MEG is an extremely sensitive imaging technique that measures magnetic fields in the brain that are produced by electrical activity.

MEG sensors positioned at 248 different locations recorded the continuous interactions within the brain over the period of time the subjects were being observed so that that resulting data could be analyzed by the researchers. The researchers were actually able to record the activity of individual brain cells every millisecond.

Within their paper, the researchers state: “On the basis of the estimated PCC⁰_ij [partial cross-correlations between the i and j sensors, where i and j are variables that denote specific sensors], dynamic neural networks were constructed (one per subject) that showed distinct features, including several local interactions. These features were robust across subjects and could serve as a blueprint for evaluating dynamic brain function.”

After analyzing their data, the researchers were able to classify the subjects into one of six medical disease categories: Alzheimer’s disease, chronic alcoholism, schizophrenia, multiple sclerosis, Sjogren’s syndrome, or healthy.

The result of the study by the Georgopoulos team, which includes Frederick J. P. Langheim and Arthur C. Leuthold, appears in the January 10, 2006 issue of the Proceedings of the National Academy of Sciences. The article is titled “Synchronous dynamic brain networks revealed by magnetoencephalography”.

The researchers are associated with the Domenici Research Center for Mental Illness, Brain Sciences Center, Veterans Affairs Medical Center, Minneapolis; and the Departments of  Neuroscience, Neurology, and Psychiatry, and the Center for Cognitive Sciences, University of Minnesota, Minneapolis.

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