Summary: Researchers have identified age-related changes in brain network connectivity that correlate with increased risk for developing schizophrenia.
Using a data-driven framework called Neuromark, the team analyzed fMRI scans, genetic profiles, and clinical data from 9,236 individuals to map how brain networks change with age and how those changes relate to genetic risk for schizophrenia. The study found differences in specific brain circuits—most notably prefrontal–sensorimotor and cerebellar–occipitoparietal connections—appearing during late adolescence and early adulthood. These patterns were present in people diagnosed with schizophrenia, in their unaffected siblings, and in young people showing subthreshold psychotic symptoms.
The results point to measurable brain network signatures that may improve early detection, guide interventions, and serve as biomarkers for schizophrenia risk.
Key Facts:
- Age-sensitive changes in functional brain connectivity are associated with increased risk for schizophrenia.
- Researchers applied Neuromark, a hybrid data-driven method, to reliably extract brain networks from neuroimaging data collected from 9,236 participants.
- Altered connectivity in prefrontal–sensorimotor and cerebellar–occipitoparietal circuits correlated with genetic risk scores and appeared most prominently in adolescence and early adulthood, offering potential biomarkers for earlier detection and intervention.
Source: Georgia State University
New research led by scientists associated with Georgia State University’s TReNDS Center has revealed age-related brain network changes linked to schizophrenia risk.
The findings could help clinicians spot individuals at higher risk earlier and refine treatment strategies during critical developmental windows.
The study is published in the Proceedings of the National Academy of Sciences (PNAS).
This international collaboration includes researchers from the University of Bari Aldo Moro, the Lieber Institute for Brain Development, and the Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) based at Georgia State University.
At the center of the analysis is Neuromark, a hybrid, data-driven technique developed at TReNDS that extracts reproducible functional brain networks across large neuroimaging collections. Neuromark enables consistent comparison of network features across individuals and age groups.
The study began with functional MRI (fMRI) data to identify how functional network connectivity (FNC) evolves from childhood into adulthood and whether those trajectories relate to schizophrenia risk. To improve measurement accuracy, researchers averaged roughly three fMRI sessions per person rather than relying on single-session scans.
“We combined more than 9,000 datasets using an approach that computes functional brain networks in a way that is both adaptive and comparable across people,” said Distinguished University Professor Vince Calhoun, director of the TReNDS center. “We found that genetic risk for schizophrenia shows up in brain network interactions even among people without a diagnosis, and that these changes diminish with age. This motivates further study into using network interactions as an early risk indicator.”
Data came from five major cohorts spanning multiple developmental stages: the University of Bari Aldo Moro, the Lieber Institute for Brain Development, the U.K. Biobank, the Adolescent Brain Cognitive Development Study, and the Philadelphia Neurodevelopmental Cohort. Combining fMRI with genetic and clinical measures, the researchers identified consistent alterations in prefrontal–sensorimotor and cerebellar–occipitoparietal connectivity linked to polygenic risk scores for schizophrenia.
These connectivity differences were evident in diagnosed patients, in their unaffected siblings, and in young people with subclinical psychotic symptoms, particularly during late adolescence and early adulthood.
First author Roberta Passiatore, a visiting fellow from the University of Bari Aldo Moro, reported that the age-dependent network disruptions emerged during adolescence and early adulthood—periods that align with typical onset windows for schizophrenia. Younger individuals with elevated genetic risk displayed connectivity patterns resembling those seen in older patients, suggesting a developmental trajectory that could flag later vulnerability.
“Working at TReNDS under Professor Calhoun’s mentorship allowed us to develop an approach that pools multiple functional acquisitions and reveals a distinct brain signature linked to schizophrenia risk,” Passiatore said. “These age-related trajectories may improve early diagnosis and inform timely interventions for at-risk individuals.”
The research underscores the value of age-sensitive analysis and multiple-scan strategies for identifying network-based risk markers and linking them to genetic risk. Such biomarkers could help focus preventive efforts and clarify the molecular pathways involved in schizophrenia development.
The Translational Research in Neuroimaging and Data Science Center (TReNDS) is a partnership among Georgia State University, the Georgia Institute of Technology, and Emory University. TReNDS develops, applies, and shares advanced neuroimaging analytics and neuroinformatic tools to translate large-scale brain imaging and genetic data into clinically useful biomarkers.
Funding: This study was supported in part by National Institutes of Health grants R01MH118695 and R01MH123610.
About this schizophrenia research news
Author: Noelle Reetz ([email protected])
Source: Georgia State University
Contact: Noelle Reetz – Georgia State University
Image: The image is credited to Neuroscience News
Original Research: Closed access. “Changes in patterns of age-related network connectivity are associated with risk for schizophrenia” by Vince Calhoun et al. PNAS
Abstract
Changes in patterns of age-related network connectivity are associated with risk for schizophrenia
Functional network connectivity (FNC) measured with fMRI is altered in schizophrenia and can reflect genetic liability or subclinical symptoms that precede clinical onset, which often occurs in early adulthood. Age-sensitive changes in FNC may therefore mark schizophrenia risk.
Using independent component analysis on data from 9,236 individuals spanning childhood to adulthood, researchers estimated FNC trajectories and compared first-degree relatives of patients—mainly unaffected siblings—with age-matched neurotypical controls. To increase robustness, the analysis used an average of three fMRI sessions per participant. The study then assessed associations with polygenic risk scores for schizophrenia and tested whether the same FNC patterns appeared in adult patients and young people with subthreshold psychotic symptoms.
Age-sensitive, risk-related FNC patterns emerged during adolescence and early adulthood but were not present earlier in development. Younger siblings consistently resembled the connectivity patterns of older controls: they showed reduced FNC in a cerebellar–occipitoparietal circuit and increased FNC in two prefrontal–sensorimotor circuits compared with age-matched controls. Two of these alterations were also detected in adult patients, and all were associated with polygenic risk for schizophrenia (explained variance R² ranged roughly from 0.02 to 0.05). Young individuals with subclinical psychotic symptoms showed network changes in the same direction as siblings, supporting the view that age-related FNC correlates with genetic risk and can be detected with MRI in young participants.