Paying attention is hard.

Paying attention is hard.


Having trouble concentrating? This brain network may be to blame

In the modern world, replete with its smartphones, email, push notifications, and myriad other distractions, it can be tough to pay attention to the task at hand. Maintaining focus—especially on something boring—often seems to require an inordinate amount of brainpower. But where this ability comes from in the brain has remained enigmatic. Now, scientists have mapped out a network of brain regions whose connectedness may predict our ability to focus and even the likelihood that we have attention deficit hyperactivity disorder (ADHD).

“I thought it was really fascinating work,” says University of Chicago in Illinois cognitive neuroscientist Ed Awh, who was not involved in the study. “It shows that this is something that is true in a general way in human brains.”

Recent advances in a brain imaging technique known as functional magnetic resonance imaging, or fMRI, which measures blood flow in the brain in real time, have spurred a new type of neuroscience in which researchers monitor the entire brain at once to hunt for networks that activate during specific tasks. Although some brain regions contribute heavily to certain processes—like the occipital lobe in vision—much of this research shows that nearly everything we do requires many different interconnected parts, or nodes, in the brain.

The new study, published today in Nature Neuroscience, indicates that just such a network is used in maintaining concentration. After putting 25 adults in an fMRI machine, scientists asked them to complete a “gradual-onset continuous performance task” in which they were shown black and white pictures of either mountains or a city. Participants were instructed to press a button only when they saw a city scene, which accounted for about 90% of the images.

“It’s a really super boring task, kind of by design,” says Yale University cognitive neuroscientist Monica Rosenberg, the study’s lead author. As easy as it might seem to differentiate mountains from cities, maintaining good performance on the test required that subjects pay close attention and not allow their minds to wander.

After the trials were completed, a computer searched through the data set for patterns of connectivity in the brain—looking for connections that predicted a higher score on the attention test. This type of approach is incredibly powerful, but also prone to finding relationships where there are none. “When you do data driven analysis, by definition, you’re always going to find something,” says New York University in New York City child psychiatrist F. Xavier Castellanos, who was not involved in the study. “Even in random data, it’s going to find 5% of the relationships are significant.”

To ensure that they were indeed seeing evidence of a “sustained attention network” used for concentration, one participant in each trial run was not given the continuous performance task. Instead, the left-out individual’s brain was mapped at rest in the fMRI, while the team looked at the strength of network connections. The computer then predicted that person’s ability to maintain focus based on how their network compared with the other 24 brain scans and the paired test performances. “We trained models on 24 subjects to predict the remaining 25th subject's performance. We iterated through this procedure so that every subject was left out once,” Rosenberg explains.  

As the researchers hoped, they discovered that the strength of the connections in the network were predictive of how well a participant would score on the attention test. “We could predict how well you would hypothetically do if you were to perform the test, even if you were just resting,” Rosenberg says. If the fMRI showed high connectivity in the sustained attention network of a person at rest, chances are that person would do well on the attention test.  

The researchers also had access to a shared database of fMRI scans from previous studies around the world. They found a sample of 113 children in Beijing who had been evaluated for symptoms of ADHD after their brains were scanned. Using these results, the team found that the amount of connectivity in the sustained attention network could even predict a child’s score on a standardized ADHD rating scale: Children with good connectivity were less likely to have ADHD and vice versa. This finding was especially exciting to the researchers because the data set came all the way from Peking University in Beijing, meaning their model appears generalizable across highly disparate populations and age ranges as well.

The approach could offer insights into the underpinnings of ADHD, but the team’s success also helps validate the idea that whole-brain networks may underlie many neurological functions, like memory or learning.

“The horse race is on to see if these things hold up,” Castellanos says. “If they do, it means we’re really truly beginning to decipher brain functioning. This is the beginning of decoding the Egyptian hieroglyphics, and that’s immensely exciting.”