The researchers then used multi-site data sets of over 1,200 people including those with autism spectrum disorder, schizophrenia, and Alzheimer’s disease. The researchers first trained the AI models on a dataset including over 10,000 people to learn to understand basic fMRI imaging and brain function. “New patterns emerged that we could definitively link to each of the three brain disorders,” Calhoun says. Using these large but unrelated available datasets improved the model’s performance on smaller specific datasets. “There are large datasets available in individuals without a known clinical disorder,” says coauthor Vince Calhoun, founding director of the TReNDS Center at Georgia State. And those are available in large numbers. Using an artificial intelligence model, however, regular fMRI’s can be data mined. In addition, fMRI’s on these specific conditions are expensive, and not easy to obtain. But that’s the challenge-that huge amount of data is hard to interpret.” Plis compares 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 notes “the available data is so much larger, so much richer than a blood test or a regular MRI.
“We built artificial intelligence models to interpret the large amounts of information from fMRI,” says lead author Sergey Plis, associate professor of computer science and neuroscience at Georgia State University. 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. New research may lead to early diagnosis of devastating conditions such as Alzheimer’s disease, schizophrenia, and autism-in time to help prevent and more easily treat these disorders.Īs reported in Nature Scientific Reports, researchers built a sophisticated computer program that is able to comb through massive amounts of brain imaging data and discover new patterns linked to mental health conditions.
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