Research Area: Brain Imaging

brain mapping, functional connectivity, fMRI, EEG, MEG, predictive imaging, precision interventions, contrast agents, theory of mind, the developing brain, learning

Detecting the brain’s magnetic signals with MEG

by Charles Jennings |

Magnetoencephalography (MEG) is a noninvasive technique for measuring neuronal activity in the human brain. Electrical currents flowing through neurons generate weak magnetic fields that can be recorded at the surface of the head using very sensitive magnetic detectors known as superconducting quantum interference devices (SQUIDs).

MEG is a purely passive method that relies on detection of signals that are produced naturally by the brain. It does not involve exposure to radiation or strong magnetic fields, and there are no known hazards associated with MEG.

MEG was developed at MIT in the early 1970s by physicist David Cohen. Photo: David Cohen

Magnetic signals from the brain are very small compared to the magnetic fluctuations that are produced by interfering sources such as nearby electrical equipment or moving metal objects. Therefore MEG scans are typically performed within a special magnetically shielded room that blocks this external interference.

It is fitting that MIT should have a state-of-the-art MEG scanner, since the MEG technology was pioneered by David Cohen in the early 1970s while he was a member of MIT’s Francis Bitter Magnet Laboratory.

MEG can detect the timing of magnetic signals with millisecond precision. This is the timescale on which neurons communicate, and MEG is thus well suited to measuring the rapid signals that reflect communication between different parts of the human brain.

MEG is complementary to other brain imaging modalities such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), which depend on changes in blood flow, and which have higher spatial resolution but much lower temporal resolution than MEG.

Our MEG scanner, an Elekta Neuromag Triux with 306 channels plus 128 channels for EEG, was installed in 2011 and is the first of its kind in North America. It is housed within a magnetically shielded room to reduce background noise.

The MEG lab is part of the Martinos Imaging Center at MIT, operating as a core facility, and accessible to all members of the local research community. Potential users should contact Dimitrios Pantazis for more information.

The MEG Lab was made possible through a grant from the National Science Foundation and through the generous support of the following donors: Thomas F. Peterson, Jr. ’57; Edward and Kay Poitras; The Simons Foundation; and an anonymous donor.

Faces have a special place in the brain

by Cathryn Delude |

Are you tempted to trade in last year’s digital camera for a newer model with even more megapixels? Researchers who make images of the human brain have the same obsession with increasing their pixel count, which increases the sharpness (or “spatial resolution”) of their images. And improvements in spatial resolution are happening as fast in brain imaging research as they are in digital camera technology.

Nancy Kanwisher, Rebecca Frye Schwarzlose and Christopher Baker at the McGovern Institute for Brain Research at MIT are now using their higher-resolution scans to produce much more detailed images of the brain than were possible just a couple years ago. Just as “hi-def” TV shows clearer views of a football game, these finely grained images are providing new answers to some very old questions in brain research.

One such question hinges on whether the brain is comprised of highly specialized parts, each optimized to conduct a single, very specific function. Or is it instead a general-purpose device that handles many tasks but specializes in none?

Using the higher-resolution scans, the Kanwisher team now provides some of the strongest evidence ever reported for extreme specialization. Their study appeared in the Nov. 23 issue of the Journal of Neuroscience.

The study focuses on face recognition, long considered an example of brain specialization. In the 1990s, researchers including Kanwisher identified a region known as the fusiform face area (FFA) as a potential brain center for face recognition. They pointed to evidence from brain-imaging experiments, and to the fact that people with damage to this brain region cannot recognize faces, even those of their family and closest friends.

However, more recent brain-imaging experiments have challenged this claimed specialization by showing that this region also responds strongly when people see images of bodies and body parts, not just faces. The new study now answers this challenge and supports the original specialization theory.

Schwarzlose suspected that the strong response of the face area to both faces and bodies might result from the blurring together of two distinct but neighboring brain regions that are too close together to distinguish at standard scanning resolutions.

To test this idea, Schwarzlose and her colleagues increased the resolution of their images (like increasing the megapixels on a digital camera) ten-fold to get sharper images of brain function. Indeed, at this higher resolution they could clearly distinguish two neighboring regions. One was primarily active when people saw faces (not bodies), and the other when people saw bodies (not faces).

This finding supports the original claim that the face area is in fact dedicated exclusively to face processing. The results further demonstrate a similar degree of specialization for the new “body region” next door.

The team’s new discovery highlights the importance of improved spatial resolution in studying the structure of the human brain. Just as a higher megapixel digital camera can show greater detail, new brain imaging methods are revealing the finer-grained structure of the human brain. Schwarzlose and her colleagues plan to use the new scanning methods to look for even finer levels of organization within the newly distinguished face and body areas. They also want to figure out how and why the brain regions for faces and bodies land next to each other in the first place.

Kanwisher is the Ellen Swallow Richards Professor of Cognitive Neuroscience. Her colleagues on this work are Schwarzlose, a graduate student in brain and cognitive sciences, and Baker, a postdoctoral researcher in the department.

The research was supported by the National Institutes of Health, the National Center for Research Resources, the Mind Institute, and the National Science Foundation’s Graduate Research Fellowship Program.