A report in the August issue of Behavioral Neuroscience, published by the American Psychological Association, details how "pattern array" software can be used to detect movements in rats which might help predict diseases such as Lou Gehrig's syndrome and other hereditary diseases. This type of data mining may enable testing of therapies to delay or even prevent disease.

The authors of the report demonstrated their original software on mutant rats used as an animal model of amyotrophic lateral sclerosis (ALS), popularly known as Lou Gehrig's disease, after the Yankee great who died in 1941 a progressive and fatal neurodegenerative disease that's inherited about one in 10 times.

Neri Kafkafi, PhD, of the Maryland Psychiatric Research Center (University of Maryland's School of Medicine) led the researchers in mathematically analyzing about 50,000 predetermined movement patterns that resulted when rats roamed freely, one by one, in a small arena. The software created an abstract space defined by combinations of behavior such as speed, acceleration and direction of movement. Mining the resulting behavioral data enabled researchers to test many more facets of behavior than they could analyze manually.
Movements of the rats were videotaped in two groups, those with a mutation that results in an ALS-type syndrome and a normal control group. The researchers used the computer to detect differences between the groups and identified a unique motor pattern in mutant rats two months before disease onset (which would equate to roughly five to 10 years in humans).

A significant behavior pattern change was the "heavily braking while slightly turning away from the wall" group difference. In two independent data sets, rats with the ALS-type mutation were significantly less likely than controls to brake and turn from the arena wall as they approached. Normal rats used that behavior for about 1.8 percent of their total movement time; the mutants for 1.2 percent.

"This is a very subtle difference but it is significant," says Kafkafi, in discussing how the subtlety of the movement would be to detect by naked eye. "Persons with emerging ALS may also have similarly subtle symptoms."

"We can only guess why this pattern is less common in the mutants," Kafkafi adds. One possibility is that losing the nerve cells that control leg muscles could result in problems with braking. The team is working with mechanical engineers to learn more about the meaning of the other movements.

Conceivably, by being able to predict more accurately which carriers may express the disease before they experience symptoms (the "premorbid" state), researchers could test medicines that might prevent symptoms from emerging. Kafkafi says, "Such therapies could very well be effective against the non-genetic version of the disease as well."

Methods such as data mining can be therapeutically useful even before science understands how disease begins. The authors wrote, "The discovery of reliable behavioral endpoints with predictive validity, even before a good understanding of their etiology is achieved, can significantly improve intervention research."

Adapted from a press release from the American Psychological Association.

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