Researchers have identified biomarkers of attention-deficit/hyperactivity disorder (ADHD) in children and a possible role for neuroimaging machine learning

Researchers analysing the data from MRI exams on nearly 8,000 children identified biomarkers of attention-deficit/hyperactivity disorder (ADHD) in children and a possible role for neuroimaging machine learning to help with the diagnosis, treatment planning and surveillance of the disorder. The results of the new study will be presented next week at the annual meeting of the Radiological Society of North America (RSNA).

According to the Centers for Disease Control and Prevention, ADHD affects approximately 6 million American children between the ages of 3 and 17. It is one of the most common neurodevelopmental disorders in childhood.

ADHD in children manifests itself in many ways

Children with the neurological disorder may have trouble paying attention and controlling impulsive behaviours, or they may be overly active.

Usually, diagnosis relies on a checklist completed by the child’s caregiver to rate the presence of ADHD symptoms.

ADHD symptoms are often undiagnosed or misdiagnosed

“There’s a need for a more objective methodology for a more efficient and reliable diagnosis,” commented study co-author Huang Lin, a post-graduate researcher at the Yale School of Medicine in New Haven, Connecticut.

“ADHD symptoms are often undiagnosed or misdiagnosed because the evaluation is subjective.”

Changes recorded across the brain

The researchers used MRI data from the Adolescent Brain Cognitive Development (ABCD) study, which is the largest long-term study of brain development and child health in the United States. The ABCD study involved 11,878 children aged 9-10 years from 21 centres across the country to represent sociodemographic diversity in the U.S.

“The demographics of our group mirror the U.S. population, making our results clinically applicable to the general population,” Lin explained.

After exclusions, Lin’s study group included 7,805 patients, including 1,798 diagnosed with ADHD, all of whom underwent structural MRI scans, diffusion tensor imaging and resting-state functional MRI.

After performing a statistical analysis of the imaging data to determine the association of ADHD with neuroimaging metrics, including brain volume, surface area, white matter integrity and functional connectivity, researchers found changes across the brain.

“We found changes in almost all the regions of the brain we investigated,” Lin said. “The pervasiveness throughout the whole brain was surprising since many prior studies have identified changes in selective regions of the brain.”

In the patients with ADHD, the researchers observed abnormal connectivity in the brain networks involved in memory processing and auditory processing, a thinning of the brain cortex, and significant white matter microstructural changes, especially in the brain’s frontal lobe.

“The frontal lobe is the area of the brain involved in governing impulsivity and attention or lack thereof—two of the leading symptoms of ADHD,” Lin said.

Lin said MRI data was significant enough to be used as input for machine learning models to predict an ADHD diagnosis.

ADHD is a neurological disorder

“Our study underscores that ADHD is a neurological disorder with neuro-structural and functional manifestations in the brain, not just a purely externalized behaviour syndrome,” she said.

Lin said the population-level data from the study offers reassurance that the MRI biomarkers give a solid picture of the brain and allow us to understand more about the complex disorder.

“At times when a clinical diagnosis is in doubt, objective brain MRI scans can help to clearly identify affected children,” Lin said. “Objective MRI biomarkers can be used for decision making in ADHD diagnosis, treatment planning and treatment monitoring.”


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