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Introducing BrainChart: A Benchmark Tool for Brain Growth
At childhood wellness visits, pediatricians measure a variety of things to determine how children are growing, such as height, weight, and head circumference. They don't have a way to measure brain development yet, but researchers at Children's Hospital of Philadelphia have created a tool that uses magnetic resonance imaging (MRI) data to provide benchmarks of brain development over the human lifespan.
A collaborative international team led by researchers from the Lifespan Brain Institute of CHOP and the University of Pennsylvania, as well as the University of Cambridge, described the new resource, BrainChart, in the journal Nature. It would allow researchers — and in the future, the researchers hope, clinicians — to analyze brain development in children against reference charts, much like reference charts used for height and weight.
"Benchmarks for height and weight are critical for clinical applications and also for research," said Aaron Alexander-Bloch, MD, PhD, director of the Brain-Gene-Development Lab in the Department of Child and Adolescent Psychiatry and Behavioral Sciences at CHOP, and assistant professor of Psychiatry at the University of Pennsylvania. "A goal shared by many neuroimaging researchers is to create similar benchmarks for brain development and conceptualize what is typical in the context of this rapidly changing organ over the lifespan. Widely used quantitative standards for human brain measurement do not yet exist."
Many psychiatric disorders may result from abnormal brain development, and neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, are also associated with changes to the brain. In addition, preterm birth and neurogenetic disorders are linked to abnormal brain structures that are associated with learning disabilities and mental health disorders. Because mental health disorders are a significant global health burden, the need for standardized methods to quantify brain structure over the lifespan is crucial.
To develop the BrainChart platform, Dr. Alexander-Bloch, and postdoctoral researcher Jakob Seidlitz, PhD, co-first author of the study, along with colleagues from the University of Cambridge, aggregated neuroimaging data including 123,984 MRI scans from 101,457 participants that ranged in age from 115 days after conception to 100 years old. They used data from more than 100 primary research studies.
The researchers analyzed cerebrum tissue volumes and other MRI metrics, and then they applied centile scores and rates of change over the lifespan. When they did this, they found previously unreported neurodevelopmental milestones: an early growth period that starts around 17 weeks after conception until age 3 years. The researchers also noted that the centile scores provided a standardized measure of atypical brain structures that may lead to neurological and psychiatric disorders.
An important aspect of the study is that the researchers were able to aggregate and harmonize MRI data from a variety of sources, time periods, and clinical conditions, allowing them to create a common language that could understand brain images from different sources. This standardization lays the groundwork for adding images from routine scans in the future.
At the moment, BrainChart is mainly for use by researchers as a source of benchmarks that they can use to analyze their findings. However, the researchers anticipate that after more research to validate and refine the model, BrainChart could be useful in clinical settings. The researchers also noted that the model must be representative of the whole population and needs more MRI data from under-represented socioeconomic and ethnic groups.
"Just as there are still significant caveats when it comes to the diagnostic interpretation of where a child falls on a height, weight, and BMI chart, we would likewise expect that there will be nuances to how BrainChart could be used in a clinical setting," Dr. Seidlitz said. "That said, by creating this common language for brain images, we have built a necessary bridge that can help facilitate the translation of research insights into clinical workflows, which bodes well for current longitudinal clinical imaging practices in conditions such as hydrocephalus or Alzheimer's disease, as well as future progress toward large-scale digital mapping of atypical brain changes across many diverse clinical conditions."