In the previous module, we've talked about the basic design of an fMRI experiment. In this module, I'd like to talk a little bit about things to consider when you're designing an fMRI task. The goal of the fMRI experiment typically is to use stimuli to induce a certain psychological state that you are interested in. Then use the MR signal to measure brain activity associated with that psychological state to be able to draw conclusions about where and how in the brain this psychological process is supported. In the previous modules I've talked about this example, which is a very simple example of an fMRI experiment. Here, obviously the psychological state of interest is motor control of a finger tapping. We compare an active finger tapping condition with a passive rest condition and the instructions here merely at the beginning of each block of tapping is to start tapping your finger or stop tapping your finger at the beginning of a rest state by comparing brain activation between those two conditions, we can localize and examine the brain activation that is responsible for performing this task. But obviously, we can do many more complicated types of design using more sophisticated cognitive processes. Memory executive function, vision, all types of domains that we've talked about previously. I'd like to talk a little bit about factors that you should consider when designing an experiment and they can be grossly subdivided into technical limitations, psychological state considerations, and finally the statistical design of your paradigm of your experiment. So in talking about technical limitations, we have to consider the constrained circumstances in which we collect fMRI data. Obviously, the subject is positioned in fairly tight quarters in the bore of the scanner and often the stimuli are presented using a projector outside of the MRI room as to not interfere with the magnetic signal and project the image from outside of the room onto a screen that's within the bore of the fMRI scanner and the subjects can see that screen through a mirror that's mounted on top of the head coil, as you can see a little bit from the image here on the right hand side. The subject is then given button boxes in their hands either in both hands or on one hand, which are often limited to only three maybe four buttons in each hand. So within that constrained environment, we have to remember that this visual stimuli on the screen typically need to be fairly large to be able to be seen through this mirror and that our response options are limited to three or four options of show because that's the maximum number of buttons a participant has available. We also should consider the stimulus timing and the length of the stimulus. So we've talked about block design and event related design before. When we do an event related design we have to make sure that the timing or the amount of time between two stimuli is somewhat varied. If we don't do that, we get a fixed interval and not enough variability in the MR signal to be able to what's called deconvolve the signal and assign a single hemodynamic response to an individual stimulus. Instead, we need to vary the spacing between the stimuli so that we have more variation in the overall signal, which allows us to reliably assign a hemodynamic response to the onset of a stimulus. So we need to consider the experimental design in terms of timing of the presentation of the various stimuli that we want to include in our experiment. We also should consider the number of stimuli that we can present at a time. Typically, an experiment is designed or subdivided into individual runs and a run will consist of a certain number of stimuli. Runs should typically be limited to four to six minutes or so. And this is because of scanner drift. Due to thermal energy and the continuous operation of the scanner, the scanner will heat up ever so slightly. This increased thermal energy will change the magnetic field, the measurements that can be taken from the MR scanner, which potentially could influence the results if you were to compare stimuli at the very end of your run from stimuli at the beginning of your run. So typically runs are limited to four to six minutes or so and then stopped so that the scanner has time to cool back down to normalize again if you will before the next run is started. So when you design your experiment you have to make sure that your stimuli, the sequence of stimuli can be subdivided into these individual runs. One should also consider the overall length of the experiments. You can certainly run a number of runs one after another. But the participant is in a somewhat uncomfortable environment, it is difficult to communicate with the technician due to the loud noise of the scanner and they're constrained in this small scanner tube. So typically you want to limit experiments to about 60 minutes or so or less in order to give the participant the opportunity to come back out of the scanner and distraction and be a little bit more comfortable. So both in terms of the number of runs and the overall length of the experiment, one should consider the physical constraints and the technical limitations of the scanner setup. Another technical limitation to consider is subject motion. Obviously when we're doing measurements at the millimeter scale, any type of motion by the subject will significantly interfere with our measurements. On the right hand side, you can see the green, blue and red lines indicating the amount of translation or the amount of shifting that this particular person did in the MRI scanner and you can see that sometimes it is several millimeters. If our voxel size is about a millimeter to a millimeter and a half, that could significantly change the measurements that we're trying to get from our experiment. The bottom side shows you a similar problem with rotations, so any type of rotation of the volume within the magnetic field will interfere with our measurements. The very top shows an example of motion in a structural scan which causes blurriness and the same is true for functional MRI scans that will have this blurriness as a result of motion that will decrease your ability to detect signals related to your active and your rest conditions. So it's very important to control for motion as much as possible which typically means constraining the participants using padding next to their head, which makes the overall experience unfortunately less comfortable. So again the length of your overall experiment should be considered and reduce the likelihood that motion becomes a significant factor in your experiment