Combing through brain imaging data to discover novel patterns linked to mental health conditions

Combing through brain imaging data to discover novel patterns linked to mental health conditions

New research by Georgia State University’s TReNDS Center may lead to early diagnosis of conditions such as Alzheimer’s disease, schizophrenia and autism — in time to help prevent and more easily treat these disorders. In a new study published in Scientific Reports a team of seven scientists from Georgia State built a sophisticated computer program that was able to comb through massive amounts of brain imaging data and discover novel patterns linked to mental health conditions. The brain imaging data came from scans using functional magnetic resonance imaging (fMRI), which measures dynamic brain activity by detecting tiny changes in blood flow.

“We built artificial intelligence models to interpret the large amounts of information from fMRI,” said Sergey Plis, associate professor of computer science and neuroscience at Georgia State, and lead author on the study.

He compared this kind of dynamic imaging to a movie — as opposed to a snapshot such as an x-ray or, the more common structural MRI — and noted “the available data is so much larger, so much richer than a blood test or a regular MRI. But that’s the challenge — that huge amount of data is hard to interpret.”

In addition, fMRI’s on these specific conditions are expensive, and not easy to obtain. Using an artificial intelligence model, however, regular fMRI’s can be data mined. And those are available in large numbers.

“There are large datasets available in individuals without a known clinical disorder,” explains Vince Calhoun, Founding Director of the TReNDS Center, and one of the study’s authors. Using these large but unrelated available datasets improved the model’s performance on smaller specific datasets.

“New patterns emerged that we could definitively link to each of the three brain disorders,” Calhoun said.

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