Researchers have used the well-established Potts model-an algorithm mathematicians and biologists use to measure how individual entities behave in relation to their neighbors-to help map the spatial organization of neurons in the brains of Alzheimer's patients and normal volunteers. Due to appear in this week's PNAS early online edition, the results mark a new approach to automate recognition of neurons in microscopic brain images, which could help measure anatomical changes in conditions where neurons die.
First author S. Peng, working with Boston University's Eugene Stanley and Brad Hyman from Massachusetts General Hospital, applied what they call a "parallel Potts segmentation approach" to help identify individual neurons in confocal microscope images of the brain. In this approach the simple Potts algorithm is run several times but with slightly different parameters. Varying the Potts parameters enabled the authors to correct for slight variations in contrast or focus that are inherent across the confocal images. This improved resolution and helped solve a major problem in brain mapping, namely the difficulty in identifying individual neurons when they overlap with others.
Peng and colleagues applied this mapping system to five images taken from a healthy subject and five from a patient suffering from Alzheimer's disease. A simple Potts analysis identified 86 percent of neurons in the former and 77 percent in the latter, but the parallel segmentation approach increased these numbers to 98 and 93 percent, respectively. Given that the method appears to work even in AD images, which often are of poorer quality due to morphological changes in the tissue, the authors suggest it will be useful when applied to other degenerative disorders and tissue types.—Tom Fagan
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