The researchers identified brain markers for ADHD in children

The researchers analyzed MRI data from nearly 8,000 children and found biomarkers for attention-deficit/hyperactivity disorder (ADHD) and the possible role
of neuroimaging machine learning in helping diagnose, plan and monitor 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, attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in childhood, affecting about 6 million U.
S
.
children ages 3 to 17.

Children with this disorder may have difficulty concentrating and controlling impulsive behavior, or they may be overactive
.
Diagnosis relies on a checklist completed by the child's caregiver to assess the presence of
ADHD symptoms.

"We need a more objective, effective, and reliable diagnostic method," said
Lin Huang, a co-author of the study and a graduate researcher at Yale University School of Medicine in New Haven, Connecticut.
"Symptoms of ADHD are often not diagnosed or misdiagnosed because evaluation
is subjective.
"

The researchers used MRI data from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study
of brain development and children's health in the United States.
The ABCD study involved 11,878 children ages 9 to 10 from 21 centers across the country to represent the sociodemographic diversity of the United States

"The demographics of our group reflect the U.
S.
population, making our results clinically applicable to the general population.
"

After exclusion, Lin's research group included 7805 patients, of whom 1798 were diagnosed with ADHD, all of whom underwent structural MRI scans, diffusion tensor imaging, and resting functional MRIs
.
The researchers performed statistical analysis of imaging data to determine the correlation
of ADHD with neuroimaging indicators such as brain volume, surface area, white matter integrity, and functional connectivity.

Lin said: "We found that almost all areas of the brain that we investigated changed
.
" "This phenomenon that spreads throughout the brain is surprising because many previous studies have identified changes in
specific areas of the brain.
"

In patients with ADHD, researchers observed abnormal brain network connections involved in memory processing and auditory processing, thinning of the cerebral cortex, and significant changes in the microstructure of white matter, particularly in the frontal lobes
of the brain.

"The frontal lobe is the area of the brain that controls impulses and attention deficits, which are the two main symptoms of
ADHD," Lin said.

MRI data is important as input to machine learning models to predict a diagnosis
of ADHD.
Machine learning is a type of artificial intelligence that makes it possible
to analyze large amounts of MRI data.

"Our study highlights that ADHD is a neurological disorder that manifests itself in neural structures and functions in the brain, not just a purely externalizing behavioral syndrome," she said
.

The population-level data from this study provided assurance, and MRI biomarkers provided reliable images
of the brain.

"When the clinical diagnosis is in doubt, objective MRI scans of the brain can help clearly identify affected children
.
" Objective: MRI biomarkers can be used for ADHD diagnosis, treatment planning, and treatment monitoring decisions
.

Senior author Sam Payabvash, Ph.
D.
, noted that recent trials reported microstructural changes
in response to treatment in children with ADHD.

"Our study provides these children with new, multimodality neuroimaging biomarkers as potential therapeutic targets
," he said.