3 Magnetic Resonance Imaging

fMRI

The Physiological Basis of fMRI

On average, the brain makes up for 5% of a human’s body weight but it uses 20% of the body’s energy. This is because neural activity is incredibly demanding from a metabolic perspective. As such, a well-established relationship exists between energy metabolism and blood supply in the brain that fMRI capitalizes on to acquire its images.

During a task, oxygenated blood rushes to the brain to supply regions with enough nutrients to meet the energy demands of increased brain activity (Nippert et al., 2018). This relationship is called neurovascular coupling (De Silva and Faraci, 2017). Because hemoglobin (the primary carrier of oxygen) is diamagnetic (very slightly repelled by the magnetic field) when oxygenated and paramagnetic (attracted to the magnetic field) when deoxygenated, changes in the blood-oxygen-level-dependent (BOLD) signal are detectable by fMRI (Buxton et al., 2004). The decay rate of the magnetic signal is increased by deoxyhemoglobin in blood vessels (Buxton, 2013). Therefore, the influx of oxygenated blood from activity leads to an increased ratio of oxy- to deoxy-genated hemoglobin, giving it a stronger signal compared to surrounding tissue.

But does the BOLD signal look the same in everyone? Gauthier et al. (2013) asked an important question: can we interpret fMRI results for older and younger individuals equally? The argument is made that metabolic and vascular changes associated with aging will influence the coupling between the neuronal activity and the hemodynamic response that is picked up by the BOLD signal. Evidence suggests that BOLD responses may represent a larger change in neuronal activity in older individuals than the same BOLD response in a younger individual.

Further, it is important to ask if the changes observed in fMRI can be observed in all tissue types? The answer to that question has been an ongoing debate in the field of cognitive neuroscience. fMRI has previously been assumed to be insufficient to detect white matter activation, and only used to explore the neuronal-dense grey matter regions. However, Gawryluk et al. (2014) has made a case for the use of fMRI to study white matter by presenting evidence of hemodynamic changes during various tasks. However, much work remains to be done to understand the relationship between structural white matter changes and functional impairments, as well as improving the assessment and diagnosis of connectivity disorders (Gawryluk et al., 2014).  

Acquisition and Experimental Design

Analysis and Software Packages

An important concept in fMRI analysis is cognitive subtraction. This process involves comparing the activation in a region of interest during a task of interest to a control task. For example, an individual may be shown images of faces as part of a facial processing experiment. The activation will be interesting, but ultimately meaningless unless it can be shown that the recorded activation is associated with the task and is not merely intrinsic activity. As such, the participant may also be shown non-facial visual stimuli (e.g., a picture of fruit) or of a warped/scrambled version of facial stimuli. Next, the fMRI signal recorded while the participant completed the facial processing task will be subtracted from the control visual task. This resulting activity is the difference observed by the facial processing task and is the activity of interest.  Some software that is often used for this analysis is BrainVoyager and FSL.  

Applications and Limitations of fMRI

fMRI is very helpful for localizing regions during activity and exploring neuroplasticity (changes to and reorganization of the nervous system; Soares et al., 2016). However, what is its clinical utility? Currently, there is only one clinical use of fMRI: presurgical mapping. When an individual requires neurosurgery, presurgical mapping is conducted to identify functional regions bordering a tumor (Stevens et al., 2016). This is pertinent for optimizing surgical planning and reducing likelihood of a surgeon removing functionally necessary tissue.

It is important to recognize that there are numerous challenges and limitations of fMRI work. fMRI studies frequently have limited sample sizes and a high number of comparisons, meaning they have lower statistical power. One of the main reasons fMRI studies frequently have small sample sizes is that they are very expensive experiments to run. Participants need to be compensated, the MR machine needs to be booked in a slot (which can be hard to do if one scanner is used for both research and clinical purposes), and everyone working on the project needs to be compensated. This includes technicians, the research team, interns, and potentially others depending on the study. As such, there is a growing movement in the field of cognitive neuroscience to share brain imaging data once it has been acquired. This allows more studies to be completed using data, proving to be a more cost-effective and accessible option. 

Another limitation of this modality is that fMRI data are highly susceptible to artifacts (discrepancies in the recorded image from the actual participant). These can be caused by metallic objects (e.g., hair ties), hardware malfunctions, or when the scanner doesn’t know where a signal is coming from and misidentifies the signal as coming from two places (a process known as ghosting).  

Principles of Diffusion Tensor Imaging (DTI)

What is Diffusion? 

Diffusion tensor imaging (DTI) is another noninvasive neuroimaging tool. This variant of diffusion-weighted imaging (DWI) MRI produces images by recording tissue water diffusion rates (Ranzenberger & Snyder, 2022). DWI refers to the contrast of the images, whereas DTI refers to the type of modeling applied to the images that measures the degree of anisotropy and structural orientation.

Water molecules are profuse in the brain. What makes them interesting to examine is that their diffusion is heterogeneous. Depending on the type of tissue and architectural differences, water molecules in the brain diffuse differently. As such, we can infer various properties at the voxel level, including the molecular diffusion rate (Mean Diffusivity; MD) and we can evaluate white matter fiber tracts (Fractional Anisotropy; FA(Kaplan, 2011) though not the direction of the bundles.

Axons measure approximately 1/1000 mm in width. Currently we do not have the technology to image individual axons, but with DTI we can image groups of axons that form white matter bundles. 

There are different ways water molecules can move. If the molecules are moving equally in all directions, this is known as isotropic diffusion. Conversely, anisotropic diffusion refers to water molecules moving in a directional way instead of randomly. This tends to occur along white matter fibers (Ranzenberger & Snyder, 2022). The use of individual voxels’ directional information paired with anisotropic diffusion allows the reconstruction of white matter tracts that can then inform neuroscientists about the underlying brain structure (Huisman, 2010). 

This information can also be used to create colour FA maps which demonstrate the direction of the fibers. In these graphical depictions of white matter connectivity, brighter colours represent white matter bundles with greater anisotropic diffusion. Most commonly, red fiber tracts are left to right, green are front to back, and blue are up and down. 

DTI can also be used to generate tractograms (see Fig 2) and MD maps.  

 

Applications and Limitations of DTI

DTI is often used to explore diseases associated with white matter dysconnectivity. This includes traumatic brain injuries (TBIs), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), Parkinson’s disease, epilepsy, and stroke (Tae et al., 2018).

While the possibilities of DTI are exciting, this modality is not without its flaws. It currently has a low signal to noise ratio (SNR) and requires long scan times (Huisman, 2010). Further, it has low spatial resolution and is very susceptible to motion (Soares et al., 2013).