magnet, that serves as the static magnetic field, which we will discuss in much greater detail in our next module. When it concerns brain research, often a head coil is used, as you can see in this image here, which has additional antennae that are used to read out the signal from the brain. On top of that, you see a mirror, which is often used to allow the participant to look outside of the scanner, to make them feel a little bit less claustrophobic. Functional Magnetic Resonance Imaging is essentially a refinement of that same technique. In this particular case, the magnetic resonance from oxygenated versus deoxygenated blood is used to create an image of the functional activity of the brain. Blood and oxygenated blood, particularly, is necessary to support brain function. So, by measuring the amount of oxygenated versus deoxygenated blood, we can get an idea of areas of activation in the brain. This is used to assess localized brain function, as you can see an image of on the bottom left-hand side. On the top in the middle, you see an example of where that mirror that I just spoke about on top of the head coil is now used to present images so that a person can respond to stimuli. Very often, the person will have buttons in their hand, with which they can make choices, response choices, depending on what type of cognitive paradigm is employed. Diffusion Tensor Imaging is also a refined application of Magnetic Resonance Imaging, and uses all the same equipment, but it measures the directionality and diffusion of water, to generate a particular image. This is particularly used to assess fiber projections and the integrity of white matter. If you recall from our previous modules, white matter forms essentially the axon bundles that facilitates communication between neurons, and they're highly directional. By measuring the directionality of the water molecules within those fiber bundles, we can create an image, as you can see in the bottom right, giving a representation of the structural integrity of white matter, and the projections from one area to the next. Finally, we have Spectroscopy Imaging, which is again a refined application, and uses the same hardware as a Magnetic Resonance Imaging. It measures magnetic signatures of various metabolites in the brain, and it creates a frequency distribution of metabolites for defined locations. As you can see on the top-hand side on the right, a white box is outlined, from which this measurement is taken, and you can see the spectrograph, showing the peaks of certain metabolites that can be detected in that area. This is very commonly used for stroke patients, or in some cases cancer, but it can also be used for research, to figure out which metabolites play an important role, in association with certain brain areas or with certain brain functions. The bottom table shows you a short list of the types of metabolites that can be measured with Spectroscopy Imaging. In the next module, we will go deeper into the basis of the MRI signal, and how the MRI technology is used to create structural as well as functional images of the human nervous system.

Basics of MRI

In the previous module, we discussed several approaches to neuroimaging included EEG, PET and magnetic resonance imaging approaches. In this module, we'll go into more depth on specifically Magnetic resonance imaging, and the basics of the signal that is used to create MRI images. Thermal energy causes protons in our body, and really in any volume, to spin. In a magnetic field, protons assume a state parallel or anti-parallel to the direction of the magnetic field. As you can see on the top right, the protons spins around its own axis as well as around the axis of the magnetic field that it's experiencing. On the bottom left hand side, you can see a situation in which a proton is lying parallel or anti-parallel to the direction of the magnetic field. Those are also referred to as low energy when it's parallel or high energy when it's anti-parallel aligned to the magnetic field. They maintain this gyroscopic motion in the presence of this magnetic field, again, along the axis, the principle axis of the magnetic field often referred to as B0. After a few seconds, the proton reaches equilibrium and spins around the axis, its own axis, and the magnetic field at a steady state. And all the protons in a particular volume, for example, the human brain, are aligned along the same direction, along that same static magnetic field. But you can't measure a static situation. There's no change. And we need change in order to make measurements in changes. The introduction of a small magnetic field perpendicular to the direction of the main magnetic field, B0, causes the precession to move away from the axis. So essentially a radio frequency pulse, a magnetic radio frequency pulse is used to push the spinning proton referred to as a spin out of alignment of the static magnetic field. And this is referred to as excitation. So in the image you can see a 90 degree radio frequency pulse which is enough to push the alignment or to push the spin over by a total of 90 degrees. Once that radio-frequency pulse is stopped, the spin will follow the same typical pre-session motion around the axis of the magnetic field back to its original aligned to state, if you will. So it is progressing at that point from a high energy state that it's achieved through this radio frequency pulse back to a low energy state, parallel to the axis of the magnetic field using this precession motion that you can see in the bottom right hand image. Now further looking at at this precession motion, we can see across the top that there's two aspect of this precession motion that we can measure, two vectors, if you will. On the one hand we can measure the longitudinal or also refer to as T1 relaxation as indicated by the

yellow arrow. So the yellow arrow essentially determines the distance that the precision is off the flat plain, off the xy plain, if you will. So you can imagine that when the spin or when the proton is knocked over by a full 90 degrees that vector is 0, the precision happens fully within the xy plain. And over time as relaxation occurs that vector becomes larger and larger and larger until the proton has fully aligned again with the access of the magnetic field and reach its maximum value. So, again this is referred to as longitudinal or T1 relaxation. And when you plot this out in a graph overtime that basically displays the amount of magnetization overtime, you can see that this signal starts out small and becomes larger and larger. And at a certain point asymptote up to the maximum possible value. The second aspect that we can measure in such a system is the transverse or T2 relaxation, indicated by the blue arrow up top. This indicates the distance in the xy plane. And as you can imagine, as you can see, this factor starts out largest and becomes smaller and smaller and smaller over time as the spin reaches its z axis, if you will, the B0 axis of the magnetic field. So this is a vector that starts out large and becomes smaller and smaller as the spin relaxes. If you plot this out over time you essentially get the inverse of a T1 signal as indicated in the bottom hand graph with a high initial signal, and over time the magnetization decreases with time. Referred to as T2. A free precession of longitudinal or transverse magnetization can induce a signal in a receiver coil. So if a receiver coil is introduced into that environment measuring the transverse or longitudinal relaxation, it generates a current in the receiver coil that can be measured. So the relaxation time is the characteristic time that it takes to spin to recover after being disturbed