The Treatment for Schizophrenia Gets Improved Due to Improved Way of Analyzing fMRI Data

The Treatment for Schizophrenia Gets Improved Due to Improved Way of Analyzing fMRI Data

Published: 29-Jun-2020 | Published By: Market Research Store

University of Maryland’s researchers have introduced new tools that have improved the analysis of fMRI data. Tülay Adali and Qunfang Long headed a pioneering work that identifies the key patterns in the brain imaging particularly in case of mental illnesses. This research study is available in NeuroImage Volume 216. With the help of this research the diagnosis and treatment of patients suffering from mental illnesses can be identified easily. This will assist the medical practitioners to determine whether the current treatments are working or not on the basis of the image groupings.

The image analysis method has been termed as independent vector analysis (IVA) for common subspace extraction (CS).With the help of this method Adali and Long were able to classify fMRI data subgroups on the basis of brain activity. This proved that there is a link between certain mental sickness and brain activity. In particular, with the help of the fMRI data analysis they were able to categorize subgroups of schizophrenia patients.

Earlier, brain imaging alone couldn’t help in identifying schizophrenia in patients but with the help of this method it has become a bit easy. Long made statements giving a clear idea about the study that, “The most exciting part is that we found out the identified subgroups possess clinical significance by looking at their diagnostic symptoms. This finding encouraged us to put more effort into the study of subtypes of patients with schizophrenia using neuroimaging data.”

Furthermore Adali added that, “Now that data-driven methods have gained popularity, a big challenge has been capturing the variability for each subject while simultaneously performing analysis on fMRI datasets from a large number of subjects. Now we can perform this analysis effectively, and can identify meaningful groupings of subjects.”
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