A comprehensive study has found clear biomarkers within the MRI scans of youngsters diagnosed with attention deficit/hyperactivity disorder (ADHD).

The findings of the study were presented recently on the annual meeting of the Radiological Society of North America (RSNA). The study also found the potential use of neuroimaging machine learning within the diagnosis, treatment planning, and surveillance of the disorder.

ADHD, probably the most prevalent neurodevelopmental disorders in childhood, affects about 6 million American children between the ages of three and 17 years, in keeping with SciTechDaily. Symptoms of the disorder include trouble being attentive and controlling impulsive behaviors or being overly lively. The disorder is diagnosed by a toddler’s caregiver, which incorporates a checklist to rate the presence of ADHD symptoms.

“There’s a necessity for a more objective methodology for a more efficient and reliable diagnosis,” study co-author Huang Lin, a post-graduate researcher on the Yale School of Medicine in Recent Haven, Connecticut, said, the outlet reported. “ADHD symptoms are sometimes undiagnosed or misdiagnosed since the evaluation is subjective.”

For the evaluation, researchers used MRI data from the Adolescent Brain Cognitive Development (ABCD) study, the most important long-term study of brain development and child health in the US. 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 final population,” Lin said, as per the outlet.

Following evaluation, the association of ADHD with neuroimaging metrics, including brain volume, surface area, white matter integrity, and functional connectivity, was studied.

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

Abnormal connectivity within the brain networks involved in memory and auditory processing, a thinning of the brain cortex, and pronounced white matter microstructural changes were present in ADHD patients.

“Our study underscores that ADHD is a neurological disorder with neuro-structural and functional manifestations within the brain, not only a purely externalized behavior syndrome,” Lin said.

“At times when a clinical diagnosis is unsure, objective brain MRI scans will help to obviously discover affected children. Objective MRI biomarkers may be used for decision-making in ADHD diagnosis, treatment planning, and treatment monitoring,” Lin further added.

The researcher can also be confident their MRI data was significant enough for use as input for machine learning models to predict an ADHD diagnosis. Machine learning will have the opportunity to research large amounts of MRI data.

A separate study found that caffeine could possibly be a possible treatment for a number of the symptoms of ADHD. “The therapeutic arsenal for alleviating ADHD is restricted, and there may be a certain degree of controversy around the usage of some varieties of medications and stimulants, especially during childhood and adolescence,” Javier Vázquez of Universitat Oberta de Catalunya (UOC), one among the brand new study’s fundamental authors, said within the UOC news release. “That is why it’s useful to review the efficacy of other substances, akin to caffeine.”