MEG is based on the detection of the very weak magnetic fields that originate from electrical activity within the brain. These signals are detected with an array of devices known as SQUIDs (superconducting quantum interference devices) that are placed close to the scalp. The array is mounted in a close fitting helmet and is cooled with liquid helium. MEG technology has evolved since its invention in the late 1960s and, in particular, the density of detector arrays has greatly increased; current state-of-the art systems have more than 300 channels. This, combined with increasingly more sophisticated analytical methods, leads to constantly increasing spatial resolution and data richness.

MEG source localization is the technique of inferring the location of the source(s) within the brain from signals measured at the scalp. There are several well defined approaches to perform this and researchers continue to make improvements.

MEG is fundamentally different from fMRI. In particular, MEG signals originate directly from electrical activity in the brain, providing very high (sub-millisecond) temporal resolution that is essential for studying rapid brain events that underlie mental processes. By contrast, fMRI provides good spatial resolution, but its temporal resolution is at least 1000 times less than that of MEG.

MEG has some similarities to scalp electroencephalography (EEG), which is based on electrical signals rather than magnetic signals from the brain. Both methods have similar temporal resolution, but the sources of the two signals are largely non-overlapping, since they are dominated by dendrites in different orientations. MEG has better spatial resolution than EEG because magnetic signals are less susceptible to distortion by intervening tissues, and because the MEG sensors are not electrically or magnetically coupled to one another, unlike EEG electrodes which are electrically coupled through the scalp. MEG also is free from microsaccade-derived muscle artifacts, which can contaminate EEG measurements in the gamma frequency range (Yuval-Greenberg et al, 2008). Importantly, MEG also is less demanding on the operator and patient; it requires almost no setup for the human subjects, whereas EEG requires the application of conducting gel to the scalp in order to produce electrical contact with the detectors. Typical setup time for EEG is about 30 minutes, a significant disadvantage for any project requiring large numbers of subjects and particularly for studies involving patients, elderly subjects or children.

MEG and EEG are mutually compatible, so—in cases in which EEG setup is logistically feasible—it is possible to acquire both signals simultaneously, providing further opportunities for multimodal integration and source localization. Elekta Neuromag includes a built-in EEG system to provide for simultaneous recording of MEG and EEG.

Although MEG technology was initially used for fundamental brain research, today an increasing number of hospitals are using MEG for a variety of clinical applications. MEG has entered the clinical mainstream as is illustrated by, for example, the position of large US insurance companies. Of the twenty largest insurers (collectively covering about half the US population), ten of them now have formal policies stating that MEG examinations are routinely covered. This includes WellPoint, Aetna, Humana, Health Net, Highmark and several other smaller payors. Many of the remainder will reimburse on a case-by-case basis. Medicare and Medicaid, the US publicly run systems, have covered MEG for years. The most common clinical procedures covered by reimbursement are:

• Detection of epileptic activity for pre-surgical evaluation. MEG provides a non-invasive method to accurately pinpoint the origin of seizure activity by measuring interictal spikes. It is well tolerated by patients and has been shown to perform better than PET and SPECT. For those with positive MEG information, the sensitivity for a seizure-free (Engel Class I) outcome was 72% and the specificity was 70%, the positive predictive value was 78%, and the negative predictive value was 64%. MEGs showing multifocal or generalized epileptic discharges early in the surgical evaluation process had a high negative predictive value for poor outcome. Further reading on MEG’s utility for epilepsy includes:

   

  Sutherling et al. Influence of magnetic source imaging for planning intracranial EEG in epilepsy. Neurology 2008;71(13):990‐6

  Knowlton et al. Functional imaging: I. Relative predictive value of intracranial electroencephalography. Ann Neurol 2008;64(1):25‐34

  Knowlton et al. Functional imaging: II. Prediction of epilepsy surgery outcome. Ann Neurol 2008;64(1):35‐41.

• Presurgical Functional Mapping (PSFM) is a technique in which sensory or motor function is localized within the brain by repeatedly stimulating the patient or asking them to perform a task. The American Academy of Neurology has stated under a heading called the Value of MEG in Localization and Resective Surgery (AAN Magnetoencephalography (MEG) Policy, Adopted May 8, 2009): “A cardinal principle in resective surgery is to remove only the abnormal tissue and preserve normal functional tissue. This is particularly crucial in the cortical regions of the brain. Normal and abnormal tissues are often in close proximity and may appear contiguous and indistinguishable to naked eye inspection. Even when the abnormal structure, such as a vascular malformation, may be obvious, the location of a normal eloquent brain tissue cannot be determined without specialized testing...The value of MEG and certain other tests lies in their ability to localize and demarcate both normal and abnormal functioning regions of the brain.”
• Language lateralization, as an alternative to the Wada test (Papanicolaou et al, 2004). The American Academy of Neurology (AAN Magnetoencephalo-graphy (MEG) Policy, Adopted May 8, 2009) has stated that: “Language and memory functions may reside in both or one hemisphere in patients with epilepsy. Determination of laterality is crucial in resective surgery that aims to preserve as much language and memory functions as feasible. Towards this end, the intracarotid amobarbital test (Wada test) has long been as the standard bearer. There are drawbacks to the Wada test – the procedure is quite invasive, uncomfortable to the patient and it carries morbidity. Several alternatives such as neuropsychological testing, fMRI, MEG, behavioral testing and SPECT-PET are available. Each has certain merits and disadvantages...MEG, while requiring patient cooperation, had the advantage of being a non-invasive direct measure with excellent temporal resolution.”
   

