- We are going to explore fMRI (functional magnetic resonance imaging) machines.
- First, we will provide a functional magnetic resonance imaging definition.
- Then, we will delve into the world of fMRI scans, discussing various examples and describing how fMRI machines work.
- We will briefly discuss deactivations and hypoactivation in functional magnetic resonance imaging.
- Finally, we will provide an evaluation of fMRIs, analysing the advantages and disadvantages of functional magnetic resonance imaging.
Fig. 1: fMRI machines are valuable in medicine¹.
Functional Magnetic Resonance Imaging Definition
A magnetic resonance imaging (MRI) machine uses magnetic fields and radio waves to gain an inside look into the body. The patient is placed in a tube-shaped machine that uses incredibly powerful electromagnets to scan the body and brain. The results can then be used by researchers and medical professionals to understand any abnormalities.
So, what is an fMRI?
Functional magnetic resonance imaging (fMRI) scans are a form of magnetic resonance imaging, able to identify areas of function by showing active areas of the brain. fMRI scans are able to do this because they can detect blood property changes, which are then linked to active or underactive areas of the brain.
How does Functional Magnetic Resonance Imaging Work?
Functional magnetic resonance imaging detects changes in blood oxygenation in the brain, the flow of which brain activity affects. When an area of the brain is more active because the participant or patient being observed is doing something, such as working on a task, or because of damage, the brain's blood flow will increase or decrease based on oxygen demand.
This process is referred to as hyperactivation (more) or hypoactivation (less). Hyperactivation can be detected on fMRI scans when areas of the brain are highlighted in red and hypoactivation is indicated by blue areas.
Haemoglobin supplies oxygen to neurones. When these neurones activate, the increased activity must be balanced by providing the necessary oxygen and blood flow to make this possible, as well as providing energy in the form of glucose.
Neurones need energy, too!
Blood with a higher oxygen concentration is affected differently by magnetic fields than blood with lower oxygen content. An fMRI magnetic field can detect this when scanning the participant or patient. This is called the BOLD (Blood oxygenation level-dependent) signal or theory and is primarily responsible for how an fMRI identifies functional areas.
An fMRI will then map the activated areas using voxels (when creating a 3D image of the brain, a voxel unit represents a tiny portion of brain tissue in the image), producing neural images.
The highlighted areas are active parts of the brain.
For example, certain areas of the brain will show up when a person is working on a memory task.
Fig. 2: An fMRI scan during working memory tasks reveals active areas of the brain.
Interestingly, a participant or patient must not speak or otherwise communicate when thinking about a task or answering a question. They have to answer it internally to prevent the brain from activating in other areas.
Suppose the participant answers a question about a memory task out loud. In that case, the motor cortex (getting the body and muscles to speak) and the language areas (Broca’s and Wernicke’s areas) could activate and deactivate, interfering with the results.
If a participant is working on a memory task, but other brain areas are also ‘firing’ up, it would be nearly impossible to assign a function to one area of the brain with certainty.
Furthermore, when analysing the results, it would be difficult to pinpoint areas suffering functional loss due to damage if other parts of the brain are also hyperactivating and deactivating during a task.
fMRI Psychology Examples
A good example of the use of fMRI in research is a study by Downing et al. (2001), in which they used fMRI to assign a function to specific brain regions
- There is evidence that the human visual cortex regions respond specifically and selectively to faces.
- Downing et al. (2001) wanted to find out if this was also true for other regions responding to human body images and not just to faces.
- They found that cortical regions in the brain do indeed respond selectively to images of the human body, particularly the lateral occipitotemporal cortex, and that a specialised neural system exists for visual perception of the human body.
- The use of fMRI made all these discoveries possible!
Similarly, Haxby et al. (2001) studied the architecture of the object visual pathway in the brain using fMRI.
- They measured the ventral temporal cortex patterns while subjects looked at faces, cats, nonsensical pictures, and artificial objects.
- They found distinct pattern responses for each category.
- Overall, they found that representations of faces and objects in the ventral temporal cortex were widely distributed and overlapping.
- They identified these functional areas thanks to the use of fMRI.
By using this brain scanning technique to identify potential functional areas, we can say that certain behaviours could be due to these functional areas. We can assume that an area of the brain that ‘lights up’, so to speak, correlates with the actions and behaviours of the individual, especially if we are careful in our experiments of isolating specific stimuli.
