That Neuroscience Guy - The Tools That Neuroscientists Use

Episode Date: May 28, 2025

In today's epsiode of That Neuroscience Guy, we discuss the tools that neuroscientists use to study the brain. 

Hi, my name is Olof Kugolson. I'm a neuroscientist at the University of Victoria. And in my spare time, I'm that neuroscience guy. Welcome to the podcast. So we've been really paying attention to what you want to know about the neuroscience of daily life. So make sure you reach out to us through Instagram. You can message us there at that Neuroscienceguy. You can also get us at thatneuroscienceguy at gmail.com

threads and X at thatneuroscienceguy. So please reach out and tell us what you want to know today. Listener request. We've had a lot of listeners sort of ask about the tools that we use as neuroscientists. So how do we measure brain activity? Like I keep telling you this brain region does this and that brain region does that. And you know, back in season one and two, we did some in-depth episodes on the tools,

but they got a little technical. So on today's podcast, I'm just going to give you an overview of the tools of neuroscience. So the most common ones that are used tell you a little bit about how they work and what they measure and give you an example of each. And it's just basically so if you hear about these tools, you have some idea as to what they are. All right, let's do it. So when you're looking inside the brain, we have what we call primary and secondary measures. So primary measures are measures that actually measure brain activity or something directly related to brain activity,

like EEG or fMRI. And secondary measures are things like eye tracking that doesn't actually measure brain activity, but it infers brain activity. So I'm gonna take you through the five most common primary measures and then a couple of secondary measures and that'll give you the overview of the tools that neuroscientists use. So when we think

about puring inside the brain the holy grail if you will is space and time. Some measurements are highly accurate in terms of spatial resolution. So you know exactly where in the brain something is happening. And some measures are better at time, so temporal resolution. So their spatial resolution won't be as good as their temporal resolution. Now, the most accurate method is the one I'll start with,

which is single unit recordings. So in a single unit recording, you're basically putting a probe into a single neuron. And when this technique started, the probe would go into a cluster of neurons, but you know, now with advances in technology, you're able to put a probe, and by a probe I basically mean like a voltage detector in a sense, and you put it into a single neuron and you can tell whether or not that neuron is firing. So you're measuring the electrical activity of that neuron and you know whether it fires

or not. And of course, what you do with these kind of things is you, a really good example is some of the work in visual cortex where they will show a stimulus, imagine a horizontal line, and they'll find a neuron that fires to the presence of a horizontal line, but it won't fire to the presence of a vertical line. And then they'll keep searching through visual cortex and they'll find a neuron that fires to a vertical line, but not to a horizontal line. Now the reason I say this is the most accurate technique

is because you have space in time. You know exactly where you are in the brain and you know exactly when things are firing. And what you get is an output that's called a raster plot. It's basically a little dot on a trace anytime that neuron fires. And you're literally measuring individual neurons and you know exactly where they are and you when they are. So that's single unit recording. Not that common in humans because you have to get a wire into the brain. It has been done,

typically it's done in pre-surgical patients. So a patient that is about to undergo surgery and they will, they, you know, so they've literally in brain surgery, of course you've got the skull open and they will do that. Most commonly done in animal models, well, they'll put probes directly into the brain. So that's single unit recordings, measuring individual neurons and what they're sensitive to.

One last example is if you put one in the primary sensory cortex, imagine you've got a muscle spindle that represents your left biceps muscle. If that muscle spindle contracts, that neuron will fire and you'll measure that via a single unit recording. Okay, measure number two, another primary measure, EEG, electroencephalography. This is kind of my shtick, if you will. It's what we, you know, I've done the most of throughout my career. I've talked about it a lot, but at the end of the day, we're talking about brain waves. You put electrodes on the surface of the head and you're measuring the firing of

neurons within the brain and more specifically populations of neurons. You can't isolate single neurons. And for those of you that are really into this, you're not actually measuring action potentials. You're measuring excitatory and inhibitory post-synaptic potentials. Now, we've done a full episode on EEG, so I won't go into it in too much depth. But the two ways it's commonly used is what we call continuous EEG. That's when you're measuring EEG continuously, hence the name. You might be doing this to track sleep. You might be doing this to look at steady state activity, so meditation, what's going on when someone's watching a movie, and you're just

looking at... Typically this EEG is analyzed in terms of frequency, so you hear about theta power and alpha power. You these are common ones if I was measuring continuous EEG over the front of your head and I got you to count down by 13 from 10,000 you'd see an increase in theta oscillations these are oscillations between 4 and 7 Hertz over the prefrontal cortex. If I put electrodes more towards the back of your head over the parietal regions and I got you to relax, I'd see an increase in alpha power. The other way EEG is used are what we call event-related potentials. Basically it's the brain's response to a stimulus. So imagine I play the following tones to you.

