Functional Neuroimaging and Depression

By Tina Liu

Do you know that the activities of the neurons in our brain are constantly fluctuating as we engage in daily activities, no matter if the action is simple or not? Even on occasions when we close our eyes and rest, our brain remains highly active. To measure and map brain activities and improve understanding of certain brain areas, functional magnetic resonance imaging (fMRI) is often applied. Now, let’ s find more about this technique.

An overview of fMRI

The fMRI technique was invented in 1990 by a group at Bell Laboratories led by Seiji Ogawa. It measures brain activity by detecting changes associated with blood flow. When a certain brain region is activated, it calls out for more oxygen. The fMRI will then follow oxygenated blood as it flows through the brain. On the printout of the scan, these oxygenated brain regions will appear to be lit up. The areas scientists can see are smaller than a millimetre cubed, or a voxel (a pixel in three dimensions). The major goal of fMRI data analysis is to detect correlations between brain activation and a task the subject performs during the scan.

Advantages of fMRI

fMRI has several elementary features which contribute to its unmatched performance in neurological applications. First, fMRI does not involve any radiation such as X-rays during the process. If done correctly, fMRI has no risks and can evaluate brain function in a safe and non-invasive manner. It is easy to use, and can produce an image at a very high resolution, making fMRI very effective. Compared to other methods of psychological evaluation like questionnaires, fMRI is certainly more objective.

Moreover, the clinical convenience provided by fMRI is worth noticing. As fMRI is performed with standard MRI scanners, the injection of contrast agents (responsible for stimuli) is avoided, as it can be achieved through the measurement of an endogenous contrast agent that is originally present at a high concentration in the brain. Lastly, fMRI enhances the possibility to study not only alterations in brain morphology that affect the activation of a certain region, but also its connectivity to other brain regions.

Clinical applications: How fMRI aid the diagnose depression

Being the most common mental disorder in the world, depression had impacted around 300 million people. Common treatments for depression ranged from psychotherapies to counselling to taking medicines. For example, antidepressants compounded with psychotherapy will be recommended by doctors for patients having moderate to severe depression. However, although antidepressants are not generally dependency-forming, the misuse of such medicine can be dangerous. Hence, research shows that antidepressants may be more damaging than beneficial to health. As prescription drugs are a relatively new phenomenon to the human race, concern regarding whether chemical treatments interfere with the human brain also arises.

Although depression is often treated as a single disease, many researchers tend to believe that it is a multiple ailment. While topics such as how depression subtypes and how they differ from each other are being debated over time, some try to correlate the neural circuits that light up during specific tasks with the patterns of activation with symptoms. fMRI is used in this process to provide doctors with a deeper look into the patient’ s brain.

An example will be The Research on Anxiety and Depression-Anhedonia Treatment (RAD-AT), a study led by Stanford clinical neuroscientist Leanne Williams, aiming at probing how depression manifests in the brain, hence discoveringhow it is associatedwith suicide. After years of studying, Williams has collected thousands of brain scans from people facing depression, hence discovering the existence of at least six subtypes of depression.

Characterizing depression begins with the fMRI scanner, where the volunteers will lie while performing a list of mental tasks. Each task exercises a different assembly of circuits in their brains, corresponding to the six depression subtypes Williams has hypothesized. According to Brooke Staveland, a neuroimaging research engineer as well as a companion of Williams, the scan can reveal how different regions of the brain coordinate brain-wide neuronal chatter. However, despite the importance of fMRI in deciphering the pathogenesis of depression, the scan couldn ’t capture symptoms that wax and wane as it is only a snapshot in time. As a result, fMRI was questioned by some neurologists for its effectiveness in reflecting the emotional fluctuation of the patient’ s brain.

Social implications

fMRI technique offers a powerful set of tools for observing experience-related changes. Researchers can look at the brain ’ s response to a particular input, such as a cognitive stimulus, a contextual setting or, in the clinical realm, a change in response to an intervention. Since the 1970s, some psychiatrists have encouraged the incorporation of fMRI into their field. With the advent of biological psychiatry, functional neuroimaging research on behavioural problems has exploded. Although functional neuroimaging has not yielded clinically relevant biomarkers for mental disorders, current studies lay the groundwork for its eventual use in the diagnosis, treatment, and prevention of behavioural problems and psychiatric disorders.

Critiques and challenges

The technique of fMRI has always been facing complaints or challenges by many. One significant complaint is that fMRI only looks at the blood flow in the brain. fMRI measures blood flow and not neural activity directly because researchers assume that the blood flow correlates with neural activity. And although blood flow can reveal changes in brain areas as small as a millimetre cube, there can be hundreds of thousands of neurons in a tiny voxel. In this case, fMRI fails to hone in on neurons which are critical to mental functioning. On the other hand, as each area of the brain is made up of thousands of individual neurons and represent different functions, when a certain area is lit up on fMRI, it is hard to identify the exact type of brain activity being represented in the scan.

As most of the fMRI researchers are not trained in software engineering, they utilise one of several open-source analysis packages for pre-processing and statistical analyses. Hence, the attention to good software development practices that could help prevent errors is insufficient, and software errors may occur. An example of this would be shown in an article published in PubMed a few years ago, named AlphaSim: Software for Breeding Program Simulation. The analyses presented in a preprint of the present article contained two software errors that led to different results being presented in the final version of the paper.

Because of these drawbacks, some critics even argue that fMRI is nothing more than a high-tech version of phrenology (the 19th-century pseudo-science that claimed to divulge a person ' s character based solely on the shape of his/her skull).

Solutions and how to pursue in the future

Despite the critiques and challenges fMRI has faced, researchers still made suggestions to eliminate some of the problems, such as replacing custom code with software tools from well-established projects. By doing so, errors are more likely to be discovered when the code is used in a larger group and larger projects are more likely to follow better software development practices. Also, researchers should learn and implement good programming practices including the use of software testing and validation, to avoid such mistakes.

In more recent years, a technique named “ resting-state ” fMRI (rs-fMRI) was developed. Here, research subjects are only scanned over a certain period without any involvement of active tasks. Rs-fMRI gave researchers a new and intriguing possibility for doing clinical fMRI, especially in cases where patients are severely affected like after a stroke or traumatic brain injury. Investigators are exploring novel applications of these techniques, hoping to increase the diagnostic sensitivity, accuracy and specificity, as well as the predictive value of the information.

Reference https://cfmriweb.ucsd.edu/Research/whatisfmri.html https://www.biorxiv.org/content/10.1101/059188v3.full.pdf https://www.sciencemag.org/news/2019/08/brain-scans-could-help-personalizetreatment-people-who-are-depressed-or-suicidal https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3787513/ https://www.ncbi.nlm.nih.gov/books/NBK538909/ https://www.nhs.uk/mental-health/conditions/clinical-depression/treatment/