Summary: Researchers have developed new brain growth charts that provide reference models for brain development and aging across the entire lifespan.
Source: Radboud University
Researchers from Radboudumc have developed a set of growth charts for the brain. These ‘brain charts’ provide reference models for brain development and aging across the entire human lifespan, based on a very large data set.
These models can be used to make personalized predictions for each individual relevant to many brain conditions, and therefore have a high clinical potential. The software tools and models are available online.
The work has been published in Life.
“Nearly everybody is familiar with the growth charts used to measure child development, for example the growth charts developed by the World Health Organization,” says Andre Marquand, researcher at the department of Cognitive Neuroscience of Radboudumc.
“These models are being used worldwide to assess the development of children, for instance by plotting body weight or height as a function of age. Pediatricians plot the development of an individual child against variation in the population provided by these growth charts, in order to detect, for example, developmental delay.”
The researchers now provide the same thing for the brain: a growth chart to assess brain development and aging, not only for children, but across the lifespan from ages two to 100.
“We have analyzed high resolution MRI images from nearly 60.000 people from around 80 MRI scanners all over the world,” explains Saige Rutherford, Ph.D. candidate and first author.
“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.”
Alterations in brain structure
These models enable predictions at the level of an individual person about brain growth and aging, with respect to population norms. Marquand says that “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.”
These models have many uses: they can be helpful to detect alterations in brain structure that might indicate the emergence of a mental disorder at a very early stage. 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 this life stage. But it is also useful for the 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. In addition, the model enables tracking of disease progression over time, and also monitoring the effect of a treatment.
A reference model for the brain like this has not been available before. The models and also the software to use them are made freely 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.”
In the near 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.”
Charting brain growth and aging at high spatial precision
Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and use normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume.
Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1,985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences.
These models will be augmented with additional samples and imaging modalities as they become available.
This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision making.
About this brain development research news
Original Research: Open access.
“Charting brain growth and aging at high spatial precision” by Andre Marquand et al. eLife