Andrew Tarantola reports via Engadget: Schizophrenia is not a particularly common mental health disorder in America, affecting just 1.2 percent of the population or around 3.2 million people, but its effects can be debilitating. However, pioneering research conducted by IBM and the University of Alberta could soon help doctors diagnose the onset of the disease and the severity of its symptoms using a simple MRI scan and a neural network built to look at blood flow within the brain. 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. What's more, the model managed to also predict the severity of symptoms once they set in. The study has been published in the journal Nature.