Neuroimaging tools offer a lot of information by providing insight into the structure and function of the nervous system. The concept of functional neuroimaging involves creating several images of the brain to identify changes over time. Neuroimaging allows researchers to analyze the structure, function and pharmacology of the brain. The techniques and methods vary based on the research goals, but some neuroimaging tools are becoming more mainstream for commercial use. It’s important to have conversations about the drawbacks and limitations of neuroimaging, since the technology continues to advance as researchers seek out best practices for understanding the brain. Productive discussions about benefits and limitations promote good ideas to help make improvements and really find out if one champions the rest.
Neuroimaging can be divided into two approaches of exploring neural firing: a direct measure recording electrical activity and an indirect measurement which subscribes to the assumption increased blood flow and metabolic responses are a result of neural activity (Bunge & Kahn, 2009). Electroencephalography (EEG) and magnetoencephalography (MEG) are techniques which directly measure electrical activity in the brain, while methods such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (NIRS) detect increased blood flow and metabolic activity as a means of indirectly measuring brain activity. Reviewing the technology available for a comprehensive understanding of the various approaches to viewing brain activity gives insight into current capabilities, as well as conceptualizes how the field can progress. Diving into both direct and indirect measures of neuroimaging will help determine which (if any) of these tools are best applicable to certain research designs.
Straight Shot: Direct Measures of Neuroimaging
EEG is valued for its ability to record neural activity in real time. While it is sometimes debated if EEG qualifies as neuroimaging since it does not take a snapshot of the brain, the technology can provide a graphical representation of brain activity allowing it to qualify as a neuroimaging modality. Furthermore, advanced quantitative EEG (qEEG) provides a visual representation of neurofeedback (Figure 1). How does it work? The net flow of electrical current is determined using electrical dipoles which helps to give a global context of different brain states (Bunge & Kahn, 2009). Similarly, the electrical activity occurring in the brain is used to determine the magnetic field during an MEG (Figure 2). The scan both detects and amplifies the magnetic signals and develops a magnetic source image which shows any abnormal activity in the brain. MEG and EEG can have high temporal sampling rates, with MEG reaching as fast as 1200 samples per second (hertz) and EEG being anywhere from 250-2000 hertz, depending on the type of headset used (Boto et al., 2019). Commercial-grade EEG headsets are available to the public with prices ranging from as little as $200 to well over $25,000. The prices vary based on how many electrodes are included in the cap. MEG scanners cost upwards of 2 million dollars each, and renting the equipment is an hourly rate of a few hundred dollars. While cheaper EEG sets may be tempting to utilize, it is important to consider the quality of the output if limited sensors are used.
Figure 1: A pictorial representation of brain activity being mapped via qEEG from Penrod (2018).
Source localization, or knowing where the signal is coming from, is a big drawback when using direct neuroimaging measures. EEG and MEG struggle to isolate the precise origin of the signal. Using an academic-grade EEG cap permits more sampling from neurons than MEG; however, it struggles to get a clear signal due to the interaction with the skull and scalp. Additionally, the mesh cap required for an EEG cannot have the muscles move around the head because it increases inaccurate data referred to as artifact. Magnetic fields are unaffected by the skull and scalp; thus, making MEG a better option for localization between the two direct measures, but not by much. The sensitivity to poor spatial resolution is hard to resolve; however, if the research aims to achieve a global understanding of the brain with a strong temporal reading, either EEG or MEG may be the preferred neuroimaging option.
Figure 2: MEG set up for recording magnetic fields to explore brain activity.
Winding Around for a Winning Way: Indirect Measures of Neuroimaging
Indirect brain imaging involves a few different approaches. The BOLD response (blood oxygen level dependent) is the standard technique used in fNIRS and MRI technology. When neurons need oxygen to be replenished, as messages are being communicated throughout the body, a protein in our blood called hemoglobin delivers the oxygen to neural activation sites. This type of response is referred to as a hemodynamic response (Bunge & Kahn, 2009). Measuring levels of oxygenated hemoglobin is collected by both MRI and fNIRS; however, it is in different ways. The MRI records the magnetic field difference when blood changes from oxygenated to deoxygenated, while NIRS reports on cerebral oxygenation, blood flow and metabolic activity of regions in the brain by reviewing the absorption of light.
