Georgia State University’s TReNDS Center researchers, research 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.

Figure 1: AI in imaging to indicate dynamic mental illness.

Figure 1 shows thata 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. [1]

“We built artificial intelligence models to interpret the large amounts of information from fMRI,” said Sergey Plis. Researchers 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.”

Using an artificial intelligence model, however, regular fMRI’s can be data mined. And those are available in large numbers.The AI models were first trained on a dataset including over 10,000 individuals to learn to understand basic fMRI imaging and brain function. The researchers then used multi-site data sets of over 1200 individuals including those with autism spectrum disorder, schizophrenia, and Alzheimer’s disease. [2]

How does it work? It’s a bit like Facebook, YouTube or Amazon learning about you from your online behavior, and beginning to be able to predict future behavior, likes and dislikes. The computer software was even able to home in on the “moment” when the brain imaging data was most likely linked to the mental disorder in question.To make these findings clinically useful, they will need to be applied before a disorder manifests. [3]

“The vision is that we collect a large imaging dataset, our AI models pore over it, and show us what they learned about certain disorders,” Plis said. “We are building systems to discover new knowledge we could not discover on our own.”

Md Mahfuzur Rahman,“our goal is to bridge big worlds and big datasets with small worlds and disease-specific datasets and move towards markers relevant for clinical decisions.” [2]

References:

1. https://news.gsu.edu/2022/07/21/dynamic-mental-illness-indicators-caught-by-advanced-ai-in-brain-imaging/
2. https://neurosciencenews.com/ai-mental-health-21092/
3. https://7wdata.be/big-data/dynamic-mental-illness-indicators-caught-by-advanced-ai-in-brain-imaging/

Cite this article:

Sri Vasagi K (2022), AI in Brain Imaging to Indicate Dynamic Mental Illness, Anatechmaz, pp.139