At the Georgia State University’s TReNDS Center, a new study headed by researchers has determined age-related variations in brain patterns linked to the risk of developing schizophrenia.
Image Credit: Georgia State University
The breakthrough could help clinicians find the risk for developing mental illness earlier and enhance treatment options.
The study has been reported in the journal Proceedings of the National Academy of Sciences (PNAS).
The research ispart of a collaboration by experts from the University of Bari Aldo Moro, the Lieber Institute of Brain Development, and the Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) based at Georgia State University.
New analytic approaches developed at the TReNDS center were utilized by the study. Scientists utilized a hybrid, data-driven method known as Neuromark to extract trustworthy brain networks from the neuroimaging data, which were then additionally examined in the study.
Scientists began with functional MRI scans (fMRI) to detect age-related changes happening in brain connectivity and their link with schizophrenia risk. The study determined high-risk individuals for coming up with psychosis at the time of early adulthood and late adolescence.
Utilizing this novel method to present functional neuroimaging datasets led to a discovery in comprehending both clinical and genetic risks for schizophrenia in the context of how brain regions communicate with each other.
This study combined over 9,000 data sets using an approach which computes functional brain networks adaptively while also allowing us to summarize and compare across individuals.
Vince Calhoun, Distinguished University Professor and Director, TReNDS center, Georgia State University
Calhoun added, “This led us to a really interesting result showing that genetic risk for schizophrenia is detectable in brain network interactions even for those who do not have schizophrenia, and this change reduces with age. These results also motivate us to do further investigation into the potential of functional brain network interactions to be used as an early risk detector.”
The research group examined data from 9,236 individuals in different age stages that have been acquired by the University of Bari Aldo Moro, the Lieber Institute of Brain Development, the U.K. Biobank, the Adolescent Brain Cognitive Development Study and the Philadelphia Neurodevelopmental Cohort.
By utilizing fMRI scans, genetic and clinical measures, the researchers discovered that alterations in cerebellar-occipitoparietal brain and prefrontal-sensorimotor connections are associated with genetic risk for schizophrenia.
Such alterations were noted in patients with schizophrenia, their neurotypical siblings, and those exhibiting under-threshold psychotic symptoms.
Roberta Passiatore, a visiting fellow from the University of Bari Aldo Moro in Bari, Italy, and first author of the study, stated scientists discovered modifications in the age-related network connectivity, particularly during early adulthood and late adolescence. Normally, schizophrenia symptoms develop early in life, frequently beginning in the mid-20s, with early onset happening before 18.
The scientists discovered that younger individuals with high risk have similar network connectivity as the brains seen in older patients. Such findings could help determine a patient’s risk for developing a disease later in life.
Visiting TReNDS under the expert guidance of Professor Calhoun has been an exceptional experience. It provided me with a unique opportunity to develop an innovative approach that led to the discovery of a distinct brain signature for assessing the risk of schizophrenia by pooling multiple functional acquisitions.
Roberta Passiatore, Visiting Fellow, University of Bari Aldo Moro
Passiatore added, “These findings trace a risk-related brain trajectory across multiple age stages with the potential to enhance our understanding of the disorder and to improve early diagnosis and intervention efforts, with a significant impact on the lives of at-risk individuals.”
The study emphasizes the significance of an age-oriented method and leveraging several scans to determine risk in brain networks and possible genetic associations.
The findings could enhance early detection and intervention strategies and provide possible biomarkers for examining the role of particular genes and molecular pathways in developing schizophrenia.
The Translational Research in Neuroimaging and Data Science Center (TReNDS) is a partnership between Georgia State University, the Georgia Institute of Technology, and Emory University.
It concentrates on developing, applying, and sharing advanced analytic methods and neuroinformatic tools that leverage cutting-edge brain imaging and large-scale data analysis to translate such approaches into biomarkers that could help address applicable areas of brain health and disease.
This study was financially supported in part by National Institutes of Health grants R01MH118695 and R01MH123610.
Journal Reference
Passiatore, R., et al. (2023) Changes in patterns of age-related network connectivity are associated with risk for schizophrenia. Proceedings of the National Academy of Sciences. doi.org/10.1073/pnas.2221533120.
Source: https://www.gsu.edu/
