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Machine Learning May Improve Schizophrenia Diagnosis

A machine learning approach that uses artificial intelligence (AI) to examine millions of brain cortical links can identify schizophrenia cases with a high degree of accuracy and predict symptom severity, new research shows.
In a brain imaging analysis, investigators led by Irina Rish, PhD, IBM T. J. Watson Research Center, Yorktown Heights, New York, found that, through use of alterations in connections between areas such as the thalamus and the primary cortex, schizophrenia patients could be distinguished from control persons with almost 75% accuracy.
In addition, results revealed that the severity of symptoms such as inattentiveness and bizarre behavior could be predicted from brain scans, although the specific connectivity changes were in different areas.
The study was published online May 16 in Schizophrenia.
Towards Reliable Biomarkers

This study, the investigators write, "represents a step towards finding more reliable objective neuroimaging biomarkers for diagnosing schizophrenia, which have higher reproducibility and generalization accuracy compared to the potential 'biomarkers' reported in association studies," which are typically derived from univariate statistical tests.
Noting that objective measurements have been emphasized as part of the recently proposed Research Domain Criteria (RDoC) initiative from the National Institutes of Mental Health, the researchers believe that their sparse multivariate regression approach to identifying predictors of symptom of severity "is likely to be extremely important in the move towards incorporating the RDoC approach to schizophrenia."
Schizophrenia is typically considered a disease of disrupted brain connectivity. However, the identification of pathologic neuroimaging patterns that are specific to schizophrenia and are related to symptom severity is a challenge. Such patterns could serve as biomarkers for the condition.

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