How does it Benefit Patients?
For patients with intractable epilepsy, surgery is often the best solution to end seizures. MEG is used to localize interictal (between seizure) activity, which is usually the source of the seizures themselves. MEG is particularly useful when MRI is negative (does not show a lesion) or routine EEG is inconsistent. MEG then guides the surgeon to a successful resection. Some findings may suggest a more complex situation, with a need for more investigations, or even the impossibility of surgery.
Another routine application of MEG is pre-surgical functional mapping (PSFM). This is useful for patients who have a tumours, other lesions, vascular malformations, epilepsy and/or brain injury. MEG is used to map the exact location of the healthy areas near the pathology, so that surgery does not result in postoperative weakness or loss of function. These functional areas, known as eloquent cortex, can include those used for audition, vision, motor control, language, etc.
Beyond the routine clinical indications for MEG, there are many other emerging applications, including mild traumatic brain injury, post-traumatic stress disorder, Alzheimer’s disease, autism, stroke recovery, dyslexia, stuttering and others.
Due to its fidelity and high temporal resolution, MEG has the ability to discern human brain networks with unprecedented accuracy. Increasingly, neuroscientists believe that many clinical disorders are caused by brain network interruptions. For example, evidence has shown that disruptions in the brain’s network can lead to both Alzheimer’s and autism. This positions MEG as the brain imaging modality of choice for studying and diagnosing these disorders.
The NIH funded Human Connectome Project is mapping the human brain as accurately as possible to gain information about connectivity, function and parcellation of the brain. It is largely focused on communication networks. The principle investigators have stated that because MEG measures neuronal activity in “real time” the connections activated either at rest or during task can be measured, giving a picture of the dynamic interactions among brain networks. Furthermore, because it has much greater temporal resolution than fMRI, MEG-based analysis provides correlates of connectivity, time-frequency content and temporal interactions. MEG provides the information that can reveal information flow between nodes of key cognitive networks.