Breakthrough in Analysis of fMRI Data in Mental Illnesses; Novel Analysis Tool Allows Segregation of Schizophrenia into Clinically Consistent Subgroups on Basis of Brain Imaging Alone; Approach May Prove Useful in Both Diagnosis and Treatment Monitoring

Researchers at the University of Maryland, Baltimore County (UMBC) have developed tools to improve the analysis of functional magnetic resonance imaging (fMRI) data. Tülay Adali, PhD, Professor of Computer Science and Electrical Engineering and Director of UMBC's Machine Learning for Signal Processing Lab, and Qunfang Long, a PhD candidate at UMBC in Electrical Engineering, have spearheaded ground-breaking work identifying key patterns in brain imaging for those with particular mental illnesses, such as schizophrenia. This new research has been published in the August 1, 2020 issue of NeuroImage ( Their work can assist in diagnosis and treatment of patients with mental illnesses that can be difficult to identify. It can also show medical practitioners whether the current treatments have or have not been working based on image groupings. The open-access article is titled “Independent Vector Analysis for Common Subspace Analysis: Application to Multi-Subject fMRI Data Yields Meaningful Subgroups of Schizophrenia.” The image analysis method developed by Dr. Adali and Mr. Long is called independent vector analysis for common subspace extraction (IVA-CS). Through this method, they were able to categorize subgroups of fMRI data based solely on brain activity, proving that there is a connection between brain activity and certain mental illnesses. In particular, they were able to identify subgroups of schizophrenia patients using the fMRI data that they analyzed. Previously, there was not a clear way to group schizophrenia in patients based on brain imaging alone, but the methods developed by Dr. Adali and Mr. Long, and colleagues, show that there is a significant connection between a patient's brain activity and his/her diagnosis.
Login Or Register To Read Full Story