from equilibrium by this radio frequency pulse. And by measuring this signal, we can make an estimate of the time that it takes and the relaxation that occurs in both the transverse and the longitudinal direction. And it turns out biological matter has different but very consistent T1 and T2 relaxation times, depending on its composition, depending on its fat and water content, if you will. So if you look at the bottom table, varying by field strength of 1.5 Tesla or 3 Tesla, different types of tissue have different T1 or T2 relaxation times. The discovery of this methodology in large part by Dr. Demidian, in the first original paper in 79 actually highlighted these differences and focused on the fact that tumors have yet a different relaxation time than normal tissue. So, for example, in the liver you can see normal relaxation values for T1 versus a tumor which has a very different relaxation value in that structure. The same is true for muscle. So organically, this method was highly sensitive to the detection of tumor in otherwise normal tissue. Taking advantage of this information, we can see that white matter, grey matter, and CSF has different relaxation properties both in the T1 measurement and in the T2 measurement. And taking advantage of these differences in tissue type, we can take lots of relaxation measurements throughout a volume and determine for particular area of the brain, for a particular location in the image, what the relaxation time is and therefore what type of tissue would be present at that location. This would generate the prototypical T1 image that you see on the left-hand side of a brain. In which the CSF appears black or very dark and the gray and white matter show up as variations of gray. And the prototypical T2 image, that shows the CSF as a bright white color and the gray and white matter, again, as different gradations of gray. So again, here you see the contrast between the two. These are the two most commonly used MRI contrasts as they're often referred to. And again, they're based on the different longitudinal or transverse relaxation times that are measured from a proton relaxing after being knocked out of its alignment by a radio frequency pulse, by an excitation known as a radio frequency pulse. Now how do we introduce spacial specificity in this system? How do we know where in the volume that we're imaging, the location of this activation or the location of this magnetization occurs? As I've discussed before,as I mentioned before, precession or spins are in low energy or high energy anti-parallel states. And to change the spin, to change one of these precessions from a low energy to a high energy state, an electromagnetic energy is needed, as I mentioned, the radio frequency pulse. And it turns out that the frequency that is needed to do that is defined by the Larmor frequency as indicated by the formula here. The way to think about it, for example, if you want to try to have somebody on a swing set in your back yard. The optimal weight to make the person go faster is to exact a little bit of energy just at the right moment and do that very, very frequently. The same is true for this particular system. Not just any frequency or any electromagnetic energy will work. If you do it at a certain frequency, it provides the most efficient and optimal transfer of energy into the system, the most efficient excitation of that proton, out of alignment. And again, this is defined by this formula. And, as you can see, the Larmor frequency is expressed in megahertz. And it is defined by the gyromagnetic ratio which is inherent to the tissue that you're dealing with times the strength of the magnetic field. So the magnetic field is essentially the only determinant other than the volume, the type of tissue that you're trying to image that determines the Larmor frequency. And as I mentioned, different particles have very specific gyromagnetic ratios that are well known. So depending on what we're trying to image, an MRI most often water molecules, we know what that gyromagnetic ratio needs to be. We know what the field strength is of the magnet and therefore we know what the Larmor frequency needs to be for excitation. Now then to introduce spatial specificity, we know that the Larmor frequency depends on the local magnetic field strength. So if we introduce a linear gradient which is essentially a small additional magnetic field that is linearly varied in one direction. In combination with the pulse with a center frequency and a defined bandwidth, we can essentially select a specific slice location that we can use for excitation. So by varying the local magnetic field, we can change the frequency in different places in the volume that we're trying to image, in this particular case, again, the brain. And thereby change the location where optimal warmer frequency is used and thereby exciting a specific location in the two dimensional volume as opposed to just the entire volume. You can then change slice selection by either varying the local magnetic gradient or by changing the frequency, exciting a different slice, as you can see in the bottom two images. Either the left, where you're changing the gradient slightly, and thereby changing the slice location. Or by changing the Larmor frequency on the right hand image with the same gradient magnetic field thereby changing the slice location that you're using. So selective excitation provides one dimension of selection, usually in the z direction in space. Distinguishing signals from different location by applying gradient fields is called the frequency encoding. And the gradient field in the third direction which is perpendicular to both of the other two gradients is usually called phase encoding or is called phase encoding. So by making a combination of these three parameters in conjunction with the radio frequency pulse, you can define different imaging sequences or pulse sequences as they are known. And image different aspects of the brain or the volume that you're trying to make a picture of. So then magnetic resonance imaging consists of a static magnetic field that causes precession in a spin system. Linear gradients then are used to cause or create spacial specificity in the x, y, and z direction. The Larmor radio frequency pulse selectively excites a slice based on those settings. And the combination of those gradients and frequency pulses essentially allows for the imaging of any voxel in any direction, as you can see on the bottom right-hand side. So an image is very often defined in xy direction and a voxel, which is essentially a three dimensional pixel, is then also defined by thickness of that slice that you've used. So again, by varying the gradients and excitation and relaxation time, many different pulse sequences possibly can be defined, focusing on many different properties of the volume. On the bottom left-hand side you can see a T1 weighted image that is created that way, or a T2 created image. Or we can use a proton density image, which is yet a different type of image that we can use using varying pulse sequences. So we've come a long way, if you recall from the first module, on the top image is one of the few first MRI images that was generated using a MRI scanner. On the bottom, with refinement of the methods, refinement of with these types of pulse sequences as well as the gradients. We can create very high definition images of the human brain these days, as you can see in the bottom image. So we've come a very long way. So this gives us an overview what the basis is of the MRI signal that is used to create a structural MRI scan. In the next module, we'll discuss the basis of the FMRI signal, the functional MRI signal. 

