New fMRI Analysis Could Improve Schizophrenia Treatment

A new tool can improve how functional MRI (fMRI) analyzes key brain patterns associated with particular types of mental illness, such as schizophrenia, improving diagnosis and treatment assessments, according to researchers.

In a new article published in NeuroImage, investigators from the University of Maryland, Baltimore County, detailed a new image analysis method, called independent vector analysis for common subspace extraction (IVA-CS), can be used to categorize subgroups of fMRI data based only on brain activity.

Spatial maps of the common components in six categories: Motor; COG, cognitive control; DM, default mode; AUD, auditory, VIS, visual; CB, cerebellum. (a)–(d) for SZ group and (e)–(h) for HC group. The number of independent components (ICs) that are composited in each subfigure is listed and different colors refer to the spatial maps of individual components. The anatomical regions of the activation in the common components are provided in the supplementary materials. Courtesy: NeuroImage

This capability, said Tülay Adali, Ph.D., distinguished university professor, and Qunfang Long, an electrical engineering Ph.D., candidate, confirms the connection between brain activity and various mental illnesses. In particular, they said, using IVA-CS, they were able to identity subgroups of schizophrenia patients by the fMRI data they evaluated.

“The most exciting part is that we found out the identified subgroups possess clinical significance by looking at their diagnostic symptoms,” Long said in a statement. “This finding encouraged us to put more effort into the study of subtypes of patients with schizophrenia using neuroimaging data.”

Prior to their discovery, a clear way to group schizophrenia patients based on brain imaging alone did not exist. Their IVA-CS method is particularly useful, they explained, because it maintains the nuances in the data, but it still renders statistically significant groupings.

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“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,” Adali said. “Now, we can perform this analysis effectively, and can identify meaningful groupings of subjects.”

Clinical Impact

In addition to data analysis, IVA-CS is clinically valuable. Depending on the patient, a mental illness can present in a variety of ways, and treatments are rarely one-size-fits-all. Consequently, there has been a need for a method to determine whether a particular therapy has been impactful.

By working collaboratively with Vince Calhoun, Ph.D., director of the Center for Translational Research in Neuroimaging and Data Science at Georgia State University, Adali and Long’s development can give clinicians an objective way to analyze fMRI results over time from patients from a similar diagnostic subgroup, making it easier for them to spot changes in response to treatment.

To make IVA-CS further valuable to patient care, Adali and Long have plans for a longitudinal study that will investigate which treatments are most effective for patients with specific mental illnesses, such as addiction and substance abuse in adolescents.