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Imaging the Brain
DOE scientists from many disciplines are collaborating to develop and apply new technologies to study the function of the human brain. The goals of the research are to expand our basic understanding of how the brain works and to develop tools for the diagnosis and treatment of mental and neurological disorders.

by John George.


A representation of the magnetic fields measured by sensors as superimposed on the surface of the subject's head.

The approach involves advanced sensor technologies, complex system integration, mathematical and computer modeling, and computational visualization. At Los Alamos National Laboratory, the primary method of noninvasive functional brain imaging being investigated is magnetoencephalography (MEG) and the integration of MEG with magnetic resonance imaging (MRI) and functional MRI (fMRI).

MEG measures minute magnetic fields produced by currents in electrically active nerve cells (neurons) in the brain. These minute magnetic fields can be measured by superconducting quantum interference device (SQUID) sensors, the most sensitive magnetic field detectors known, which are arranged in an array over the surface of the head. MEG is a completely noninvasive and passive (not even using externally applied magnetic fields) method of measuring brain function. MEG data input to sophisticated computer models allows neuronal activity to be located in space and with better than millisecond temporal resolution.

We have developed a new type of MEG sensor based on the Superconducting Imaging Surface (SIS) concept. The SIS produces an image of the magnetic source while shielding the sensors from the more powerful external magnetic fields, such as those produced by electric motors and Earth itself. (The magnetic field "lines" of these external sources can not penetrate the SIS). As a result, the SIS technology improves the signal-to-noise ratio of MEG measurements. Prototypes based on this idea have been successfully tested and a whole-head sensor array "helmet" based on this concept is currently being assembled.

Imaging brain function with MEG requires a series of computational tools for collecting and processing signals, modeling the physics of the measurement, and building probabilistic models of neuronal currents that account for the data. Anatomical MRI is used to noninvasively generate an image of the anatomy that is used to define the geometry of the head and brain, for modeling, and for visualizing regions of brain activity. Information from PET, fMRI, and other methods can be used to further improve the accuracy and reliability of functional brain maps based on MEG. The functional information obtained from PET and fMRI are supportive in that they provide a map of where activity is occurring, while the MEG provides the when, or temporal information, simultaneously with the where.


Modeling of cortical activations during a vision experiment.

In spite of their tremendous value, MEG, MRI and other available methods do not provide all of the information needed for the best medical care. By integrating information from multiple techniques we can exploit complementary strengths of existing methods, and we are working with scientists who are developing technologies that will provide new capabilities for research and clinical practice. For example, optical tomography can provide useful information about the biochemistry, physiology and anatomy of biological tissues, such as the head or breast. Thus, it is possible that optical tomography could add the what to the where and when information provided by MEG and FMRI.

We are exploring several "spin-off" biomedical applications of technologies developed for functional brain imaging. For example, we are developing a method of detecting and treating cancers (recently filed patent) and a method of detecting and locating aberrant electrical activity in the heart (such as those that cause atrial fibrillation). In addition, we are exploring non-biomedical applications such as a method of detecting and characterizing defects in materials.  


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First Science 2014