Basics of fMRI

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?

Structural MRI Studies

In this module we'll be discussing some examples of structural MRI studies. In the previous two modules, we've discussed how the MRI signal is generated. We've talked about protons spinning around their axis, being knocked out of their aligned position by the magnetic field of the MRI scanner. When you remove that stimulation pulse, the proton spins back into its original position, emitting energy that can be measured by radio frequency coils. So you see this procession here in this image, and you can measure two aspects of that procession, the longitudinal and the transverse relaxation. And if you do that and map that out, you can identify tissue classes because CSF grey matter and white matter have different relaxation times providing you with a measurement of what the type of tissue is at that particular location. We've then talked about the introduction of gradients in the x, y, and z direction that allow you to give spatial specificity to those measurements, exciting one slice of the brain at a time, doing a readout for that particular slice, and then moving on to the next slice in the volume. So in the middle you see here an example of these stacked slices and each slice is a particular thickness. That thickness is defined as the volumetric pixel thickness. And if you do a series of these slices, one after another, put them together in a two or three dimensional volume and that provides the basis for your structure MRI scan of which you see an example here on your far right. Now what types of studies are the structural MRI scans used for? Well very fundamentally, we can start with the qualitative evaluation of the structural MRI scan. On the far left side, you see an example of a structural MRI scan of a patient who had a cortical infarction as highlighted by the arrow. So qualitatively, you can evaluate the size and location of the cortical infarction that you see there and compare these with control patients or other patients who've had a similar type of insult to the brain. Next to it you see an example of a brain scan with white matter hyperintensities. These areas of increased signal in the white matter of the brains of these patients. You can quantify them simply by counting the number, or also account for where in the brain you see these types of hyperintensities. And conclude something potentially about the symptoms that a patient might be experiencing as a consequence of these types of white matter hyperintensities. The third image from the left shows an example of a brain scan with traumatic brain

injury in this particular case, a veteran. You see these small areas in the posterior aspect of the brain of shearing, or basically minuscule holes that are in the white and gray matter tissue that are the consequence of brain injury in this particular case, a shock injury. On the far right you see the example of periventricular white matter lesions, these are very common in aging. They occur at the end of the ventricles on the corners of the ventricles, and also appear as these white matter hyperintensities, that in some cases can have some clinical correlates associated with them. So here you're just looking at a qualitative evaluation of the brain scan that you get from a structural scan that can be used to either to diagnose the presence of an abnormality, or in some cases even a disease syndrome. Like Wilson's disease, that is characterized by yyperintensities in the gray and the white matter that you can observe with these T2 or T1 brain scans. But often for research studies, we want to take a more quantitative approach. We want to be able to quantify certain aspects, [COUGH] of the brain anatomy. And one of the simplest approaches is to do a volumetric analysis to determine the volume of a particular structure of the brain that you're interested in. On the left hand side, you can see a highlighted section of the medial temporal lobe, in this particular case, the hippocampus and the surrounding cortices. You can then, as you can see at the bottom of the screen, use a manual segmentation approach in which you use a computer program to manually delineate the structure that you're most interested in, as you can see in the outlines of the different colors. And then essentially count the number of voxels, the scan elements that are within that outlined structure. Multiply that by the volume of the voxel, that depends on the resolution of the scan and the thickness of the slice, which will then give you a volume for that particular structure that you're interested in. So on the left hand side, you see an example of these outlines. On the top right, you see a segmentation approach that used these overlays, these nontransparent colors to define certain structures in the center of the brain. And at the bottom right, you see an example graph of what the results from that could look like. That in the caudate, the hippocampus, and putamen, you could potentially see a pattern of differences between controls and the patient population that you're interested in. So this is a very common approach. This is very frequently used to look at brain structural differences between patients and control cases. These methods have also advanced a little bit further into automated segmentation methods that can be used to answer the same question. On the top left you see an example of a segmentation method that separates out the white matter from the gray matter and the CSF. And similar methods can then be used to quantify each of these tissue classes that you see in the top left. And on the bottom left and on the right, you see an example of an automated segmentation technique in which realignment methods and automated segmentation are used to overlay labels on a large collection of brain structure that we anatomically can define in an individual subject or even in a group of subjects. Similar to what I described before, these labels can then be used to generate volume for each of those different structures both on the left and on the right, and be used for comparison between subjects either patient groups in controls or any type of other comparison that you're interested in. Different methods are employed for T1 weighted MRI scans versus T2 weighted MRI scans depending on the type of question you want to ask for your particular experiment. There are a number of software programs that can be employed that take this automated approach each with its own benefits and downsides. But there are a large number of software packages that can be used to accomplish this very easily using a structural brain scan. Another approach that can be taken is to look at the cortical