or in your results. When we're talking about psychological state, there's a number of things to carefully consider when you're designing your experiment. The key obviously is to induce the psychological state that we're interested in. So with our design, we have to ask the question if we're successfully doing that. For example, is the subject effectively engaged in the intended task? Imagine a situation in which a participant is presented with a stimulus and asked to determine if they've seen this before in the context of the experiment i.e. is the item new or is it old. Here I'm showing an example of a Rubik's cube and imagine the total trial length is about three seconds or so when we're measuring the signal associated with this recollection. But this particular participant is able to retrieve that information within the first 500 milliseconds or so. After 500 milliseconds, the participant presses the correct button, we then still have about 2,500 milliseconds before this trial is over. And it is very likely that the participant starts to think about things not related to the experiment. For example, he could recall that he was given a Rubik's cube for his birthday when he was about four or five years old, the person is wondering where that Rubik's cube is right now, whether or not he would still be able to find it; all things that are not relevant to the psychological state that you're trying to measure. So when designing an experiment it's very important to consider how long the psychological state lasts in relation to the length of your trial. The second thing to consider is whether the subject is employing a shortcut or a strategy to solve the problem. I'm showing an example here of a Go-no-Go task in which a participant is shown a series of letters on the screen. The participant is asked to press the button as quickly as they possibly can for each letter that they see except for the letter X at which point they should not press the button. So this is a task that measures response inhibition, the ability to not press a button when you've been doing that for a while. Now imagine that there's a certain order to these stimuli. For example, the X occurs as every fourth stimulus or the X occurs after each S presentation, the letter S is seen. This makes the sequence predictable at which point the cognitive state is no longer about response inhibition but about anticipation and predictability. So when organizing the sequence of your stimuli, one should consider very carefully the shortcuts or strategies that can be employed to solve the question at hand. How long does the psychological state last is a very important factor to consider. The complexity of the cognitive task should be considered. Some cognitive problems are much more involved and complicated and take more time to resolve than others. If you are then comparing a simple cognitive task with a complex cognitive task, the amount of time that the person needs to solve that problem can be different, which could influence your results. This is particularly true for emotional responses which are typically slow. If your goal is to induce an angry state or a happy state or a sad state, those typically take longer than cognitive processes and they're more difficult to switch, so you can imagine after you've induced an angry state, it is much more difficult then to use another stimulus to then induce a happy state. You need a certain amount of time to pass between these types of trials in order for that to go back to normal. So it's very important to consider how long the elicited psychological state lasts in the context of the trial length that you're designing. Finally, does the stimulus reduce or induce unintended psychological states? You can imagine a situation in which a person is engaged in a complex cognitive task and maybe they don't know the answer to each of the questions being asked or that they feel that they're not performing the task very well, that person might get frustrated, might be anxious about whether or not they're doing the task right or whether or not they will be judged for not doing well. And at that point you're essentially measuring a state of frustration or irritability or even anger rather than the cognitive process that you're interested in your experiment. So it's important to consider the unintended psychological states that could be induced from your experimental design when you're designing your experiment. Moving on now to some of the statistical considerations that we should keep in mind. Obviously, the important questions are going to be what are the dependent and independent variables here? Will independent variables have multiple levels and how will the stimuli be organized? Will we be using a block design or an event-related design or maybe a combination thereof. Very commonly used experimental designs in fMRI studies are the subtraction design, which we've talked about a little bit before in the previous module, a factorial design in which we include multiple levels of an independent variable, a parametric design in individual differences approach or an outcome measures design, and we'll step through each of these to give you a little bit of an example for each of these types of design. But the fundamental trade-off for all of them is always that when you use fewer conditions in a simpler experimental design, you have better power but you have less ability to be specific about your conclusions and less able to generalize your results to other types of conditions. When you use a more complicated design with multiple levels or more conditions, typically it results in worse power but you do have a better potential to make inferences across your different types of stimuli. So that's the trade-off when you're designing your experiment that one should keep in mind. We've talked about the subtraction design before. Subtracting activation during a controlled condition from activation during the active or experimental condition, and the idea is that the difference between the two shows activation in an area that is important or supports the cognitive state or the psychological state that you're trying to measure. This works particularly well in cognitive processes where the cognitive process is considered to be categorical. So for example, I'm showing an example here of the famous faces task in which the experimenter is interested in the brain area and manner in which the brain supports recognition of familiar faces. You can imagine an example where familiar faces are shown and the activation during those familiar faces is compared with activation in