Experiments involving functional magnetic resonance imaging (fMRI) are performed frequently, yet researchers are still searching for representations of fMRI data that connect directly to anatomy and make the data easy to interpret.  A variant of traditional MRI imaging, fMRI measures magnetic differences between oxygenated hemoglobin and deoxygenated hemoglobin.  If a particular area of the brain works harder than its baseline activity level, then blood rushes into that area, and the area lights up on the scanner.  During an fMRI experiment, a person in a scanner performs a particular task, such as identifying images.  Each collected data point is four-dimensional, with three spatial coordinates and one time coordinate.  The areas of the brain in which blood flow increases when a person performs a task are assumed to be involved with that task, and these areas form a network.

The traditional method of defining a network begins with choosing a user-selected “seed” region of interest.  The time course of each region of the brain is compared to that of the seed region, and the correlation of the two is measured.  If the correlation is above a given threshold, then the region in question is said to coactivate with the selected seed.  A binary map of the brain is created, showing regions that coactivate with the seed and those that do not.  This method of defining a network, however, is not the only possibility.  Polina Golland, of MIT, is investigating a new method of defining networks.  Her technique involves identifying interesting seed regions and simultaneously associating each piece of the brain with the appropriate seed region, thus defining networks.  As the number of possible seed regions increases, larger networks break into smaller networks.  Detected patterns of co-activation seem to be inherently hierarchical.  The brain can be anatomically represented as a tree of large systems that branch into smaller systems, and it seems that the brain can be functionally described in a similar fashion.  Golland’s lab has shown that the brains of different people have similar functional tree structures.   In the future, Golland hopes to use fMRI to see if populations differ in terms of functional brain structure.     

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