thickness. We have talked about the cortical folding that occurs in the brain. And if you look at those folds, you can determine the thickness of the gray matter which is the cell bodies of the neurons. And separate it out from the white matter which is largely the axon bundles that communicate information from one location to another. So the gray matter is typically considered the computational processing unit of the brain. So if you want to quantify that specifically, you could look at the cortical thickness of that gray matter by separating out the white matter. So here you see an example of how that's approached. You basically find the boundary between the white matter and gray matter, as well as the gray matter and the outer boundary of the brain. And you create vertices to determine the average thickness of that cortical layer over a particular length of structure. So you could do that for one cortical folding, or you can do it for a stretch of cortex to determine what the average cortical thickness for that region is, quantify that and similarly compare different subject groups on their level of cortical thickness. Here on the left-hand side you see an example of scatter plots on the bottom that are quantified in the bar graphs that you see on top. In this particular case we're dealing with the study cortical thickness in the cingulate, an the area of the brain. And you see the mean cortical thickness there is very different between what they label super ages, middle aged healthy control subjects and elderly controls. And you see a very significant difference in these cingulate in the cortical thickness that these different groups show. So, this is a subset of a structural volumetric approach that focuses exclusively on the thickness of the gray matter, that you can then use for your particular study. Particularly neurodegenerative disorders like Alzheimer's disease and Parkinson's disease, you will see a number of studies focusing on cortical thickness or cortical thinning that is associated with the disease. In the approaches that we've talked about so far, we've looked at a particular area in the brain and then quantify either the volume or the thickness of that area to be able to make comparisons between subject groups. Voxel-based morphometry is a similar but slightly different approach. It focuses on the question of where in the brain there are differences between gray matter density or gray matter concentration. If you don't know for a particular patient group where you would expect the change in cortical volume or cortical thickness, you can use an approach like voxel-based morphometry. Which essentially asks the question, where in the brain are there difference in grey matter or white matter density or concentration between a patient group A and control group B or really any compassion that you can make. The approach that is taken there, you can see on the left-hand side at the bottom of the screen where the original brain scan is segmented, normalized into a central standard space. Segmented into grey matter, white matter and CSF, compare to template models or priors to determine where in the brain differences in density of white matter and grey matter can be observed. On the right hand side, you can see an example of such result where you see these highlighted in red clusters or areas where there is white matter or grey matter differences in volume or in density, is you will, between patient groups and control groups. So here we're focusing on the location of the volumetric differences rather than a preset region of interest that you're focusing on. You can do a similar approach with cortical thickness, obviously, where you're asking the question where in the brain are there cortical thickness differences between patient group A and patient group B or between the patient group and the control group. And you get similar types of results showing areas of cortical thinning, usually identified by colder colors, or cortical thickening, usually indicated by the warmer colors, if that's observable between your control and your patient groups. So, on the left-hand side here you just see an example of such results. All these approaches can obviously also be used in longitudinal structural studies. You can do a longitudinal assessment of cortical and subcortical structure volumes. If you have an area that you're particularly interested in like the hippocampus or the striatum, you can look longitudinally at the volume change by using these manual or automated segmentation methods. You can do the same for cortical thickness, where you assess cortical thickness over time. The longitudinal assessment of tissue class, to see if the proportion of white matter, gray matter and CSF changes over time. As well as structural atrophy basically determining if a structure declines in volume over time, which is most over used by neurodegenerative disorders, like Alzheimer's disease and Parkinson's disease. And finally, you can do an assessment of structural morphology. So here on the right I just want to show you an example of a thickness study that is in children with autism, compared to typically developing children. And in the occipital lobe, you can see over their life span and their early life span, that there is a little bit of a difference between the cortical thickness in the occipital lobe that occurs over time. Although they're certainly overlapping. And there's clearly an age in their teens there were both the healthy typically developing children and the autism children are very similar in their cortical thickness. But the overall pattern over their lifetime certainly seems to differ from typical developing children. So now just a word about structural morphology. Structural morphology is essentially a refinement of the volumetric technique. It is possible of course that the volume of a particular structure does not change between a patient and a control population. But the shape changes either over time or between groups in a way that does not effect the overall volume. So if the shape changes you could possibly detect without being able to detect any volume differences. So, structural morphology is an analysis method to compare shape differences between groups of subjects or shape over time. On the left-hand side, you see an example of shape differences between two groups. At the top, you see the extent or the magnitude of the displacement or the magnitude of the change. And on the bottom is indicated in these different colors of purple, red, and green whether or not the deflection or the change is inward, outward, or the areas where there are no differences. So here you can imagine a situation where the overall volume doesn't change