response to unknown faces and again the difference should then relate to our ability to recognize familiar faces. In a factorial design, we allow for multiple levels of a single factor to be included in the experiment. And this is particularly important when we're interested in the interaction between two types of conditions. So here I'm showing an example of stimuli where we use curvilinear spaces of curvilinear environments and rectangular environments that are either open spaces or more enclosed spaces and they're also varied by the level of high ceilings and low ceilings. So here we have multiple factors that are embedded in these types of stimuli and full factorial design allows us to look at activation that is important for the interactions between these types of conditions. Now the downside for the full factorial design is that sometimes the results that come from these interactions are very difficult to interpret. But if the interaction is of particular interest, a factorial design is the only way to approach your research question. In a parametric design, localized activity varies as a result of difficulty or cognitive demand. So typically what you do is you use a task that has multiple levels of difficulty such as the N-Back task shown here at the bottom of the screen. In the N-Back task, a participant is shown a black diamond that has a number in it as you can see here and in the 0-back condition, the participant is asked to press the number corresponding to the number that they see on the screen. So in the far left stimulus, the person would press the button number one. In the 1-back condition, they see a similar series of black triangles with numbers in them. And during this condition, the participant is asked to press the number corresponding to the number they saw on the previous slide. So for the first slide, they would not press anything but when the second slide comes up they would press the number corresponding to the one on the first slide. And you can imagine, you can increase the working memory load for this problem by increasing the number of 2-back steps if you will. So in the 2-back condition, you have to press the number corresponding to two slides ago, again increasing the working memory load. The problem here is that the downside of this type of experimental design is that psychological states may not always be linear as is assumed in this particular case. You can imagine a situation in which if a 4-back or a 5-back condition is employed, participants are simply not able to solve that problem and activity would be non-existent rather than higher because of the 4-back or 5-back condition. So there is a point at which there's optimal arousal in response to the difficulty after which things are likely to drop off and could interfere with the outcome of your experiment. In an individual differences design, individual behavioral performance is considered and correlated with the activation that is generated from the fMRI results. So here I'm showing an example of our own work where brain activation caudate in the hippocampus was established or was measured during a spatial navigation task and the individual subject's behavior performance on the recall tasks was correlated with their activation. So each dot here represents an individual subject and you can see a nice correlation between behavioral performance or task success if you will, and the level of activation in these two brain structures. So here we're making a connection between the behavior that an individual subject is displaying and the brain activation. And finally in terms of outcome measures design, we can use fMRI experiments to test the effect of a particular intervention. This is referred to as a within-subject design in which a person gets into the scanner twice, once before an intervention and the second time after the intervention and we can use the difference between the pre and the post test to examine the effect of that intervention. And these interventions can span a wide range of things. Obviously, drug development is an important factor where we look at the effect of a particular drug on brain function and on cognitive function. We can also use practice or training as an intervention to see what the effects are. There have been a number of studies that used exercise as an intervention method to see what effect exercise has on the brain. Or you can talk about other types of clinical intervention like deep brain stimulation which is often used in Parkinson's disease to control the motor symptoms, transmagnetic stimulation where an external magnetic field is applied to a specific area of the brain to try to alter the electrical activity in that area of the brain or ECT, electroconvulsive therapy, which is commonly used for depression. So you can do a pre scan, do the intervention, and then do a post scan to determine using fMRI what the effect is of those types of interventions. Finally, briefly because we've already talked about this, it's important to consider how this stimuli will be designed within your experiment. We've talked about the block design in which a series of stimuli are organized within a block and that is compared then with the rest condition. The N-back is an example of a block design because you need to provide the instruction at the beginning of a block and then have a series of stimuli that all have the same instruction before you move on to the 2-back or the 3-back. But you can in other situations use an event related design if you have questions about the individual stimulus. For example, the recollection of the Rubik's cube that I showed before or do a hybrid between the two, where pseudo randomly organized events are organized in blocks so that you can analyze the data either as a block design or an event-related design depending on the question that you have. What I have hopefully made clear is that there are a number of factors to consider when designing your experiment. I've only highlighted a few and there's many more that one should think about when designing an experiment, which takes quite a bit of research and practice and experience to be able to do. Careful experimental design is critical and selection of the experimental control conditions is critical for the outcome of the study. So one should really focus a significant amount of time considering all these factors and testing out their experiment before employing it in the fMRI scanner and the MRI scanner. In the next module, I'm going to talk about a specific case of functional magnetic resonance imaging which is called resting state connectivity analysis.
