by Laura Naysmith
Since the first human scan on 3rd July 1977, magnetic resonance imaging (MRI) has been the method of choice in neuroscience as it provides detailed images to illustrate the structure and function of the living brain. Using MRI, we can map the brain’s current state. New scans can be compared to previous scans to detect changes in brain structure over time. Consequently, MRI is used as an important diagnostic tool for detecting tumours, brain damage from strokes or head trauma, or for measuring cortical atrophy as brain cells degenerate and “shrink” from diseases such as Alzheimer’s disease and multiple sclerosis (MS).
As well as structure, MRI can also measure brain function; this technique is known as functional magnetic resonance imaging (fMRI). In neuroscientific research, fMRI presents an opportunity to investigate how specific brain regions function in response to an experimental task. For example, we could design an fMRI experiment to explore which brain regions are involved in hand movement. During the scan, the participant is given a ball to squeeze in their right hand. The instructions are presented on a screen of when they are to squeeze the ball and when they are to ‘rest’ (not squeeze the ball). Brain activity during the squeezing movement can be precisely mapped. In this example, we would expect to see brain activity increase in the opposite left hemisphere (as the ball is squeezed in the right hand relative to when the hand is resting) in an area known as the motor cortex, a frontal brain region involved in planning and producing movement. Therefore, fMRI is an important and powerful neuroimaging tool for providing insight into brain function.
However, fMRI has its weaknesses. One of the main critiques of fMRI is that it measures brain function indirectly, this is to say it does not provide a causal link between brain activity and the task. This is important as it means we cannot conclude with certainty that the brain activity we see when the ball is squeezed is a direct result of the movement, or some other factor, such as brain function relating to reading the instructions off the screen, the loud hammering noise of the MRI scanner, or simply daydreaming. To provide more certainty, timing is critical. If we can confirm the exact moment the participant squeezed the ball, then we can lock brain activity to the hand movement.
One promising approach is to combine another technique with fMRI to better measure the movement, specifically to observe the movement of the muscle. Electromyography (EMG) is an electrophysiological method which measures muscle movement using small electrodes which are attached to the skin. In the current example, we would place electrodes on the forearm during the fMRI experiment so that when the ball is squeezed, the tiny burst of electrical activity produced by the contracting muscle can be detected, recorded, and timed by EMG. The EMG measure reflects the whole muscle contraction. Measuring muscle movement with EMG during an fMRI scan means we can precisely time-lock the person’s movement with their brain activity. This allows researchers to draw stronger conclusions about brain function, in relation to movement in a task, than possible with fMRI alone.
Simultaneous EMG-fMRI, the combined use of EMG and fMRI in experiments is beneficial for research, such as in sport science, but also in clinical research. EMG can be used to detect tremors in movement disorders such as Parkinson’s disease. As disease-related tremors can occur alongside voluntary muscle movements, EMG can detect tremors and differentiate brain activity relating to normal muscle movement, such as squeezing the ball, from involuntary pathological tremors. Therefore, combining EMG with fMRI allows for greater exploration of movement disorders, combining deterioration of the brain and muscle movement which is important for early detection of movement disorders, resulting in a better diagnosis and earlier intervention.
Although there are clear advantages of simultaneous EMG-fMRI, there are also challenges. Firstly, as EMG detects the tiny electrical activity from the muscles, any other external source of electrical activity can interfere with and even mask the muscle signal. The biggest nuisance signal comes from the MRI scanner itself. The MRI scanner core contains a powerful magnet (measured in Tesla), and the field of a typical 3 Tesla (also called 3T) scanner is ~60,000 times stronger than the earth’s magnetic field. During an fMRI experiment, the combination of this strong magnetic field and the radiofrequency pulses emitted by the scanner can mask the tiny EMG muscle signal, making it difficult to detect. In addition, electrical power sources, such as lights and fans inside the MRI scanner room, can further affect the EMG signal and should therefore be turned off during the experiment.
While technically challenging, removing these sources will provide a clean EMG signal and reveal the activity of the muscle movement. Once the experiment has finished, different data techniques can “filter” and remove the unwanted signal from the MRI scanner. Fortunately, MRI has a distinct electrical frequency signature (measured in Hz, hertz) which can be subtracted to reveal the true burst of electrical activity from the muscle.
Overall, functional magnetic resonance imaging (fMRI) is a powerful method to explore brain function. However, fMRI has been criticised for its lack of direct measure, its inability to precisely pinpoint the cause of a change in neural activity. This means that conclusions drawn about the relationship between brain activity and an experimental task are never definite and are open to question, as observed brain activity could mistakenly be related to other processes in the brain which are unrelated to the experimental task. However, fMRI reinforced with a robust behavioural measure, such as electromyography (EMG), offers an insightful and powerful way to investigate brain function and movement more precisely. There are logistical challenges to combining EMG with fMRI, but the benefits of this combined approach far outweigh the challenges.
fMRI: Dewis, L. (2019). https://www.open.edu/openlearn/body-mind/health/health-sciences/how-fmri-works
EMG: Farnsworth, B. (2018). https://imotions.com/blog/electromyography-101/