Despite its clinical adoption, researchers continue to use MEG to provide new insights into the neural basis of developmental disorders such as autism and dyslexia, psychiatric diseases including depression, bipolar disorder and schizophrenia, and neurodegenerative diseases such as Alzheimer’s disease. MEG also is being used to address basic questions about brain functions, such as memory, attention, emotion, language and social cognition, abilities that are frequently disrupted by brain disorders. Some applications in which MEG can contribute to scientific and medical research include:

• The basis of language itself, a uniquely human capacity that cannot be investigated in animals and whose rapid neural processes are not well resolved by fMRI.
• Brain responses in children as young as newborns (Kujala et al., 2004). This is facilitated by signal processing methods that reduce the effect of head motion (Wehner et al 2008), the necessary software and hardware for which is supplied with Elekta Neuromag. The ability to study in children with high temporal and spatial precision could transform the understanding of early brain development, including development of perceptual abilities, language and social cognition and disorders such as autism and dyslexia.
• Brain activity patterns that might serve as biomarkers for brain disorders (Georgopoulos et al, 2007, 2010), and for understanding fundamental mechanisms that may underlie such disorders (e.g., Vierling-Claasen et al., 2008). In this way, MEG is a potentially powerful tool for translational research.
• Neural effects of educational interventions in areas such as reading (Simos et al, 2007) or musical training (Kuriki et al 2006),
• Neural basis of memory, thought and emotion in the human brain. This is a growing application for a combination of behavioral testing and neuroimaging approaches.
• Short- and long-term memory.
• Development of memory in childhood.
• Development of reading and the remediation of dyslexia.
• Executive control.
• Effects of culture on brain function.
• Neural basis of intelligence.
• Neural basis of developmental, psychiatric and neurodegenerative disorders.
• Social-affective information processing in individuals with autism spectrum disorders (ASD).
• Neural basis of attention in animals and humans. MEG provides an ideal tool for monitoring high frequency oscillations in humans. It therefore holds great promise for studying how functional connectivity is modulated during cognitive tasks, and how this may be impaired in brain disorders.
• MEG studies of synchronous neural activity in normal human subjects, to better link the understanding of human cognitive mechanisms to the neurobiology of cognition studies in animals.
• Neural basis of perception, especially the neural correlates of tactile perception. Specific elements of the evoked response from SI predict success on a trial to trial basis in a tactile detection task (Jones et al., 2007).
• Accelerate perceptual training in combination with EEG.
• The neural basis of age-related changes in cognition. MEG can be used to monitor brain activity in older adults performing working memory tasks, to the correlate these changes with alterations in brain morphology as revealed by structural MRI (Ziegler et al, 2008) and diffusion tensor imaging (Salat et al, 2005) of the same individuals. Similar methods can be employed to examine patients with mild cognitive impairment (MCI), who are at heightened risk for developing Alzheimer’s disease. By following these patients over time, researchers could determine whether abnormal patterns of activity are associated with conversion from MCI to full-blown Alzheimer’s disease.
• Brain mechanisms of face recognition (Liu et al, 2000, 2002; Xu et al, 2005), taking advantage of MEG’s high temporal resolution to identify separate signals for categorizing a stimulus as a face and (less than 100 milliseconds later) for recognizing individual faces (Liu et al, 2002).
• Social cognition, and to examine how individuals differ with respect to their processing of socially significant cues such as faces. MEG also can be used to apply a similar approach to other aspects of visual perception, to explore how different categories of visual objects are represented within the brain and how such representations are affected by experience.
• A general principle of cognition is the integration of bottom-up perceptual signals with top-down control from higher brain regions. Investigators are developing computational models of this integration process, with the goal of modeling higher visual abilities such as probabilistic interpretation of complex visual scenes. MEG can be harnessed to test and refine these models in humans, taking advantage of MEG’s unique combination of high temporal and spatial resolution to dissect the contributions of feed-forward and feedback processes to visual perception.