13 Jul Machine Learning Can Help Identify First Episodes of Schizophrenia, As Well As Treatment Response
MedicalResearch.com Interview with:
Bo Cao, Ph.D.
Department of Psychiatry
Faculty of Medicine & Dentistry
University of Alberta
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: Schizophrenia is a severe psychiatric disorder that comes with delusions, hallucinations, poor motivation, cognitive impairments.
The economic burden of schizophrenia was estimated at $155.7 billion in 2013 alone in the United States. Schizophrenia usually emerges early in life and can potentially become a lifetime burden for some patients. Repeated untreated psychotic episodes may be associated with irreversible alterations of the brain. Thus, it is crucial to identify schizophrenia early and provide effective treatment. However, identifying biomarkers in schizophrenia during the first episode without the confounding effects of treatment has been challenging. Limited progress has been made in leveraging these biomarkers to establish diagnosis and make individualized predictions of future treatment responses to antipsychotics.
In a recent study by Dr. Cao and his colleagues, they successfully identified the first-episode drug-naïve schizophrenia patients (accuracy 78.6%) and predict their responses to antipsychotic treatment (accuracy 82.5%) at an individual level by using a machine learning algorithm and the functional connections of a brain region called the superior temporal cortex.
MedicalResearch.com: What should readers take away from your report?
Response: The methods and findings in this paper could provide a critical step towards individualized identification and treatment response prediction in first-episode drug-naïve schizophrenia. It could complement other biomarkers in the development of precision medicine approaches for this severe mental disorder.
MedicalResearch.com: What recommendations do you have for future research as a result of this work?
Response: We hope to help the clinicians diagnose schizophrenia and make optimized treatment plans based on objective biomarkers in the future, and this study is just a beginning among other efforts in the field of computational psychiatry research.
MedicalResearch.com: Is there anything else you would like to add?
Response: This study was supported in part by the NARSAD Young Investigator Grant of the Brain & Behavior Research Foundation.
Dr. Cao is an assistant professor of the Department of Psychiatry at the University of Alberta. He is also a member of the Computational Psychiatry group with a team of excellent clinicians and scientists.
Bo Cao, Raymond Y. Cho, Dachun Chen, Meihong Xiu, Li Wang, Jair C. Soares, Xiang Yang Zhang. Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity. Molecular Psychiatry, 2018; DOI: 10.1038/s41380-018-0106-5
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