- Researchers analyzed brain scans from thousands of kids to learn more about ADHD.
- They found consistent trends in brain volume and function across a diverse population with ADHD.
- ADHD is one of the most common neurodevelopmental disorders in children, affecting about 6 million in the US.
Researchers have identified markers associated with attention-deficit/hyperactivity disorder (ADHD) in the brain, which could someday be used to help diagnose and monitor the disorder.
A team at Yale University School of Medicine analyzed brain scans from nearly 8,000 children — some of whom had been diagnosed with ADHD, and others who were not. Their findings will be presented at the annual meeting of the Radiological Society of North America next week.
“We found changes in almost all the regions of the brain we investigated,” Huang Lin, a co-author of the paper, said in a press release. “The pervasiveness throughout the whole brain was surprising since many prior studies have identified changes in selective regions of the brain.”
But ADHD is typically diagnosed based on behavioral patterns that are reported by parents or caregivers, which introduces an element of subjectivity, Lin explained. Since young children may not be able to describe their own symptoms, their answers to diagnostic tests are filtered through the adults in the room.
Brain markers could someday be used as a more objective tool for diagnosing ADHD, the authors concluded.
If the team were to input their findings into a machine learning model, it’s possible that the data could help doctors predict or support an ADHD diagnosis —although the new technology shouldn’t replace patient-physician interactions, Lin said.
Brains with ADHD had subtle differences from those without
The study group ultimately included 7,805 patients who were 9 or 10 years old when they underwent brain scans for the Adolescent Brain Cognitive Development study, the largest long-term study of brain development and child health in the US. Of that group, nearly 1,800 children had ADHD diagnoses at the time of the study.
The kids with ADHD showed unusual connectivity between parts of the brain involved in memory and auditory processing, Lin said, as well as an odd relationship between the brain’s default mode network and the dorsal attention network.
The brain’s default network is normally engaged for internal processing, like daydreaming or reflecting, while the dorsal attention network is used for focusing on a specific task or input, Lin explained.
In a neurotypical person, the default network takes a backseat when attention is needed, and vice versa. But increased connectivity between the regions — as seen in brains of people with ADHD — could explain patterns of inattention associated with the disorder, the authors said.
Additionally, the researchers noticed a trend of reduced brain volume in kids with ADHD. All of the brain’s structures were significantly thinner in cases of ADHD, although the difference was relatively small and wouldn’t necessarily stand out on an individual brain scan.
“It’s not like Alzheimer’s or very severe neurological diseases where you can really see how thin the gray matter gets,” Lin said. “We’re really talking about subtle changes that might not be visible.”
The markers could predict an ADHD diagnosis
Lin said the MRI data used in the study was significant enough that it could be used to help diagnose ADHD — with the support of a physician and a computer algorithm.
Each participant in the study got MRI scans to capture their brain structures and resting-state brain function, as well as diffusion tensor imaging, a technique used to view the brain’s white matter. The participants came from 21 child health centers across the country, in an effort to reflect the sociodemographic diversity of the US population.
The team hopes that the data will be fed into a machine learning model, a type of artificial intelligence that could be used to predict ADHD based on a child’s brain scan.
“At times when a clinical diagnosis is in doubt, objective brain MRI scans can help to clearly identify affected children,” Lin said.