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Magnetoencephalography (MEG) is a functional neuroimaging technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using very sensitive magnetometers.
Initiated in 2006 and currently funded by NIH Grant number: 1R24EB029173, [1] [2] NITRC's mission is to provide a user-friendly knowledge environment that enables the distribution, enhancement, and adoption of neuroimaging tools and resources and has expanded from MR to Imaging Genomics, EEG/MEG, PET/SPECT, CT, optical imaging, clinical neuroinformatics, and computational neuroscience.
EEG-fMRI (short for EEG-correlated fMRI or electroencephalography-correlated functional magnetic resonance imaging) is a multimodal neuroimaging technique whereby EEG and fMRI data are recorded synchronously for the study of electrical brain activity in correlation with haemodynamic changes in brain during the electrical activity, be it normal function or associated with disorders.
The institute's core facilities include the Martinos Imaging Center, which provides neuroimaging technologies for human and animal research, including MRI, EEG and MEG. The McGovern Institute occupies approximately 85,000 sq ft (net) within the MIT Brain and Cognitive Sciences Complex.
The magnetoencephalography (MEG) equivalent of ERP is the ERF, or event-related field. [2] Evoked potentials and induced potentials are subtypes of ERPs. History
Neuroimaging is the use of quantitative (computational) techniques to study the structure and function of the central nervous system, developed as an objective way of scientifically studying the healthy human brain in a non-invasive manner. Increasingly it is also being used for quantitative research studies of brain disease and psychiatric ...
When comparing and contrasting neuroimaging devices it is important to look at the temporal resolution, spatial resolution, and the degree of immobility. In particular, EEG (electroencephalograph) and MEG (magnetoencephalography) have high temporal resolution, but a low spatial resolution. EEG also has a higher degree of mobility than MEG has.
An example that identified 10 large-scale brain networks from resting state fMRI activity through independent component analysis [15]. Because brain networks can be identified at various different resolutions and with various different neurobiological properties, there is currently no universal atlas of brain networks that fits all circumstances. [16]