Several imaging biomarkers can be found in brain MRI scans of children with attention-deficit/hyperactivity disorder (ADHD), according to new research to be presented at RSNA 2022 in Chicago, the annual meeting of the Radiological Society of North America (RSNA).
Experts involved in the research believe that these markers could be used in future machine learning models to streamline the diagnosis and treatment planning pertaining to ADHD.
Currently, ADHD diagnosis involves a checklist that assesses for the presence or absence of several symptoms, including impulsivity, difficulty paying attention, restlessness, etc. But researchers involved in a new study have found a potential role for neuroimaging in the diagnostic process of ADHD, suggesting that imaging biomarkers on MRI exams prove that the neurodevelopmental disorder is more than just a checklist of adverse behaviors, that it instead has neuro-structural and functional imaging manifestations that can be used to identify the root cause of children’s symptoms.
“At times when a clinical diagnosis is in doubt, objective brain MRI scans can help to clearly identify affected children,” study co-author Huang Lin, a post-graduate researcher at the Yale School of Medicine in New Haven, Connecticut, said in a statement. “Objective MRI biomarkers can be used for decision making in ADHD diagnosis, treatment planning and treatment monitoring.”
For their work, the researchers utilized data from 7,805 patients involved in the Adolescent Brain Cognitive Development (ABCD) study. A total of 1,798 of their subjects had received a prior ADHD diagnosis and had available structural MRI scans, diffusion tensor imaging and resting-state functional MRI exams. Researchers used the imaging data to determine whether any specific neuroimaging metrics were associated with a diagnosis of ADHD among the group.
Through this, they found alterations in almost every region of the brain in children with ADHD.
“The pervasiveness throughout the whole brain was surprising since many prior studies have identified changes in selective regions of the brain,” Lin said.
The ADHD patients’ imaging displayed numerous abnormal connectivity patterns, thinning of the brain cortex and microstructural changes within their white matter, especially in the frontal lobe, which is responsible for impulse control and focus.
Lin suggested that the team’s findings were significant enough that they could provide a framework for developing machine learning models that can predict ADHD using MRI data. This could provide clinicians with more objective means of diagnosing and treating children who struggle with symptoms of ADHD, Lin said, adding that the disorder is often undiagnosed or misdiagnosed due to the subjective nature of ADHD assessments.
Additional coverage of RSNA 2022 is available here and here.
In addition to her background in journalism, Hannah also has patient-facing experience in clinical settings, having spent more than 12 years working as a registered rad tech. She joined Innovate Healthcare in 2021 and has since put her unique expertise to use in her editorial role with Health Imaging.