because in certain areas you have inward deflections and other areas have outward deflections that overall cancel each other out. But they're certainly shape changes that happened between subject groups that may be relevant to the symptomatology that these patients or these subjects are displaying. On the right-hand side I'm showing an example of longitudinal morphometry. So you could look over longitudinal period of time if there are shape changes to a particular structure that could be relevant. In this particular case, we're dealing with patients with early Alzheimer's disease. And if you look, the red colors indicate significant shape changes over time. So, this is 0, 6, 12, 18, and 24, month scans. And the red indicates the earliest change. And you'll see the red progressing from the bottom left-hand side to the top right side showing a very clear shaped pattern of changes overtime that could have very significant implications for the behavior that these patients or the impairments that these patients are showing. And this is an example of the shape change in the entorhinal cortex specifically in patients with early Alzheimer's disease. An additional factor that we could introduce in these types of studies is the correlation or the connection between these brain changes, structural, volume, thickness, or morphology, with behavioral measures. So you can do qualitative structural analysis, volumetric, cortical, voxel based morphometry, longitudinal analysis or structural morphology. And you could look at all the results from those studies in the context of certain task performance, or a pathological vulnerability, or an intervention outcome. Does the cortical thickness change as a function of antidepressant treatment? Does the structure or voxel based morphometry change or predict some type of pathological vulnerability? Are patients who are experiencing cortical thinning in mid-life at a greater likelihood of developing Alzheimer's disease than those that do not? Or, more fundamentally, we can look at the correlation between certain brain structure and performance on particular tasks. Are people who have a large hippocampus, particularly good at memory tasks, or are people who have a particularly small frontal lobe poor at tests of executive function? So we can take all the results from these different types of structural scans and put them into context of the behavior that that person displays. And there's a great many examples that we can bring to bear that studies are exactly using this way. I just highlighted a few examples here of interest, title of the paper on the top left shows that brain structures differ between musicians and non musicians. So they compared two groups of expert levels basically, in this particular case musicians. And showed very different brain patterns or volume of structures between these groups, that might be relevant to their musical ability. On the bottom left there's an example of a paper that showed that aerobic exercise training increases brain volume in aging humans. So at the bottom you see an example of the location of the areas in blue and the reddish color that are increasing over time as a result of the aerobic exercise regimen that's been introduced to these older adults. So here we're talking about brain volume increases. And finally, on the right hand side, I'm showing an example of abnormal cortical thickness and brain behavior correlation patterns in individuals with heavy prenatal alcohol exposure. So here's an example where you use MRI structural scanning as an outcome measure to determine what the consequences of a particular intervention, in this particular case, exposure to prenatal alcohol. And at the bottom, you see a correlation between structural changes in the brain, and performance on the work list learning task that seem to be relevant to this patient category. So here's some examples where we take the results from various types of structural imaging scans and relate them to the behavior or the syndrome of behaviors that certain subjects are displayed, that are interest to the scientist. But there's a number of limitations that must be kept in mind when using structural MRI scans in your research. There's a number of reasons why the structure of the brain, or the volume of the brain, can change. Some of those are important to our research questions. Like developmental questions or atrophy questions in terms of neurodegeneration, Alzheimer's disease and Parkinson's disease. But there's also a number of causes of structural brain changes that might not be relevant to your research study. I've just shown you an example where exercise increases the volume of a particular brain structure, and if that's not your research question it'd be important to keep in mind as a covariant. Substance use can also very significantly affect the volume of the brain. This is both positive in terms of prescribe medication that will affect brain structure. For example, a certain anti depressant medication will positively affect the volume of certain brain areas. But also negatively when there's a substance abuse, which often has negative consequences for bringing volume and cortical thickness, such as alcohol or drug abuse. Inflammation or edema, which is essentially a swelling or absorption of fluid into the tissue will cause the brain to increase in size overall and these might also not be relevant to your particular research question. But they certainly could have a significant defect on the outcome. If you have an infection somewhere in your body, it could have an inflammation effect in the brain that could alter the volume of the brain structure that you're interested in. In a way that's not relevant to your research question. And finally, I want to mention gliosis, as I mentioned in a previous module, the brain cells consists of either glial cells or neurons. And the glial cells support the neurons and perform these housekeeping tasks. When the glia is affected by disease or some other type of process, that could very significantly affect the overall volume of that area, without affecting the neurons. So you might be measuring a gliosis disease or a gliosis change, rather than a neuronal change. So if you think that the volume is a consequence of a neurodegenerative disorder, but in fact it only affects the glia, you would potentially draw the wrong conclusion on the basis of that volumetric difference. So there's many reasons why the brain can change in size and these things must be kept in mind when you're doing this type of research using structural methods. In the next module, I'll be talking about some examples of functional MRI studies and how functional MRI is used to answer scientific questions.

