UCL: Brain charts map rapid growth and slow decline across lifespan

The charts are the result of a research project spanning six continents and bringing together possibly the largest ever MRI datasets ever aggregated – almost 125,000 brain scans from over 100 different studies – reported in a new Nature paper. Although not currently intended for clinical use, the team hopes the charts will become a routine clinical tool similar to how standardised paediatric growth charts are used.

Growth charts have been a cornerstone of paediatric healthcare for over 200 years and are used ubiquitously in clinics to help monitor the growth and development of children in comparison to their peers. A typical growth chart might plot age on the horizontal axis versus height on the vertical axis, but rather than being a single line, it will show a range that reflects the natural variability in height, weight or head circumference.

There are no analogous reference charts for measuring age-related changes in the human brain. The lack of tools for standardised assessment of brain development and ageing is particularly relevant to the study of psychiatric disorders, where the differences between conditions and the heterogeneity within them demands instruments that can say something meaningful about a single individual in the way clinical reference charts can, and to conditions such as Alzheimer’s disease that cause degeneration of brain tissue and cognitive decline.

The new study is a major step towards filling this gap. Unlike paediatric growth charts, BrainChart – published on the open access site brainchart.io – covers the whole lifespan, from development in the womb through to old age, and aims to create a common language to describe the variability in brain development and maturation.

The incredible growing and shrinking brain

The brain charts have allowed the researchers to confirm – and in some cases, show for the first time – developmental milestones that have previously only been hypothesised, such as at what age the brain’s major tissue classes reach peak volume and when do specific regions of the brain reach maturity.

Dr Richard Bethlehem (University of Cambridge), one of the co-leads of the study, said: “One of the things we’ve been able to do, through a very concerted global effort, is to stitch together data across the whole life span. It’s allowed us to measure the very early, rapid changes that are happening in the brain, and the long, slow decline as we age.”

Among the key milestones observed by the team were:

The volume of grey matter (brain cells) increases rapidly from mid-gestation onwards, peaking just before we are six years old. It then begins to decrease slowly.
The volume of white matter (brain connections) also increased rapidly from mid-gestation through early childhood and peaks just before we are 29 years old.
The decline in white matter volume begins to accelerate after 50 years old.
Grey matter volume in the subcortex (which controls bodily functions and basic behaviour) peaks in adolescence at 14-and-a-half years old.
Co-author Professor Ray Dolan (Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology) commented: “The work represents an extraordinary collective international effort that has realised a unique reference data set that will allow a fine-grained analysis of how disease impacts on brain development, as well as help predict the likelihood of a range of neurodegenerative disorders.”

Towards a clinically-useful tool

While the brain charts are already proving useful for research, in the long term, the team intend them to be used as a clinical tool. The datasets already have around 165 different diagnostic labels, meaning that researchers can see how the brain differs in conditions such as Alzheimer’s disease.

Dr Bethlehem explained: “The charts are already beginning to provide interesting insights into brain development, and our ambition is that in future, as we integrate more datasets and refine the charts, they could eventually become part of routine clinical practice.

“You could imagine them being used to help evaluate patients screened for conditions such as Alzheimer’s, for example, allowing doctors to spot signs of neurodegeneration by comparing how rapidly a patient’s brain volume has changed compared to their peers.”

In addition, the team hope to make the brain charts more representative of the whole population, pointing to the need for more brain MRI data on previously under-represented socio-economic and ethnic groups.

A huge technical feat

Dr Jakob Seidlitz (Lifespan Brain Institute at Children’s Hospital of Philadelphia and University of Pennsylvania), another of the co-leads of the study, said: “With brain imaging data, things are a bit more complicated than just taking out a measuring tape and measuring someone’s height, or head circumference. There were significant challenges to deal with, including logistic and administrative hurdles as well as the huge methodological variability we find between brain imaging datasets.”

The team used standardised neuroimaging software to extract data from MRI scans, beginning with simple properties such as the volume of grey matter or white matter, and then expanding their work to look at finer details, such as the thickness of the cortex or the volume of specific regions of the brain. They used a framework implemented by the World Health Organization for generating growth charts to build their brain charts.

Altogether, they estimate that they have used around 2 million hours of computing time, analysing close to a petabyte of data (a petabyte is equivalent to 1,000,000,000,000,000 bytes).

Dr Seidlitz said: “We still see this as a work in progress. It’s a first pass at establishing a standardised reference chart for neuroimaging. That’s why we’ve built the website and created a large network of collaborators. We expect to consistently update the charts and build on these models as new data becomes available.”

The team have created the tool with a reference framework to allow other researchers and clinicians to adjust their own datasets, making it possible to compare them against the BrainChart population.

Dr Bethlehem explained: “The NHS does millions of brain scans every year and in most of these cases, they are assessed by radiologists or neurologists relying on their extensive expertise to judge whether there is anything clinically relevant apparent on these scans. We hope that clinicians in future will be able to compare their data against ours and produce a more comprehensive report adding additional objective and quantitative observations to their assessment.

“This should effectively allow the neurologist to answer the question ‘this area looks atypical but atypical by how much?’. As the tool is standardised, it shouldn’t matter where you have your brain scan – you should still be able to compare it.”

The work builds on a worldwide collective endeavour over the last few decades to measure human brain structure with MRI, in many different groups of people at many different ages. The team say it would not have been possible without open access to many high quality MRI datasets, and hope their results will contribute to further openness and sharing of data and analytics for brain imaging science.

UCL