This retrospective analysis also showed the technology predicted the severity of specific symptoms in schizophrenia patients with significant correlation, based on correlations between activity observed across different regions of the brain. This pioneering research could furthermore help scientists identify more reliable objective neuroimaging biomarkers that could be used to predict schizophrenia and its severity. The findings were published in Nature’s partner journal, Schizophrenia Research.
Schizophrenia is a chronic and debilitating neurological disorder that affects 7 or 8 out of every 1,000 people. People with schizophrenia can experience hallucinations, delusions or thought disorders, along with cognitive impairments, such as an inability to pay attention and physical impairments, such as movement disorders.
Innovative multidisciplinary approach
Dr. Serdar Dursun, a Professor of Psychiatry & Neuroscience with the University of Alberta, believes the research together with IBM offers a unique, innovative multidisciplinary approach. It opens new insights and advances our understanding of the neurobiology of schizophrenia, which may help to improve the treatment and management of the disease.
“We’ve discovered a number of significant abnormal connections in the brain that can be explored in future studies, and AI-created models bring us one step closer to finding objective neuroimaging-based patterns that are diagnostic and prognostic markers of schizophrenia.”
In their paper, the researchers analyzed de-identified brain functional Magnetic Resonance Imaging (fMRI) data from the open data set, Function Biomedical Informatics Research Network (fBIRN) for patients with schizophrenia and schizoaffective disorders, as well as a healthy control group.
MRI measures brain activity through blood flow changes in particular areas of the brain. Specifically, the fBIRN data set reflects research done on brain networks at different levels of resolution, from data gathered while study participants conducted a common auditory test. Examining scans from 95 participants, researchers used machine learning techniques to develop a model of schizophrenia that identifies the connections in the brain most associated with the illness.
Machine learning algorithms 74% accurate
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, such as inattentiveness, bizarre behavior and formal thought disorder, as well as alogia, (poverty of speech) and lack of motivation.
According to IBM, 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.
“The ultimate goal of this research effort is to identify and develop objective, data-driven measures for characterizing mental states, and apply them to psychiatric and neurological disorders” said Ajay Royyuru, Vice President of Healthcare & Life Sciences, IBM Research. “We also hope to offer new insights into how AI and machine learning can be used to analyze psychiatric and neurological disorders to aid psychiatrists in their assessment and treatment of patients.”
As part of the ongoing partnership, researchers will continue to investigate areas and connections in the brain that hold significant links to schizophrenia. Work will continue on improving the algorithms by conducting machine learning analysis on larger datasets, and by exploring ways to extend these techniques to other psychiatric disorders such as depression or post-traumatic stress disorder.