Beep, beep, beep, beep, boop, beep, beep, beep, beep, boop, beep, beep. So that's what we call an oddball pattern. And if you look at the brain's response to the onset of those sounds, you'll see more activity for the boop, because it's rare compared to the beep. And you can, those components mean different things. In my lab, because it's rare compared to the beep. And those components mean different things. In my lab, we look a lot of these things. We look at something called the reward positivity, which is an ERP component sensitive to reward processing.

We look at something called the P300. We've done a lot of work, which I've talked about, where the P300's been shown to be sensitive to cognitive fatigue and other factors. So that's EEG. Now in terms of spatial and temporal, excellent temporal resolution, millisecond accuracy, very bad spatial resolution. Just because you see activity under a given electrode doesn't mean that's where in the brain the activity is coming from. Next on the list, MRI and fMRI, magnetic resonance imaging and functional magnetic resonance

imaging. Well, an MRI scan most people are familiar with, you're basically doing a, it's a contrast. It's got, you know, I did a whole episode on MRI, so I'll refer you there to get into the nuts and bolts. But you're basically looking at a contrast between different types of tissue. So in the brain, white matter versus gray matter, but obviously the skull, the ventricles, everything, the contrasts are different so you can see them. MRI is typically used to look for abnormalities in the body. So if, so if you have a tumor, for instance, in the brain, it would show up on an MRI scan.

If you tear your ACL, your anterior cruciate ligament in your knee, you could see that in an MRI scan, which is why after injury or suspicion of internal issues, you do an MRI. fMRI, or functional magnetic magnetic resonance imaging is specific to the brain and you're looking in changes in blood flow essentially, the hemodynamic response. And the logic is pretty simple, which is if you have more neurons fire for some reason, you need more blood to recharge the neurons and you can see that with fMRI. So a classic fMRI study is if you show people a series of emotional images, imagine cute little puppy dogs and kittens versus neutral images, I

don't know a series of rocks maybe, and you contrast those images you typically would see in this case more activity in the amygdala, our old friend for the emotional images versus the non-emotional images. So fMRI is used to look at activity in different parts of the brain. You know, if you go back to contracting your biceps muscle, if you do this in the fMRI scanner, you will see activity over the motor cortex, representation of the biceps muscle. The problem with FMRI is its excellent spatial resolution, down to the millimeter.

The temporal resolution is very poor. The hemodynamic response takes about three to eight seconds to materialize. So it's not perfect. Excellent spatial, poor temporal. Next tool that I will talk about is functional near infrared spectroscopy, or FNIRS. This is one of the newer tools. It's one I'm quite excited about. Basically, you're measuring the hemodynamic response

just as you are with an fMRI, but technically it's quite different. In fMRI, you basically are talking about hydrogen, knocking hydrogen atoms out of alignment with a magnetic field and a radio pulse. With FNIRs, you're actually firing an infrared beam into the brain. And the way the spectroscopy part works is, again, with the hemodynamic response and specifically

the oxy-deoxy-heboglobin ratio. And you can detect changes in that with FNIRs. So if we go back to the example I used with EEG, if I had you count down from 10,000 by 13s or 7s, I forget what I said, and you were measuring FNIRs over the prefrontal cortex, you would see an increase in blood flow because it's a challenging task. The prefrontal cortex will engage and more blood flow is needed, and again you would infer something from that. One of my PhD students published a paper recently looking at this issue and basically showed that the more cognitive load that there is, so the harder you're thinking, the larger the change in the hemodynamic response over the prefrontal cortex.

