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fmri-for-beginners

A course designed for anyone entering the field of fMRI-based cognitive neuroscience. It covers both the theory behind how we analyze the data and technical skills/knowledge to apply right away. This course is designed for those who are new to the field, but contains useful resources for all skill levels.

What is an MRI and how does it work?

MRI stands for Magnetic Resonance Imaging, which probably doesn’t mean much to you now but will by the end of this module!

If you bet that I was going to actually be the one writing all of this material, then you would probably lose that bet. This article does a good job at explaining how an MRI works and if a lot of it doesn’t make sense, that is okay. You do not need to be an MRI physicist by the end of this but just have a basic idea of how we go from putting a person in the scanner to looking a pretty image of their brain:

Magnetic resonance imaging (MRI) (article) - Khan Academy

Now that you have read some about how an MRI works, these videos are great for reinforcing the concepts through helpful animations since not everyone intuitively imagines what proton precessions look like(by not everyone, I mean no one). The videos below are in order of increasing level of detail(aka the top is easiest and the bottom is the hardest) since there will be people with different levels of background knowledge.

Is it the MRI way or the highway?

To make a long story short, no. MRI is a very useful imaging modality since it does not expose the subject to radiation like some other imaging modalities such as PET, CT, and X-Ray. Not only that, but it offers a good combination of spatial resolution and temporal resolution.

While MRI is a very versatile tool for imaging, some other important imaging methods such as EEG, MEG, and PET scans are also used and each has their own pros and cons(as previewed in the graphic below). As you continue to learn, you will see that some imaging methods are better suited for some types of studies.

Pro Con List of imaging methdos

Image from:

Mendez Orellana, Carolina. (2015). Functional MRI of Language Processing and Recovery. https://www.researchgate.net/publication/295856844_Functional_MRI_of_Language_Processing_and_Recovery.

Signal To Noise Ratio (SNR)

This is a concept important enough to get its own section, but I will preface it with this is one of the more difficult topics for some.

So when we are measuring the intensity of the voxel, what we are able to read is not always the “true” or “exact” value since our measuring tools are not 100% accurate and there are other factors that can cause changes to the intensity we measure. We call the measurements that we take the observed signal.

SNR is a representative of the magnitude of the observed signal(the stuff we are interested in, which in our case is the different intensity created by different bodily tissues) versus the magnitude of the noise(potentially caused by factors that could change the intensity of voxels like subject movement, magnetic field interruptions, etc.). I know most of you are not signal processing experts, but the image below does a good job at showing what this looks like for a 2D wave.

In this example, we measured the line on the left. It has a similar structure to the true signal shown on the top right, but if we went to the same X coordinate on the observed signal and true signal, we would get slightly different values. This is due to the noise - the noise introduces slight variations. Think of the observed signal as the signal+noise.

Image showing observed signal decomposed into signal and noise Image from https://www.predig.com/whitepaper/reducing-signal-noise-practice

So why do we care about SNR? Because we want to know that what we are seeing is actually from the signal we are measuring(brain activity) and not from random noise (which would mean the results are meaningless).

How do we even calculate SNR from an image? For most purposes, you will use a premade program to do this for you. There are equations to calculate this, but to spare your brain, I will leave it out of this introduction.

I am not going to go more in depth on this topic, but it is important to have a good understanding of if you are going to be managing neuroimaging data. Many of the steps in the preprocessing of MRIs is done to improve SNR so we can be more confident in our results.

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