A novel approach to existing functional neuroimaging datasets led to a breakthrough in understanding genetic and clinical risk for schizophrenia in the context of how brain regions communicate with each other. The research was published in PNAS—read the paper here.
Researchers, including Lieber Institute Investigator Giulio Pergola, Ph.D., and others from the University of Bari Aldo Moro (Bari, Italy) and the Tri-institutional Center for Translational Research in Neuroimaging and Data Science (Atlanta, GA) employed multiple functional magnetic resonance imaging (fMRI) acquisitions from the same participants to detect age-related changes in measures of brain connectivity.
The next question was whether these changes were associated with schizophrenia risk. As schizophrenia runs in families, there is a significant genetic component to it that can be used to index individual risk. A more clinically oriented approach, instead, identifies individuals with symptoms that, while not meeting the criteria for diagnosis, may precede the onset of schizophrenia.
By analyzing data from 9,236 individuals in different age stages, available at the University of Bari Aldo Moro, the Lieber Institute of Brain Development, and from the public UK Biobank, Adolescent Brain Cognitive Development Study, and the Philadelphia Neurodevelopmental Cohort, the study revealed that alterations in prefrontal-sensorimotor and cerebellar-occipitoparietal brain connections characterize young siblings of schizophrenia patients and are linked to the genetic risk for the disorder.
These alterations were also observed in patients with schizophrenia and individuals displaying subthreshold psychotic symptoms, suggesting a convergence between genetic and clinical risk factors. Notably, these alterations were most evident during late adolescence or early adulthood, which is closer to the typical age of onset for schizophrenia, and not before or after that age stage. This finding highlights the importance of an age-oriented approach and of leveraging multiple scans from each participant for identifying risk brain networks and potential genetic associations.
The study’s insights highlight brain measures that could be used for improved early detection and intervention strategies. They also offer potential biomarkers that could be used to investigate the role of specific genes and pathways in developing schizophrenia in view of novel drug target identification. With further research and development, functional neuroimaging patterns associated with risk have the potential to enhance our understanding of the disorder and impact on the lives of at risk individuals.
UK Biobank is a large-scale biomedical database and research resource containing anonymized genetic, lifestyle and health information from half a million UK participants. UK Biobank’s database, which includes blood samples, heart and brain scans and genetic data of the volunteer participants, is globally accessible to approved researchers who are undertaking health-related research that’s in the public interest. UK 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 6,000 peer-reviewed papers that used the resource have now been published.