So great way to peer at brain function. Now I want to get into a couple of secondary measures. So these are not directly measuring brain activity, but they are inferring brain activity. Common one is the GSR, the galvanic skin response. Basically you put a sensor on the palm of the hand and you're measuring sweat. GSR is typically used in emotional paradigms.

Neuroimaging emotion is actually tricky, but one thing you can do is put a GSR sensor on the palm of the hand. And if you go back to my example I used with the puppy dogs versus the rocks, if you do have an emotional response to puppy dogs you would get a larger GSR response or literally an increase in sweat. Now this is a very minute change in sweat. It would be almost imperceptible if you

were touching the palm of the hand but it is there. It's not the most reliable technique in the world. A lot of people talk about they have trouble replicating famous GSR studies, but it has been used to measure changes in sweat, if you will, which gets you at insight, typically into emotion, stress, anxiety, things like that. Another secondary measure that's used a lot, one that I've used a lot myself is eye tracking.

Again, you're not directly measuring brain activity. Eye tracking is what you think it is. You're basically, you've got a device, it's either a camera or another, the other way to do it is video tape or video recording. Just dated myself there. But with eye tracking, you're basically looking at where the eyes are looking, and you try to infer brain activity from that.

So if you actually went on Google Images and just put in eye tracking and say neuroscience or psychology, inevitably you'll find an eye tracking diagram that is like, it's a picture of like, I don't know, two people talking. And what they do is they'll show you where people spend most of their time looking. And typically in images, people spend, like if you look at an image of a person,

you spend most of the time looking at their face and very little time looking at their body So eye tracking can be used to sort of infer to some extent what people are thinking just by figuring out what they're looking at There has been some work done with pupil dilation, which is how big your pupils are Again people draw a lot of meaning out of pupil dilation, but the reality is if you're interested in what's going on in the brain, you should measure brain activity and not try to infer it from eye tracking. But it is a measure that neuroscientists use. We use it in conjunction with other measures,

so eye tracking combined with EEG gets you some very interesting data. The last one I'll talk about, it's not even really a, I wouldn't even call it a neuroscience measure, it's more of a kinesiology measure, but it's motion capture. So motion capture is basically again what you think it is. You're capturing the position of the body in space. You can do this with active sensors where they're actually emitting a source that a camera detects or you can use passive sensors which is your cameras pickup specific little they're basically little silver balls that you stick on the body. Jen our social media coordinator is gonna post

a link to a really cool motion capture. It's actually a point light display, but you'll get the idea. I'd check it out, it's gonna be on Instagram. Because it's interesting, one of the cool things, I'll just preview it a bit, is by changing certain features of movement, you can infer things. So you can infer happiness by the gait pattern,

like how people walk insane for sadness. So people have used motion capture to study the way the body moves and try to draw some insight into brain function from that. So those are the classic measures, the single unit recordings, EEG, fMRI, MRI, FNIRS, GSRI, tracking and motion capture. The last thing I'll mention quickly, which is probably the biggest advance in neuroscience as being the, we use math better now. When I started doing EEG research, we were recording from 64 channels of data, so 64 electrodes across the head.

We were recording for say 20 minutes and we were sampling at a thousand hertz, so a thousand samples a second. So we had this incredible amount of data, like millions and millions of data points, and we would reduce that to a single number. So we would say that all of that data can be averaged out to one number that represents

something. Now with the advent of machine learning and neural networks, we are analyzing data differently. So I'd say the other tool that neuroscientists really rely on now is math, machine learning and neural networks. You know, if you're a student in the Craig Olson lab, you know, your math skills are just as important as your neuroscience skills. In fact, when I gave my first TEDx talk,

I sort of joked about that because people think that as a neuroscientist, I spend most of my time looking at brains and things like that. The reality is I spend most of my time in front of a computer, writing computer programs to improve the way we analyze the data that we get. Anyway, those are the tools that neuroscientists use. Hopefully that's a good reference guide for you. Coming up in a couple days, Misophonia. We're going to look at the neuroscience of Misophonia. If you don't know what

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Thank you so much for listening. And if you haven't already, please subscribe. My name is Olof Krogolson and I'm that neuroscience guy. I'll see you soon for another full episode of the podcast. Thanks for listening.