Researchers are discovering new ways to identify characteristics of mental disorders using fMRI scans

New research by a team at Georgia State University reveals surprising insights into brain pathways that could offer practitioners alternative ways to identify early signs of schizophrenia. The research is published in the news Nature mental health.

The study identifies connections that show unique spatial variation in the brain and increased sensitivity in the brains of patients with schizophrenia.

“This research marks an exciting leap forward and provides an entirely new lens to capture the complex, hidden fluctuations within functional brain networks,” said Vince Calhoun, University Professor of Psychology, one of the study's lead investigators.

Traditional functional brain connectivity studies, which use fMRI scans to identify patterns in brain activity, hold promise for elucidating changes in people with chronic brain disorders such as schizophrenia. But these studies typically focus on the linear relationships between brain regions and ignore other patterns.

The researchers developed a method to extract maps of large-scale brain networks from these typically neglected, nonlinear patterns, revealing a previously unknown dimension of brain organization in humans.

Strikingly, the team found that brain networks identified with this technique reflect differences between individuals with schizophrenia and controls that would otherwise remain hidden in conventional linear connectivity studies. The findings highlight the importance of exploiting these patterns to construct clinical biomarkers and inform theories of brain function and dysfunction.

“By focusing on nonlinear relationships – which are often overlooked in traditional neuroimaging – we uncover structured spatial patterns that could reveal the underpinnings of brain network function,” Calhoun said. “Crucially, these nonlinear patterns show disruption in people with schizophrenia, even when typical linear patterns appear unchanged.”

Calhoun is a Georgia Research Alliance Eminent Scholar with faculty appointments at Georgia Tech and Emory University and directs the collaborative tri-institutional Center for Translational Research in Neuroimaging and Data Science, or TReNDS Center. He is also a senior author of the study.

First author of the study Spencer Kinsey is a third-year Ph.D. student of neuroscience and team member of the TReNDS Center.

“We discovered these new functional brain connectivity patterns by using statistical methods that go beyond the patterns targeted by most studies,” Kinsey said. “Although functional connectivity studies typically aim to analyze linear patterns in brain connectivity, we focused instead on nonlinear connectivity patterns.”

The study's principal investigator, Armin Iraji, is an assistant professor of computer science and neuroscience and part of the TReNDS research team.

“A decade of dedicated research has laid the foundation for a groundbreaking platform that will reshape brain signals in new dimensions,” he said. “By using advanced mathematical techniques and transcending conventional spatial and temporal limitations, we are poised to unlock the secrets of the brain, uncover hidden intrinsic patterns and push the boundaries of neuroscience. This innovative approach promises to revolutionize our understanding of mental disorders, aging, neurodegenerative diseases and more.”

“This discovery brings us closer to identifying a potential brain-based biomarker for schizophrenia, with profound implications for early diagnosis and targeted intervention,” said Calhoun.

The TReNDS Center is a joint research center of Georgia State, the Georgia Institute of Technology and Emory University. It aims to develop, apply and share advanced analytical techniques, large-scale data and neuroinformatics tools to harness advanced brain imaging data and convert them into biomarkers that can be used to identify relevant aspects of health and disease of the brain.

Offered by Georgia State University