"Schizophrenia is not a particularly common mental health disorder in America, affecting just 1.2 percent of the population (around 3.2 million people), but its effects can be debilitating.
The University of Alberta and IBM "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."
Limited details but likely a convolutional neural network (CNN) trained on the MRI scans to identify blood location on the brand. And likely recurrent neural networks (RNN) to look at the flow over time.
"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."
The results of the IBM and University of Alberta research demonstrated that, even on more challenging neuroimaging data collected from multiple sites (different machines, across different groups of subjects etc.) the machine learning algorithm was able to discriminate between patients with schizophrenia and the control group with 74% accuracy using the correlations in activity across different areas of the brain." "Additionally, the research showed that functional network connectivity could also help determine the severity of several symptoms after they have manifested in the patient, including inattentiveness, bizarre behavior and formal thought disorder, as well as alogia, (poverty of speech) and lack of motivation. The prediction of symptom severity could lead to a more quantitative, measurement-based characterization of schizophrenia; viewing the disease on a spectrum, as opposed to a binary label of diagnosis or non-diagnosis. This objective, data-driven approach to severity analysis could eventually help clinicians identify treatment plans that are customized to the individual.