Your brain expands and shrinks over time: these graphs show how news and research

Your brain expands and shrinks over time: these graphs show how news and research

When neuroscientist Jakob Seidlitz took his 15-month-old son to the pediatrician for a check-up last week, he left feeling unsatisfied. There was nothing wrong with his son — the boy seemed to be developing at a typical pace, according to the height and weight charts the doctor used. What Seidlitz felt was missing was an equivalent yardstick for measuring how his son’s brain was growing. “It’s shocking how little biological information doctors have about this critical organ,” said Seidlitz, who is based at the University of Pennsylvania in Philadelphia.

He may soon be able to change that. In collaboration with colleagues, Seidlitz collected more than 120,000 brain scans — the largest collection of its kind — to create the first comprehensive growth charts for brain development. The graphs visually show how human brains grow rapidly early in life and then slowly shrink with age. The sheer size of the study, published in Nature on April 6, has baffled neuroscientists, who have long faced reproducibility issues in their research, in part because of the small sample size. Magnetic resonance imaging (MRI) is expensive, meaning scientists are often limited in the number of participants they can enroll in experiments.

“The huge dataset they have collected is extremely impressive and really sets a new standard for the field,” said Angela Laird, a cognitive neuroscientist at Florida International University in Miami.

Still, the authors caution that their database isn’t fully inclusive — they struggled to collect brain scans from all regions of the world. The resulting charts, they say, are therefore only preliminary drafts, and further adjustments would be needed to deploy them in clinical settings.

If the cards are eventually rolled out to pediatricians, great care will be needed to ensure they aren’t misinterpreted, says Hannah Tully, a pediatric neurologist at the University of Washington in Seattle. “A large brain is not necessarily a well-functioning brain,” she says.

Not an easy task

Because brain structure varies significantly from person to person, the researchers had to piece together a large number of scans to create an authoritative series of growth charts of statistical significance. That’s not an easy task, says Richard Bethlehem, a neuroscientist at the University of Cambridge, UK, and a co-author of the study. Rather than performing thousands of scans themselves, which would take decades and be prohibitively expensive, the researchers turned to already completed neuroimaging studies.

Bethlehem and Seidlitz sent emails to researchers around the world asking them to share their neuroimaging data for the project. The duo were amazed at the number of responses, which they attribute to the COVID-19 pandemic, which means researchers have less time in their labs and more time than usual with their email inboxes.

In total, the team collected 123,894 MRI scans of 101,457 people, ranging from fetuses 16 weeks after conception to 100-year-old adults. The scans included brains from neurotypical people, as well as people with a variety of medical conditions, such as Alzheimer’s disease, and neurocognitive differences, including autism spectrum disorder. The researchers used statistical models to extract information from the images and ensure that the scans were directly comparable regardless of what type of MRI machine was used.

Brian change: graph showing the proportional volume of ventricular, white and gray matter and cortical thickness throughout life.
Credit: Nature

The end result is a series of graphs plotting several key brain stats by age. Some metrics, such as gray matter volume and average cortical thickness (grey matter width) peak early in a person’s development, while white matter volume (found deeper in the brain) tends to has to peak around age 30 (see ‘Brain change’). Especially the data on the ventricular volume (the amount of cerebrospinal fluid in the brain) surprised Bethlehem. Scientists knew this volume increases with age because it’s typically associated with brain atrophy, but Bethlehem was shocked at how quickly it tends to grow in late adulthood.

A first design

The study comes on the heels of a bombshell paper published in Nature on March 16, showing that most brain imaging experiments contain too few scans to reliably detect links between brain function and behavior, meaning their conclusions may be incorrect. Given this finding, Laird expects the field to evolve towards adopting a framework similar to that of Seidlitz and Bethlehem, to increase its statistical power.

Collecting that many data sets is akin to a “diplomatic masterpiece,” said Nico Dosenbach, a neuroscientist at Washington University in St. Louis, Missouri, who co-authored the March 16 study. According to him, this is the scale at which researchers should operate when aggregating brain images.

Despite the size of the dataset, Seidlitz, Bethlehem and their colleagues acknowledge that their study suffers from a problem endemic to neuroimaging studies – a remarkable lack of diversity. The brain scans they collected are mainly from North America and Europe, disproportionately reflecting white, college, urban and affluent populations. This limits the generalizability of the findings, said Sarah-Jayne Blakemore, a cognitive neuroscientist at the University of Cambridge. The study only includes three data sets from South America and one from Africa — accounting for about 1% of all brain scans used in the study.

Billions of people around the world don’t have access to MRI machines, making it difficult to get diverse brain imaging data, Laird says. But the authors haven’t stopped trying. They have launched a website where they plan to update their growth charts in real time as they receive more brain scans.

With big datasets, big responsibility

Another challenge was determining how to give proper credit to the owners of the brain scans used to create the graphs. Some scans came from open-access datasets, but others were not accessible to researchers. Most closed-data scans hadn’t yet been processed to be included in the growth charts, so the owners put in extra work to share them. These scientists were subsequently named as authors of the article.

Meanwhile, the owners of the open datasets only received a mention in the newspaper – which doesn’t have as much prestige for researchers seeking funding, collaborations and promotions. Seidlitz, Bethlehem and their colleagues processed this data. In most cases, Bethlehem says there was essentially no direct contact with the owners of these datasets. The article lists about 200 authors and cites the work of hundreds of others who contributed brain scans.

There are a number of reasons why datasets can be closed: for example to protect the privacy of health data, or because researchers do not have the resources to make them public. But this doesn’t make it fair that the researchers who opened their datasets weren’t given authorship, the authors say. In their paper’s Supplemental Information, they state that the situation “perversely discourages open science, as the people who do the most to make their data available openly are the least likely to deserve recognition.” Bethlehem and Seidlitz claim that journal authorship guidelines include: Nature — saying that every author is expected to have made “substantial contributions” to, say, the analysis or interpretation of data — are an obstacle. †NatureThe news team is editorially independent of the publisher.)

A Nature The spokesperson responded that the issue “has been carefully considered by the editors and authors in accordance with our authorship policy” and that “all datasets have been appropriately credited under our data citation policy.”

Ultimately, these concerns stem from the way researchers are judged by the scientific enterprise, said Kaja LeWinn, a social epidemiologist at the University of California, San Francisco, who studies neurodevelopment. She says it is the duty of all relevant stakeholders — including funders, journals and research institutions — to re-evaluate how to properly recognize and reward brain science, especially as these types of large-scale studies become more common.

This article is reproduced with permission and was first published on April 6, 2022.

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