Navigating the Brain through Magnetoencephalography

By Taylor Schulte

September, 2015

Modern medical technology has provided us with methods to image the human body with extreme detail. For example, Magnetic Resonance Imaging (MRI) produces a detailed image of soft tissues in the body by inducing a magnetic field. Electroencephalography (EEG) measures electric impulses in the brain. Combining these concepts gives a unique way to image the complex activity in the brain: magnetoencephalography (MEG), a neuroimaging technique that detects the magnetic fields generated by neurons themselves. MEG is a noninvasive and precise technique that eliminates the need for induced magnetic fields, contrast dyes, or harmful radiation.

The technological basis of MEG relies on superconducting sensors, referred to as superconducting quantum interference devices or SQUIDs, to measure brain wave activity occurring between the frequency of 0.4 – 45 Hz and computer algorithms to analyze the data for consistency and abnormalities (Ioannides 2006, Foley et al. 2013). Integrating sensor placement with the patient’s MRI and EEG data allows the researcher to localize precisely where in the brain an abnormality appears. Additional algorithms calculate the phase differences between signals to track connectivity patterns throughout the brain. Phase differences can be observed when comparing two waves of the same frequency that were initiated at different times, thus causing them to be “out of phase.” This connectivity tracking technology has become extremely effective for studying epilepsy, as well as psychiatric diseases, such as bipolar disorder, schizophrenia, and depression (Takei et al. 2010, Ionescu et al. 2015, Brookes et al. 2011). 

MEG has helped characterize the complex networks that make up brain functioning. First, there are networks called resting-state networks, which are active in the absence of external stimuli (Foley et al. 2013). Resting-state networks have been studied indirectly using functional MRI (fMRI) (Brookes et al. 2011). fMRI detects specific small changes in the MRI signal caused by neuronal activity triggering fluctuations in blood oxygen level (Gore 2003). MEG allows a more direct method of classifying these networks by measuring its electrophysiological basis (Brookes et al. 2011).

Adding external stimuli to an MEG experiment allows the researcher to observe the next level of brain functioning. While MEG experiment must be performed in a magnetically shielded room to reduce noise, visual stimuli can be projected onto a screen and auditory stimuli can be added through headphones worn by the subject. These types of externally stimulated MEG experiments have been applied to the observation and study of psychiatric disorders, and they provide an objective method of diagnosing subtypes of various disorders. For example, anxious depression, a subtype of Major Depressive Disorder, is characterized by loss of cognitive function, especially memory (Ionescu et al. 2015). An MEG study provided evidence for this, showing decreased neural activation in patients with anxious depression in a more demanding memory task than patients with other depressive disorders (Ionescu et al. 2015).

While MEG studies have been valuable, there is potential for this technology to provide an even greater wealth of information. With continued research, MEG could become the standard for objective diagnosis of psychiatric disorders. Observing connectivity patterns in the brain is essentially observing how we think, how we process information, and how it flows through the complex electrical networks within our brains. Future studies utilizing MEG technology could provide a deeper, more concrete understanding of this traditionally abstract concept.



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