"This unique, innovative multidisciplinary approach opens new insights and advances our understanding of the neurobiology of schizophrenia, which may help to improve the treatment and management of the disease," Dr. Serdar Dursun, a Professor of Psychiatry & Neuroscience with the University of Alberta, said in a statement.
MRI scans showing statistically significant differing blood flows within the brain - Image: IBM
The research team first trained its neural network on a 95-member dataset of anonymized fMRI images from the Function Biomedical Informatics Research Network which included scans of both patients with schizophrenia and a healthy control group. These images illustrated the flow of blood through various parts of the brain as the patients completed a simple audio-based exercise. From this data, the neural network cobbled together a predictive model of the likelihood that a patient suffered from schizophrenia based on the blood flow. It was able to accurately discern between the control group and those with schizophrenia 74 percent of the time.
"We've discovered a number of significant abnormal connections in the brain that can be explored in future studies," Dursun continued, "and AI-created models bring us one step closer to finding objective neuroimaging-based patterns that are diagnostic and prognostic markers of schizophrenia."
What's more, the model managed to also predict the severity of symptoms once they set in. These insights could lead researchers to more effective diagnostic tools and treatment options. And why wouldn't they? IBM's most famous AI, Watson, has already shown that neural networks are surprisingly adept at coming up with effective cancer treatment regimens.