A Reference Model for Human Brain Development

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A Reference Model for Human Brain Development

 Jim Windell

            We are all familiar with growth charts which are helpful to measure the growth and development of children. Is our child tall for his age or is she using motor skills appropriate for her chronological age? A growth chart can tell us.   

            But what if there were a developmental chart for the brain? And what if such a chart could help us discover whether a child or an adult had brain functions and brain structure that was appropriate?

            As it turns out, researchers from Radboud University Medical Center in the Netherlands have developed a set of growth charts for the brain. These “brain charts” provide reference models for brain development and ageing across the entire human lifespan. The charts are based on a very large data set and these models can be used to make personalized predictions for each individual and their particular brain conditions

            The work about the brain charts has recently been published in eLife. The charts allow assessment of brain development and aging – not only for children, but across the lifespan from ages two to 100.

           According to first author Saige Rutherford, Ph.D. candidate at Radboud Medical Center, “We have analyzed high resolution MRI images from nearly 60,000 people from around 80 MRI scanners all over the world. We used measures of the volume of different brain structures or the thickness of the cerebral cortex at different ages and created growth charts for every brain region. In this way we created a fine-grained atlas of the human brain throughout life.”

           Rutherford and his colleagues point out that the models they developed have many uses. For instance, they can be helpful to detect alterations in brain structure that might indicate the emergence of a mental disorder at a very early stage. Also, the models can assess if a region in the brain is thicker or thinner than it ought to be for an individual as compared to average for their particular life stage. In addition, the charts could be useful for stratification of mental disorders. For example, finding commonalities between individuals that might describe different subtypes of disorders, or in the future to identify individuals that could respond to certain treatments. Finally, the model enables tracking of disease progression over time, and also monitoring the effect of a treatment.

           Andre Marquand, researcher at the department of Cognitive Neuroscience of Radboud Medical Center, indicates that their models enable predictions at the level of an individual person about brain growth and ageing, with respect to population norms. “This provides a reference to map variation across individuals and can be used to help understand many different brain-based conditions, like ADHD, schizophrenia, dementia and Alzheimer’s disease,” Marquand says.

           Although a reference model for the brain like this has not been available before, these model as well as the software to use them are made available online to the community. “We use an established software pipeline called ‘Freesurfer’ to measure the volume and thickness of brain structures,” explains Marquand. “This pipeline is used by thousands of hospitals worldwide, so they can easily get the measures they need and use our software to determine how a group of their own patients or study participants can be placed within the population.”

           Sometime in the future, Marquand thinks the software could be of great use in clinical studies. “If you want to investigate a new medication against a certain brain-based condition, for example Alzheimer’s disease, you could use our software to identify subjects, with a particular profile, such as early-stage degeneration. This could function like a ‘brain-based fingerprint’ which could make research more efficient by making it easier to detect differences between groups of people. Eventually such tools might also be helpful in the clinic to target medications or interventions precisely to the people that need them.”

           The software tools and models are available online.

           To read the original article, find it with this reference:

Rutherford, S., Fraza, C., Dinga, R., Kia, S.F. et al. (2022). Charting brain growth and aging at high spatial precision. eLife 2022;11:e72904 DOI: 10.7554/eLife.72904

           For more information, click here or go to:

Rutherford, Saige, & Marquand, Andre. (2021). Braincharts (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5535467


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