So when someone is confronted with frightening visual stimuli and certain areas of the brain activate, such as the amygdala, we can see that area of the brain being associated with a particular response. The amygdala is where our fight-or-flight response begins. With techniques like this, we can determine this in certain situations and attribute a ‘fight-or-flight’ behaviour to the amygdala!
Functional Magnetic Resonance Evaluation
What are the advantages of functional magnetic resonance imaging? How about its weaknesses in studying the brain? We need to examine the various fMRI strengths and weaknesses.
Advantages of Functional Magnetic Resonance Imaging
First, let's explore the advantages of using fMRI scans.
Non-invasive: An fMRI does not involve inserting anything into the brain or cutting open the head to look at the brain itself. It provides a view of the brain and its activities without invasive techniques.
Virtually no associated risks: Because fMRI does not require any of the invasive techniques mentioned above, it is already safer than those techniques. It also does not use radiation, used in other brain-scanning techniques such as the PET scan (positron emission tomography).
Clearly illustrates localisation: Neuroimages show clear areas of activity related to the patient’s or participant’s activity and are robust in studies that focus on examining a specific function, limiting confounding variables.
Helps prepare for surgery: If a patient needs surgery, fMRI is valuable beforehand to map areas needing attention to better prepare and navigate efficiently during surgery.
High spatial resolution: It provides a detailed image and is extremely accurate.
Disadvantages of Functional Magnetic Resonance Imaging
Now, let's examine the disadvantages of using fMRI scans.
Expensive: Operating an fMRI machine is quite costly, both in training and the machine itself.
Stillness required: A participant or patient must remain still while scanning in the machine, severely limiting the type of research with this method. They cannot move, respond properly, or perform tasks that require movement, as this would compromise the results or make scanning impossible altogether.
Blood flow is difficult to interpret: Because an fMRI only detects changes in blood flow, it can only tell you if an area is active or not. It does not tell you why the neurone in question is activated, nor does it tell you anything beyond changes in blood flow. The neurone itself can be activated for various reasons, with different tiny functions controlled by the primary function. Therefore, it is impossible to determine the cause and effect.
Some areas also light up for multiple reasons. Certain areas of the brain are responsible for reactions that can be opposite, especially when it comes to emotional responses.
Low temporal resolution: There is a slight delay, usually about five seconds, before changes in blood flow and activity levels within a neurone are detected, so fMRI has a poor temporal resolution.
Functional Magnetic Resonance - Key takeaways
- An fMRI (functional magnetic resonance imaging) is a neuroimaging technique used to map the brain.
- It detects changes in blood flow occurring when the brain is performing a task and can use a magnetic field to create a 3D image of the brain with highlighted areas of activity.
- Haemoglobin is responsible for transporting oxygen to neurones in the brain, which require increased blood flow during activity. The fMRI detects these changes (blood oxygenation level-dependent signal), and we can infer function based on increased activation in the brain.
- An fMRI is non-invasive, virtually risk-free and has a high spatial resolution. It is a great aid in finding localised areas of function.
- However, it has a low temporal resolution, is quite expensive, and requires the patient to remain still to obtain an accurate image. This aspect severely limits the types of research for which fMRI can provide results.
References
- Fig. 1: fMRI machine by Thomas Angus, Imperial College London, CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0, via Wikimedia Commons
How we ensure our content is accurate and trustworthy?
At StudySmarter, we have created a learning platform that serves millions of students. Meet
the people who work hard to deliver fact based content as well as making sure it is verified.
Content Creation Process:
Lily Hulatt is a Digital Content Specialist with over three years of experience in content strategy and curriculum design. She gained her PhD in English Literature from Durham University in 2022, taught in Durham University’s English Studies Department, and has contributed to a number of publications. Lily specialises in English Literature, English Language, History, and Philosophy.
Get to know Lily
Content Quality Monitored by:
Gabriel Freitas is an AI Engineer with a solid experience in software development, machine learning algorithms, and generative AI, including large language models’ (LLMs) applications. Graduated in Electrical Engineering at the University of São Paulo, he is currently pursuing an MSc in Computer Engineering at the University of Campinas, specializing in machine learning topics. Gabriel has a strong background in software engineering and has worked on projects involving computer vision, embedded AI, and LLM applications.
Get to know Gabriel