Functional MRI Studies

In this module, we are going to discuss some applications of functional MRI studies. And just to review the basis of the MRI signal here, functional MRI is basically an extension of the signal used for structural imaging. In functional imaging, we take advantage of the magnetic properties of oxygenated versus deoxygenated hemoglobin in the blood that supports our brain function. Oxygenated blood provides a stronger signal in the MRI scanner than deoxygenated blood does, as you can see in the middle image here. When we measure the relaxation time associated with oxygenated versus deoxygenated blood, we can see two distinct curves over the course of time that we can use to differentiate an oxygenated versus deoxygenated state locally. On the bottom left hand side, you see an example of a T2 decay, and particularly the T2 star decay represents transverse magnification decay from local fields variations. So these are not dependent on the gradients of the MRI scanner, but these are local field variations that presumably are due to changes in oxygenated versus deoxygenated blood, which in effect represents neural activity. So by focusing on that T2 star decay, we can provide a measure of what we think is cognitive function, or at least brain activity. This approach gives rise to the hemodynamic response function that we've talked about previously. So this is a typical example of a fall and then rise of blood flow to an area of the brain responsible for a certain brain function. And after the activity stops, the BOLD signal, the strength of the signal, drops off again, it undershoots a little bit, and comes back to normal after the blood flow and the blood supply of oxygen has been restored. So these hemodynamic response function is the key response function that we're looking for in the brain representing neural activity. Therefore, it's important to note that BOLD fMRI is not a measure of neural activity directly. It's derived from neural activation, and results from the consumption and replenishment of oxygenated blood to that brain area. So, it's not a direct measure, but it's considered to be a consequence of brain activation. BOLD fMRI is very frequently use as an experimental approach, because it has many advantages. fMRI is non-invasive, unlike single serve recordings, which give us a direct measure of neural activation. BOLD fMRI gives us an approximation of that activation without being invasive. It has a very good spatial resolution, functional MRI of about one to 1.5 millimeters. It has a

good temporal resolution of approximately half a second or greater. And to the best of our knowledge, being exposed to a magnetic field, like a three tesla field, or even a seven tesla magnetic field, does not have any long lasting effects. In some cases, people report a little bit of headache or dizziness in a very strong magnet, like a 7T. But that's passing and usually gone by the time that people are done with the experiment. So there are no long-lasting effects of having an MRI, even having multiple MRIs over the course of your lifetime, unlike, for example, an x-ray examination or a CT examination. Critically, it also allows us to investigate local structural activity associated with the cognitive function, but also brain networks of the entire extent of the brain. So unlike single cell recordings, or other invasive techniques, it allows us to look at the brain as a whole functioning unit, rather than smaller subsections by itself. So it's a very powerful method that allows us to study neuroscience at different levels of analysis, from the smaller approximately one millimeter structures, all the way to whole brain networks. So how then do we do a typical fMRI experiment? One the left-hand side, I'm showing you an example that we've now seen many times of an actual slice through the brain, this is our volume. And the image size is going to partially determine our resolution, the quality of the MRI scan that we're collecting. We're going to layer over top of that a matrix, and we can determine the size of that matrix within certain parameters. If we choose a large matrix, the individual voxels or pixels here will be rather large, and the resolution of our resulting scan will be somewhat low. If we overlay a small matrix, we have a higher resolution, we have more information from that same image size, but obviously, it's going to take longer to acquire that amount of detail. So, depending on the needs of our experiment, we can choose our matrix size and determine the resolution, the detail of the data, that we're going to collect. Then if we look at one of these individual voxels, just three dimensional pixel, we can look at the activation at a certain point in time. So let's say that this person in this experiment is not doing anything particular, the activation from that area of the brain should be somewhat random. So at a point in time, we can take a measurement, which gives us a certain level of activation for that area, measured by the T2 star relaxation time, and we can do this repeatedly over the course of a time period. So every few seconds we take a scan, which is called TR, time repetition, the amount of time it takes the scanner to acquire a new image. For that one voxel, we then take a measurement of the T2 star decay, which gives us activation over time for this particular voxel. And this, at the bottom of your screen, what you see is referred to as a time series. So this is an activation level for that one particular voxel indicated in the image here, over the course of the length of the scan. Now, you essentially do this for every single voxel in your volume, so imagine that you do this for all the voxels that you see in this matrix here. And then in the third dimension, the extent of your volume gives you a great number of these time series, these activation patterns for each and every one of those voxels. So we collect a tremendous amount of data by just doing fMRI experiments. So let's look at a very specific of an fMRI experiment, in its simplest form, you can think about a finger tapping experiment. Let's say, as an investigator, you're interested in finding out which area of the brain is responsible for being able to finger tap, literally as simple as tapping your index finger and your thumb together. We would place a willing participant in the scanner, ask them to hold their fingers still for a period of time, and then start tapping for a period of time, and then we'll ask them to stop. We'll start to continue again, and rest again, and we do this for a number of occasions over time. We measure for each and every one of the voxels in our volume, the activity associated with that finger tapping task, which would give you a time series roughly as what you would see on the bottom. And the idea here is that if an area of the brain is responsible for that finger tapping, activation in that voxel, or in that group of voxels Will be higher for the tapping conditions than it would be for the rest conditions. And you can see some evidence for that in the time surge at the bottom of your screen. So initially during