Magnetoencephalography identifies Alzheimer’s disease

Conventional diagnostics such as functional MR cannot capture the fleeting brain activity indicative of brain diseases such as Alzheimer's disease, according to University of Minnesota researchers. The communication patterns form and disappear in microseconds, too short a time for chemically based imaging to grasp. They consider magnetoencephalography a better tool.

MEG has been kicking around the medical imaging community for years as a mapping tool, but its potential for diagnosing brain disease has been largely unproven. Researchers from the University of Minnesota Medical School and the Brain Sciences Center at the Veterans Affairs Medical Center, both in Minneapolis, now believe they have found a way for this technology to reach its potential.

Their version of the technology can record brain cells communicating among themselves. These magnetic signals are then interpreted by algorithms that discern the underlying patterns of normal and abnormal processes. This ability may provide a simple test for Alzheimer's and other brain diseases.

Armed with a MEG configuration composed of 248 sensors gathering data interpreted by specially crafted algorithms, the researchers correctly classified 142 subjects as either healthy controls or patients in the throes of proven brain disease. Among the diagnoses were Alzheimer's disease, chronic alcoholism, schizophrenia, multiple sclerosis, and Sjögren's syndrome.

"We were able to classify with 100% accuracy the various disease groups represented in the group of research subjects," said Dr. Apostolos P. Georgopoulos, a UM professor of neuroscience, neurology, and psychiatry, who led the MEG research team.

Their findings are scheduled for publication in the Aug. 27, 2007 issue of the Journal of Neural Engineering.

"In the future, MEG could be applied when studying the effect of new treatments or drug therapies by assessing the status of the brain networks over time," he said.

MEG is an elegantly simple test that "provides a glimpse into the brain as it is working," according to Georgopoulos, recording the communications among brain cells that occur millisecond by millisecond. His team began developing MEG as a diagnostic tool after conducting basic research that showed neural interactions across human subjects could serve as a blueprint for evaluating dynamic brain function.

Georgopoulos and his colleagues published a paper on this novel way to assess the dynamic interactions of brain networks acting in synchrony in the Jan. 10, 2006, issue of the Proceedings of the National Academy of Sciences. In the paper, Synchronous dynamic brain networks revealed by magnetoencephalography, the researchers

noted a "remarkable robustness" of the network configuration across subjects. They concluded that this points to a relatively stable synchronous interaction pattern among neural populations, which in turn can serve as the means for assessing dynamic brain function.

Since drawing that conclusion, Georgopoulos and colleagues have spent their time applying the technique to discern patterns associated with specific brain diseases. They will continue collecting data on patients with brain diseases already proven with MEG, while exploring whether the clinical reach of this technology may extend to other ones. These include depression, post-traumatic stress disorder, autism, and Parkinson's disease.