The fNIRS methodology came along in 1977 by Frans Jöbsis at Duke University. Professor Jöbsis measured oxygen levels to analyze neural activity and hemodynamic responses (Quaresima & Ferrari, 2019). Light is used in fNIRS to gain information about blood volume, flow and oxygenation by either being absorbed into, transmitted through, reflected off, or scattered into a medium (such as skin, bones, etc.). Optical technology sends infrared light into the tissue and reports on the light that is scattered back. The difference between the original intensity of the light emitted compared to the amount returned gives insight into concentration of oxygenated and deoxygenated blood and brain activity levels (Quaresima & Ferrari, 2019). This methodology detects concentration changes in light absorption (aka the amount of oxygenated or deoxygenated blood at that moment). By obtaining concentrations over time, the images project neural activation responding to stimuli which results in an increased blood flow to the activated area. A compelling reason to use fNIRS is also due to its ability to differentiate between deoxygenated and oxygenated blood; however, it is accomplished through differences in optical properties.
Figure 3: An example of an fNIRS set-up with the cap.
Other indirect imaging includes PET scans which work by detecting gamma rays via a radioactive tracer. PET scans provide visual information about biochemical changes in neurotransmitters through the metabolic activity of cells in body tissue. However, PET scans are extremely expensive, have poor temporal resolution and require a radioactive injection (Bunge & Kahn, 2009). Due to the need to remain completely still during this test, certain populations may not be the best candidates, such as children or patients with uncontrollable movement (i.e. Parkinson’s disease). The motion tolerance obstacles hold true for fMRI as well, since it requires participants to remain completely stationary in a narrow tube, sometimes leading to the onset of anxiety, dizziness or claustrophobia. The fMRI scan creates images by using magnetic fields and pulses of radio wave energy. Since the fMRI acts as a giant magnet, it is also unsafe for individuals with implants. The fNIRS method is an up-and-coming technology to make neuroimaging a more naturalistic and comfortable experience (Figure 3). Optode sensors on fNIRS technology are intentionally tight on the scalp, to minimize movement, yet it still allows participants to fidget or walk without major interruptions to the recording (Quaresima & Ferrari, 2019). The freedom to stand during an fNIRS procedure opens doors for the exploratory neurofeedback of more realistic and interactive research.
Let’s Get Deep: The Truth about Neuroimaging
While the expansions in neuroimaging technology are very exciting, the limitations of what can actually be accomplished must be at the forefront of any research conversation. Neuroscientists are often excited by the concept of mapping the brain as a means to link neural activity to subjective experiences (i.e. emotions). It does sound enticing to use a neuroimaging scan to suggest certain mechanisms are associated with XYZ, but it can undermine the complexity of the brain. Overemphasizing one mechanism’s function can make the idea of mindreading seem not too far off, but to be clear: no measure discussed in this blog has the ability to read anyone’s mind. It is important to recall how areas of the brain have multiple uses which may result in contradictory functions. Activation and inhibition are constantly occurring in the brain for a multitude of reasons that may or may not include the emotional stimuli being researched. Additionally, variability among individuals makes it even more challenging to promote such claims. These tools have a space to truly give unique insights into the brain’s interconnectedness, but researchers must be cautioned to not rely on any one tool to give the full picture… (pun intended).
Additionally, collecting data may be hindered based on the neuroimaging tool used. Specific brain areas, depending on the equipment being used, are much harder to read than others due to penetration depth. For example, fNIRS can only read roughly 1.5 cm into the cortex. Places such as the forehead and top of the head are easiest to get signals from with fNIRS, but it cannot reach deeper brain areas such as the cingulate cortex or the olfactory cortex (Quaresima & Ferrari, 2019). The MRI scan can measure deep brain structures that fNIRS is unable to achieve without major artifact. EEG is also capable of having signal depth of the whole brain; however, it can easily be clouded by noise and electrical crosstalk. EEG has more flexibility than MEG in terms of recording capabilities, since MEG requires the recording activity to be parallel to the surface of the brain, limiting where information is picked up (Boto, 2019). PET scans can also retrieve information encompassing the whole brain; however, images can be misinterpreted based on how the tracer reacts to inflammatory conditions, high blood sugar and small tumors.
