Summary: A new study identifies previously hidden brain network patterns in schizophrenia by focusing on nonlinear connectivity, offering potential biomarkers for early diagnosis. Traditional imaging methods often overlook these patterns, but researchers developed advanced statistical tools to uncover this new dimension of brain organization.
Their findings highlight disruptions in functional brain networks unique to schizophrenia, even when traditional connectivity measures appear normal. This approach could revolutionize how brain disorders are diagnosed and understood, paving the way for targeted treatments.
Key Facts:
- Nonlinear brain network patterns reveal disruptions in schizophrenia undetected by traditional imaging.
- Advanced statistical methods unlock hidden brain signals, offering insights into network function.
- Findings could lead to early diagnosis tools and innovative treatments for brain disorders.
Source: Georgia State University
New research by a team at Georgia State University is uncovering surprising insights about brain pathways that could offer alternative ways for practitioners to identify early signs of schizophrenia.
The research is published in the journal Nature Mental Health.
The study identifies connections that show unique spatial variation across the brain and enhanced sensitivity in the brains of patients with schizophrenia.
“This research marks an exciting leap forward, offering an entirely new lens to capture the complex, hidden fluctuations within functional brain networks,” said Distinguished University Professor of Psychology Vince Calhoun, one of the principal investigators on the study.
Traditional functional brain connectivity studies, which use fMRI scans to identify patterns in brain activity, hold promise for illuminating alterations in people with chronic brain disorders such as schizophrenia.
But these studies typically focus on the linear relationships between brain areas and neglect other patterns.
The researchers developed a method to extract maps of large-scale brain networks from these typically neglected, nonlinear patterns, revealing a previously unrecognized 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 be hidden from conventional linear connectivity studies.
The findings emphasize the importance of leveraging these patterns to construct clinical biomarkers and inform theories of brain function and disfunction.
“By focusing on nonlinear relationships — 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 disruptions in individuals 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 leads the collaborative tri-institutional Center for Translational Research in Neuroimaging and Data Science, or TReNDS Center. He is also a senior author on the study.
First author of the study Spencer Kinsey is a third-year Ph.D. student in neuroscience and a team member of the TReNDS Center.
“We discovered these new functional brain connectivity patterns by using statistical methods that move beyond the patterns that most studies target,” Kinsey said.
“While functional connectivity studies typically aim to analyze linear patterns in brain connectivity, we instead focused on nonlinear connectivity patterns.”
The lead principal investigator on the study, 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 reimagine brain signals in new dimensions,” he said.
“By leveraging advanced mathematical techniques and transcending conventional spatial and temporal limitations, we’re poised to unlock the brain’s secrets, 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.”
The research was funded by the U.S. National Institutes of Health. It also received funding, in part, from Georgia State’s Research Innovation and Scholarly Excellence (RISE) initiative, which supports transformative projects across research fields.
“This discovery brings us closer to identifying a potential brain-based biomarker for schizophrenia, with profound implications for early diagnosis and targeted intervention,” Calhoun said.
About this brain mapping and schizophrenia research news
Author: Noelle Reetz
Source: Georgia State University
Contact: Noelle Reetz – Georgia State University
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls” by Vince Calhoun et al. Nature Mental Health
Abstract
Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls
Schizophrenia is a chronic brain disorder associated with widespread alterations in functional brain connectivity.
Although data-driven approaches such as independent component analysis are often used to study how schizophrenia impacts linearly connected networks, alterations within the underlying nonlinear functional connectivity structure remain largely unknown.
Here we report the analysis of networks from explicitly nonlinear functional magnetic resonance imaging connectivity in a case–control dataset.
We found systematic spatial variation, with higher nonlinear weight within core regions, suggesting that linear analyses underestimate functional connectivity within network centers.
We also found that a unique nonlinear network incorporating default-mode, cingulo-opercular and central executive regions exhibits hypoconnectivity in schizophrenia, indicating that typically hidden connectivity patterns may reflect inefficient network integration in psychosis.
Moreover, nonlinear networks including those previously implicated in auditory, linguistic and self-referential cognition exhibit heightened statistical sensitivity to schizophrenia diagnosis, collectively underscoring the potential of our methodology to resolve complex brain phenomena and transform clinical connectivity analysis.