rest activation, it's somewhat low. When tapping commences, the activation rises a little bit. For the second block of tapping, you see a very robust response of activation, and towards the end, you see another sort of block of activation corresponding to the tapping on condition. So here you would see a situation in which you measure fMRI signal that is representative of the task that you're asking the participant to do. Now then, how does this activation that we see at the bottom here, this time series, correspond to the hemodynamic response function that we've talked about? Well if we zoom in a little bit at one of these areas of high activation, we essentially expect that while the person is continuing to finger tap, the area of the brain responsible for supporting that motor movement is continuously producing hemodynamic response functions where there's increased influx of oxygenated blood to that area. If you can concatenate those hemodynamic response functions, you can see a somewhat box curve, as you see at the bottom of your screen here, that is very similar to the on off condition of the experiment. And obviously, they're offset in time a little bit because the hemodynamic response is somewhat slow, as we've talked about previously. But basically, the concatenation of the hemodynamic response functions results in these high areas of activation for the on condition relative to the off condition. And then the question obviously becomes, of all the voxels in our brain or in the volume that we're scanning, where do we see this pattern of low and high activation that corresponds to the onset times and the stop times of the rest conditions and the finger tapping conditions? Once we find that voxel or more often a cluster of voxels, we can conclude that that area of the brain is responsible for finger tapping. And in this particular case, it's obviously in the area of activation, we see in the motor cortex in this particular case, the right motor cortex. So this is a simplest form of an fMRI experiment where you used this hemodynamic response function in a block design to activate an area of the brain responsible for this finger tapping task. And block design are a very commonly used design in fMRI research. Because there's many advantages to using a block design. It's also sometimes refered to as an epoch design. They're simple to design and implement. You have an on condition and an off condition and you typically just alternate the two. The analysis of this data is very straightforward. You have a block of time in the off condition and a block of time in the on condition and you contrast activity between the two. It results in very robust activation typically because you have a large number of events that are causing a hemodynamic response. So the overall fMRI activity typically is very high in a blocked condition like this. It's also robust to the uncertainty and timing and shape of the hemodynamic response function. Let's say, for example, there's some variation in the length of the hemodynamic response function or the steepness of the hemodynamic response function in a particular area of the brain. A block design is somewhat insensitive to that because it aggregates over a number of hemodynamic response functions and just contrasts high activity conditions with the low activity conditions. So the individual differences in hemodynamic response functions for each finger tap are less consequential in this type of design. But as you can imagine, there's obviously also some downsides to the block design. You have to assume for this to work that there's a single mode of activation of a constant level over time. If I'm asking a person to continuously finger tap, but activity in response to the second and third or fourth finger tap is significantly different from the first, a block design would not necessarily work. You need a constant level of activity to each and every one of the different stimuli in order for a block design to successfully pick up areas of high activity versus low activity. It is also limited in the sense that you cannot infer anything about the individual events within a block. If you do a block design, you typically compare on to off as we've said, but you would not be able to differentiate activity in response to the first finger tap versus the fifth or the sixth within a block. It treats the entire on condition as one single activation. And sometimes that's a limit depending on the type of question that you have for your experiment. It also cannot infer anything as we've just discussed in the positive even though it's robust against variations in the hemodynamic response function. It also does not allow you to look at individual variations in the hemodynamic response function. So if a particular area of the brain shows a slightly different hemodynamic response function or different timing of it, you would no be able to pick that up with a block design like this. An alternative then to a block design is an event related design where some of these things are possible. So at the top I'm showing you a schematic representation of the block design. You have on and the off conditions as we've talked about. You multiply that with the hemodynamic response function that you expect and you get these areas of high activity and low activity temporally offset relative to the on and off task conditions. An event related design works very similar but the events are much shorter and usually consist of one trial. So for example, it would be one finger tap. Each individual event is spaced from the others in variable intervals of time. The expected hemodynamic response function remains the same, but because of the spacing, you would get a different accumulation of the fMRI signal of the time series than you would get from block design. So at the bottom right hand side, I'm showing you an example of what that expected signal would look like in an event related experimental design. Now how then do we differentiate these individual events from that accumulated or that accumulative signal? You can do that by essentially defining a single equation for each time point of your stimulus onset, multiply that by the function that you would expect to see based on the hemodynamic response function. And essentially deconvolve that complex signal into individual events associated with the timing onset of the stimulus that you're interested in. So in the middle shows you that schematic representation of what the individual events look like relative to a block design and an event related design. And one of the biggest advantages in this design is that it allows you to make inferences or draw conclusions about individual events in s series. For example, in the right hand side you can see a series of pictures. You could ask for each individual stimulus what the response to that item is and what the representation or the activation in the brain is to each of those stimuli separately. There's several other advantages to event related designs in fMRI. It allows for inferences about the timing of neural activation, like I said, these individual trials can be considered