Roberta Passiatore, a visiting fellow from the University of Bari Aldo Moro in Bari, Italy, and first author of the study, said researchers found alterations in the age-related network connectivity specifically during late adolescence and early adulthood. Schizophrenia symptoms typically develop early in life, often beginning in the mid-20s, with early onset occurring before 18.

The researchers found that younger individuals with increased risk have similar network connectivity as the brains seen in older patients. These findings could help identify a patient’s risk for developing 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,” Passiatore said. “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 highlights the importance of an age-oriented approach and leveraging multiple scans to identify risk in brain networks and potential genetic associations.

The findings could improve early detection and intervention strategies and offer potential biomarkers for investigating the role of specific genes and molecular pathways in developing schizophrenia.

The Translational Research in Neuroimaging and Data Science Center (TReNDS) is a collaboration among Georgia State University, the Georgia Institute of Technology and Emory University. It focuses on developing, applying and sharing advanced analytic approaches and neuroinformatic tools that leverage cutting-edge brain imaging and large-scale data analysis with a goal of translating these approaches into biomarkers that can help address relevant areas of brain health and disease.

This study was supported in part by National Institutes of Health grants R01MH118695 and R01MH123610.

Researchers utilized data from the U.K. Biobank, a large-scale biomedical database and research resource containing anonymised genetic, lifestyle and health information from half a million participants in the United Kingdom. U.K. Biobank’s database, which includes blood samples, heart and brain scans and genetic data of the volunteer participants, is globally accessible to approved researchers undertaking health-related research that’s in the public interest. U.K. Biobank’s resource was opened for research use in April 2012. Since then, 30,000 researchers from 100 countries have been approved to use it and more than 7,000 peer-reviewed papers that used the resource have been published.

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