Measuring brain function: how do fMRI scanners work?

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In 1977, Dr. Raymond Damadian performed the first magnetic resonance imaging (MRI) scan on a live human patient (Edelman, 2014). MRI is a non-invasive medical imaging technique producing detailed pictures of anatomical structures and physiological processes inside the body (McRobbie et al., 2017). MRI scans are considered safer than CT scans and PET scans as they do not involve X-rays or ionizing radiation

Instead, MRI scanners produce a strong magnetic field to force protons – which are abundant in water and fat – to line up with that field (Weishaupt et al., 2008). When the radiofrequency field is switched off, the MRI sensors detect the energy released by the protons as well as the time it takes them to realign with the magnetic field; these magnetic properties are in turn used by researchers to distinguish various tissues and detect abnormalities (Berger, 2002).

MRI scanners have become ubiquitous in neuroimaging research because of their versatility (Chen & Li, 2012). Beside high resolution anatomical pictures allowing researchers to study the brain’s structure, MRI scanners can produce rapid snapshots to observe brain activity. This technique, called functional MRI (fMRI), rests on the blood-oxygen-level dependence (BOLD) effect (Belliveau et al., 1991). The BOLD effect is based on the different magnetic properties of oxyhaemoglobin and deoxyhaemoglobin, which interact differently with the magnetic field produced by the MRI scanner (Ogawa & Lee, 1990; Logothetis, 2003). While oxyhaemoglobin is weakly diamagnetic, deoxyhaemoglobin is strongly paramagnetic; this allows researchers to distinguish both blood types on the resulting images and indirectly measure brain activity by looking for local changes in magnetism (Greve, 2011).

fMRI has been widely used to measure both the cognitive activities of the brain based either on an induced stimulus (Linden et al., 1999; Heeger & Ress, 2002) or when an explicit task is not being performed (Raichle et al., 2001; Fox & Raichle, 2007; Biswal, 2012). These two methods are respectively called task-based fMRI and resting state fMRI (Zhang et al., 2016). Because fMRI cannot produce individually quantitative signals, but only relative differences between two brain states (Langleben, 2008), task-based fMRI and resting-state fMRI are sometimes used in combination to minimise the signal-to-noise ratio in the resulting data (Di et al., 2013).

Task-based fMRI in particular has allowed researchers to confirm the connexion between cognitive functions and specific brain regions (Berman et al., 2006). For example, researchers posited that if two tasks lead to the activation of common brain areas, these tasks and related behaviours are likely to share some cognitive processes (Jonides et al., 2006). Conversely, Stroop tasks (Stroop, 1992) have been used in combination with fMRI to separate distinct psychological processes such as cognitive control and performance monitoring (MacDonald et al, 2000).

Task-based fMRI has also been used to understand neurological dysfunctions and diseases, for example by comparing the prefrontal cortical activity of subjects in normal aging with ones with mild cognitive impairment or suffering from Alzheimer’s disease (Li et al., 2009). All these applications of task-based fMRI are allowing researchers to progressively build an architecture of brain activity, psychological processes and cognitive behaviours.

Currently the most popular method to visualise the brain-behaviour and the structure-function and relationship (Glover, 2011), fMRI has many advantages such as high spatial resolution, a relatively affordable cost, and a low tolerance needed from the subject, which makes it an appropriate tool for both research and diagnostic (Mier & Mier, 2015). But it also has some limitations, including a medium temporal resolution, allowing researchers to only capture images every few seconds.

Researchers have attempted to achieve greater temporal resolution by integrating electroencephalography (EEG) and fMRI (Yang et al., 2010). EEG has low spatial resolution but directly measures the brain’s electrical activity, resulting in higher temporal resolution – in milliseconds rather than seconds (Burle et al, 2015).

Hover, the implementation of both techniques is challenging, and the future of fMRI research may instead lie in the development of quantitative measures to identity biomarkers for specific diseases, either by using group statistics for cognitive processes with weak BOLD responses, or by using direct measures for primary sensory systems resulting in strong BOLD responses (Glover, 2011). Another area of research that is gaining attention is the study of the brain’s resting state and how its networks are affected by neuropsychiatric disorders such as depression and Alzheimer’s disease (Greicius, M. 2008).


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