separately. It allows for very flexible analysis of the data including post-hoc trial sorting. So if you want to combine different trial types together into a single event type, or you want to reorganize things temporally, an event related design allows you to do that whereas a block design does not. And when you space the events of the trials sort of randomly apart from each other, you make sure that you don't confound the next trial or a series of trials after that. There are several disadvantages of event related designs as well. It does have a reduced ability to detect changes that you would get from a block design. Because it's only an individual event, activation and response to these individual events are typically a lot smaller than you would get from a series of events using a block design. It's very sensitive to errors in the hemodynamic response function, so if there's variability in the hemodynamic response function, an event related design is much more sensitive to those changes. And there can be refractory effects that carry over from one trial into the next. So the response to the first trial could affect the response to the second trial in certain conditions, and if that's important, it could confound the results that you're dealing with as well. So what we've now seen from both the block design and the event related design is that there are areas of on activity or high activity that we contrast with time periods of low activity or no activity. The tapping versus rest, and in the event related design, certain stimuli versus not having any stimuli. This is referred to as the cognitive subtraction method, which is a critical component of fMRI research. If you look at a time series, you see that there's always activation in the brain. So it's not that we have to look for areas in the brain where there's activity and in other areas there's no activity at all. We have to do a comparison between two contrasting conditions to see what the difference in activation is, that we can then assign to a cognitive function that we're interested in. This is also known as a categorical design, it assumes that different cognitive components are independent in space. For example, that memory occurs predominately in the hippocampus, that motor control predominately occurs in the motor strip. That these are spatially separate, and that we can dissociate them from each other. It also assumes, based on a principle proposed by Donders in 1868, the Pure Insertion Principle, which processes in complex conditions are simply added on top of simpler or baseline conditions. So, for example, if I do a complex executive function type task, and I have to tap a button to give my answer, that the simple motor task is just laid on top of the executive function task, which presumably happens somewhere else in the brain. And that that's not any different from the button press that occurs with a simple arithmetic task or a simple language task. So, it assumes that behavior and cognition is somewhat modularly organized, and that complexer tasks are just a combination of simpler modules that we can discern on this fMRI signal. The key, then is to subtract activation during a control task from activation during the on or experimental task. And the assumption there is that the difference only shows activity related to the cognitive construct of interest. So on the right hand side, I'm showing you an example that is very frequently used, of a flickering checkered pattern, which is used to activate the occipital cortex, important for vision. The checkered board pattern comes on at a certain frequency and they turn it off for a period of rest. They turn it back on for a period of stimulation, and you compare the two activity levels for these two conditions. And in the top right, you can see the off state, and if you look carefully in the bottom brain image there, you will see some intenser signal in the occipital lobe on the very bottom side of that brain, which presumably corresponds to the on condition of this flicker pattern. But we know from experimental design that subtraction of two conditions is only valid if the condition is only varied in one property. If the difference between the two experimental conditions is more than one property, we're going to have difficulty concluding which one of those two was responsible for the experimental manipulation that we enforced. So any factor that covaries with the independent variable is going to be a confounding factor and that must be controlled for. So experiments that typically use rest or not doing anything at all as a comparison condition or a control condition are actually a fairly poor approach. At the bottom, I'm showing you an example of a paper where that was explicitly tested, that using a baseline of not doing anything or a baseline of rest is actually a very poor control condition. It turns out that during rest, there's quite a number of things that are going on. We're uncoding our current experience, our current location, but there's often also the case of mind wandering. What's going to come next, how did I do on this task that I just finished? I need to remember to pick up a gallon of milk on the way home. There's quite a bit of cognitive activity that goes on when we're supposedly not doing anything, that using such a rest period as a controlled condition is actually a very poor design. It is a much stronger approach to have active contrast between condition A and condition B, and not use rest at all. So, for example, in a study where you're interested in an area of the brain that is responsible for processing faces, you could imagine a situation where the on condition would consist of pictures of faces that you show to people. And the control condition would be a similar image, merely a scrambled version of that face. So, that all the visual information, the contrast, the light gray, the light dark shading, everything remains the same between this two stimuli. The only difference is that the one constitutes a picture of a face, and the other one does not, allowing you to look for areas of the brain that are particularly sensitive to faces, pictures of faces, or faces in general. And there is such an area, and it's called, the fusiform face area. Alternatively, you could expand a little bit on this experiment and make a combination of faces, objects, and scrambled versions of those two. Allowing you to contrast the area of the brain responsible for faces, or for objects, and comparing both of those against the condition on which the same information is there, merely scrambled in a non-sensical stimulus. And at the bottom, you can see some activation that results from this type of approach. Important to remember from these slides is that the experimental design and the design of your fMRI test is critically important for the conclusions that you're going to be able to draw from the resulting activation. So in the next module, I'm going to step through a number of other factors, that should be critically considered when you're designing an fMRI experiment.

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