Neuroimaging Approach Improves Schizophrenia Diagnosis

A new study led by experts at Georgia State University’s TReNDS Center has discovered age-related alterations in brain patterns that are linked to the likelihood of developing schizophrenia.

The discovery could aid clinicians in identifying patients at risk of developing mental illness early and improving treatment options. The findings were reported in the Proceedings of the National Academy of Sciences.

The study is a partnership between scientists from the University of Bari Aldo Moro, the Lieber Institute of Brain Development, and Georgia State University’s Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS).

The TReNDS center created novel analytic approaches for the investigation. The researchers used Neuromark, a hybrid data-driven method, to generate credible brain networks from neuroimaging data, which were then further evaluated in the study.

The researchers began with functional MRI scans (fMRI) to detect age-related changes in brain connectivity and their relationship to the risk of schizophrenia. The study identified individuals who were at high risk of developing psychosis in late adolescence and early adulthood.

The application of this unique method to existing functional neuroimaging datasets resulted in a breakthrough in understanding both genetic and clinical risks for schizophrenia in the context of how brain areas communicate with one another.

“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,” said Distinguished University Professor Vince Calhoun, director of the TReNDS center.

“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 researchers examined data from 9,236 people of various ages collected by the University of Bari Aldo Moro, the Lieber Institute of Brain Development, the United Kingdom Biobank, the Adolescent Brain Cognitive Development Study, and the Philadelphia Neurodevelopmental Cohort.

They discovered that changes in prefrontal-sensorimotor and cerebellar-occipitoparietal brain connections are connected to genetic risk for schizophrenia using fMRI scans, genetic and clinical assessments. These changes were seen in people with schizophrenia, their neurotypical siblings, and those with subthreshold psychotic symptoms.

According to Roberta Passiatore, a visiting fellow at the University of Bari Aldo Moro in Bari, Italy, and the study’s primary author, researchers discovered changes in age-related network connectivity, particularly between late adolescence and early adulthood. Schizophrenia symptoms often appear early in life, commonly in the mid-20s, with early onset occurring before the age of 18.

The researchers discovered that younger people at increased risk have similar network connectivity as older patients’ brains. These data may aid in determining a patient’s likelihood of getting 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 emphasizes the relevance of using a multi-scan strategy to uncover risk in brain networks and potential genetic correlations.

The findings could improve early identification and intervention efforts, as well as provide possible biomarkers for studying the involvement of certain genes and cellular pathways in schizophrenia development.

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