Progress at the intersection of artificial intelligence and paediatric neuroimaging necessitates large, heterogeneous datasets to generate robust and generalisable models. Retrospective analysis of clinical brain MRI scans offers a promising avenue to augment prospective research datasets, leveraging the extensive repositories of scans routinely acquired by hospital systems in the course of clinical care. Here, we present a systematic protocol for identifying ‘scans with limited imaging pathology’ through machine-assisted manual review of radiology reports.

The protocol employs a standardised grading scheme developed with expert neuroradiologists and implemented by non-clinician graders. Categorising scans based on the presence or absence of significant pathology and image quality concerns facilitates the repurposing of clinical brain MRI data for brain research. Such an approach has the potential to harness vast clinical imaging archives—exemplified by over 250 000 brain MRIs at the Children’s Hospital of Philadelphia—to address demographic biases in research participation, to increase sample size and to improve replicability in neurodevelopmental imaging research. Ultimately, this protocol aims to enable scalable, reliable identification of clinical control brain MRIs, supporting large-scale, generalisable neuroimaging studies of typical brain development and neurogenetic conditions.

Studies using datasets generated from this protocol will be disseminated in peer-reviewed journals and at academic conferences.

Adolescence is a critical period marked by rapid brain development and the onset of many mental health disorders. Brain MRI studies during adolescence, especially when paired with behavioural phenotypes and information about genetic risk factors, hold promise to advance early identification of mental health risk and spur the creation of targeted treatments to improve patient function, prognosis and quality of life. However, prospective neuroimaging is costly and time-intensive, and individuals who participate may not be reflective of the general population. These challenges are compounded when examining adolescents, as many families lack the time, energy or resources to participate in studies that use research-grade imaging. Repurposing clinical MRIs obviates many of the challenges of neuroimaging research. Here, we describe the brain-behaviour-genetics study protocol. This protocol describes procedures used to recruit participants with recent high-quality clinical brain MRIs and prospectively acquire genetic and sociobehavioural data, resulting in a highly cost-efficient design that harnesses a vast and underused neuroscientific resource.

The brain-behaviour-genetics protocol aims to recruit 1000 adolescents who have clinical brain MRIs contained in Children’s Hospital of Philadelphia’s electronic health record. One or both parents of the adolescent proband will be recruited when possible. Parents and adolescents will complete a series of self-report scales spanning the domains of mental health, trauma, risk and resilience. Saliva samples will be collected from the adolescent and at least one biological parent, using an at-home saliva collection kit. Subsequent analysis will examine associations between brain development, genetics and behavioural measures in adolescence.

Approval for the study had been obtained from the Children’s Hospital of Philadelphia’s institutional review board (IRB #23–0 20 851). Results will be published in peer-reviewed journals.