Classification of schizophrenia-associated brain regions in resting-state fMRI

Recently, advances in neuroscience have attracted attention to the diagnosis, treatment, and damage to schizophrenia-associated brain regions using resting-state functional magnetic resonance imaging (rs-fMRI). This research is immersed in the endowment of machine learning approaches for discriminating schizophrenia patients to provide a viable solution. Toward these goals, firstly, we implemented a two sample t-tests to find the activation difference between schizophrenia patients and healthy controls. The average activation in control is higher than the average activation of the patient. Secondly, we implemented the correlation technique to find variations on presumably hidden associations between brain structure and its associated function. Moreover, current results support the viewpoint that the resting-state function integration is helpful to gain insight into the pathological mechanism of schizophrenia. Finally, Lasso regression is used to find a low-dimensional integration of the rs-fMRI and their experimental results showed that SVM classifier surpasses nine algorithms provided the best results with good accuracy of 94%.

Automated summary

Recently, the use of resting-state functional magnetic resonance imaging (rs-fMRI) has gained attention in the field of neuroscience for diagnosing, treating, and understanding schizophrenia-associated brain regions. Machine learning approaches have been employed to distinguish between schizophrenia patients and healthy controls. Two sample t-tests revealed higher average activation in the control group compared to the patient group. The correlation technique was used to uncover hidden associations between brain structure and function. The integration of resting-state function provides insight into the pathological mechanism of schizophrenia. Lastly, Lasso regression and SVM classifier yielded the best results with an accuracy of 94%.