Select your localized edition:

Close ×

More Ways to Connect

Discover one of our 28 local entrepreneurial communities »

Be the first to know as we launch in new countries and markets around the globe.

Interested in bringing MIT Technology Review to your local market?

MIT Technology ReviewMIT Technology Review - logo


Unsupported browser: Your browser does not meet modern web standards. See how it scores »

{ action.text }

Researchers can then look at the features that the algorithm deemed most important and learn what is changing in the brain’s functional architecture. Over the course of development, different brain regions became segregated from each other as the connections between them weakened. “It paints a picture of a brain that gradually organizes into distinct subnetworks,” says Olaf Sporns, a neuroscientist at Indiana University who was not involved in the study. “The networks appear to become more differentiated with respect to each other and more integrated internally.” It’s not yet clear exactly how this change affects the way children and teenagers think, but Sporns speculates that as the brain becomes more differentiated, with distinct neural networks, people get better at switching between tasks. Previous research has identified specific neural networks involved in memory and attention, or sensory and motor function.

The research is part of a growing trend to analyze how the brain’s networks interact, rather than focusing on specific brain areas. Schlaggar says this approach, as well as the ability to look at how multiple factors change simultaneously, allowed his team to analyze individual brain scans. The researchers could predict an individual’s age from just five minutes of brain-imaging data. Other scientists have applied a similar approach to other types of brain-imaging data, using it to determine what word someone is thinking of, for example. “I think this is going to become a new way of looking at the brain, and of finding new rules for how the brain develops,” says Martijn van den Heuvel, a neuroscientist at the Utrecht University Medical Center, who was not involved in the study.

Researchers are now using the same approach to study such developmental disorders as Tourette’s syndrome and autism. These and other developmental and psychiatric disorders have proven difficult to detect with traditional brain-imaging tools. “One of the questions I want to ask is, how can I predict the clinical course of an eight-year-old with Tourette’s? Will it be a transient tic disorder? Or full blown Tourette’s with [other psychiatric disorders]?” says Schlaggar. “We have little ability now to make predictions, and a tremendous need to make strong predictions about what’s going to happen to a patient over the next 10 years.”

0 comments about this story. Start the discussion »

Credit: Science/AAAS

Tagged: Biomedicine, machine learning, brain imaging, fMRI, neural networks, functional connectivity

Reprints and Permissions | Send feedback to the editor

From the Archives


Introducing MIT Technology Review Insider.

Already a Magazine subscriber?

You're automatically an Insider. It's easy to activate or upgrade your account.

Activate Your Account

Become an Insider

It's the new way to subscribe. Get even more of the tech news, research, and discoveries you crave.

Sign Up

Learn More

Find out why MIT Technology Review Insider is for you and explore your options.

Show Me