Among the Neuroimaging Nominees—Who’s the Winner?
While comparing the different potential methodologies, each technology has a lot of limitations and benefits. The PET scan is the most expensive and invasive protocol discussed, requiring additional compensation for its nuclear component. The MEG data easily merges with anatomical fMRI or EEG scans to give a comprehensive analysis of brain activity; however, MEG lacks versatility to measure different head shapes and explore naturalistic paradigms (Boto et al., 2019). Although fMRI has similar restraints, it can indicate complex patterns of neuroclassification, activation trends across populations and determine engrossing stimuli. It also has greater signal depth and special resolution compared to fNIRS. Due to the optical technology, fNIRS can only evaluate the surface, therefore having a fast temporal reaction to fMRI. Yet, the BOLD response is slower than EEG, which analyzes electrical impulses in muscle activity, but has much higher sensitivity to noise. fMRI is also prone to statistical biases and noise from the machine, or brain activity can corrupt the data.
For multimodality studies, fNIRS may be the best option due to its portability and cost-efficient characteristics. The fNIRS machine has relatively few accessories and is easily transported. Additionally, fNRIS is an excellent option to test challenging populations such as infants, people with implants, or those with special needs. The ability to move around also affords a naturalistic experimental paradigm where participants can be embedded in real scenarios rather than conformed to a tube. Improvements to fNIRS are still being engineered to make the headgear lighter and more comfortable. Some obstacles when trying to record with fNIRS include running participants with thicker and darker hair, like dreads, because it interferes with the sensor reading. The back of participants’ heads, where hair is most dense, can also be a challenging area to get a strong sensor reading. fNIRS is also easily integrated with fMRI, EEG, PET or event-related potential to compensate for the lack of anatomical information and spatial resolution (Quaresima & Ferrari, 2019).
Deciding among the various neuroimaging scans truly does depend on the type of research being conducted. Many oncology patients must undergo a PET scan as a means of learning the status of potential tumors, while other populations may be hesitant to undergo imaging involving radiation. Furthermore, if the research is seeking a similar tool to fMRI, but has a smaller budget, fNRIS may be a better fit. The cost of neuroimaging can be a major deterrent, making equipment such as fMRI, PET and MEG less feasible in consumer research. By asking questions not only about the neuroimaging applications, but also context of the research question, determining the correct approach will emerge. Neuroimaging technology is progressing and becoming more prevalent by expanding from universities and hospitals to industries such as marketing, entertainment, public health and communications. The expansion into new disciplines encourages refining existing methods while also increasing opportunities to critically think about the value of the data compared to the expense of the research. Making sure the new technological advancements bring additional value to the project is imperative to ensure sophisticated analysis of the data that is validated and interpreted with confidence.
References:
Boto, E., Seedat, Z. A., Holmes, N., Leggett, J., Hill, R. M., Roberts, G., … & Barnes, G. R. (2019). Wearable neuroimaging: combining and contrasting magnetoencephalography and electroencephalography. NeuroImage, 201, 116099.
Bunge, S. A., & Kahn, I. (2009). Cognition: An overview of neuroimaging techniques.
Penrod, J. M. (2018). Innovating the Mind: Three Essays on Technology, Society, and Consumer Neuroscience (Doctoral dissertation, Virginia Tech).
Quaresima, V., & Ferrari, M. (2019, September). A mini-review on functional near-infrared spectroscopy (fNIRS): where do we stand, and where should we go?. In Photonics (Vol. 6, No. 3, p. 87). Multidisciplinary Digital Publishing Institute.
