A comprehensive study has found clear biomarkers in the MRI scans of children diagnosed with attention deficit/hyperactivity disorder (ADHD).
The findings of the study were presented recently at the annual meeting of the Radiological Society of North America (RSNA). The study also found the potential use of neuroimaging machine learning in the diagnosis, treatment planning, and surveillance of the disorder.
ADHD, one of the most prevalent neurodevelopmental disorders in childhood, affects about 6 million American children between the ages of 3 and 17 years, according to SciTechDaily. Symptoms of the disorder include trouble paying attention and controlling impulsive behaviors or being overly active. The disorder is diagnosed by a child’s caregiver, which includes a checklist to rate the presence of ADHD symptoms.
“There’s a need for a more objective methodology for a more efficient and reliable diagnosis,” study co-author Huang Lin, a post-graduate researcher at the Yale School of Medicine in New Haven, Connecticut, said, the outlet reported. “ADHD symptoms are often undiagnosed or misdiagnosed because the evaluation is subjective.”
For the analysis, researchers used MRI data from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States. From the ABCD study, data from 7,805 children aged 9-10 years, including 1,798 diagnosed with ADHD children was analyzed.
“The demographics of our group mirror the U.S. population, making our results clinically applicable to the general population,” Lin said, as per the outlet.
Following analysis, the association of ADHD with neuroimaging metrics, including brain volume, surface area, white matter integrity, and functional connectivity, was studied.
“We found changes in almost all the regions of the brain we investigated,” Lin noted. “The pervasiveness throughout the whole brain was surprising since many prior studies have identified changes in selective regions of the brain.”
Abnormal connectivity in the brain networks involved in memory and auditory processing, a thinning of the brain cortex, and pronounced white matter microstructural changes were found in ADHD patients.
“Our study underscores that ADHD is a neurological disorder with neuro-structural and functional manifestations in the brain, not just a purely externalized behavior syndrome,” Lin said.
“At times when a clinical diagnosis is in doubt, objective brain MRI scans can help to clearly identify affected children. Objective MRI biomarkers can be used for decision-making in ADHD diagnosis, treatment planning, and treatment monitoring,” Lin further added.
The researcher is also confident their MRI data was significant enough to be used as input for machine learning models to predict an ADHD diagnosis. Machine learning will be able to analyze large amounts of MRI data.
A separate study found that caffeine could be a possible treatment for some of the symptoms of ADHD. “The therapeutic arsenal for alleviating ADHD is limited, and there is a certain degree of controversy around the use of some types of medications and stimulants, especially during childhood and adolescence,” Javier Vázquez of Universitat Oberta de Catalunya (UOC), one of the new study’s main authors, said in the UOC news release. “That’s why it’s useful to study the efficacy of other substances, such as caffeine.”