MRI scans can detect brain differences in people with ADHD
Psychoradiology can help diagnosis the condition and its most common forms, says Qiyong Gong, MD.
Data from magnetic resonance imaging can help identify people with attention deficit hyperactivity disorder and distinguish between the condition’s two most common subtypes, according to a recent study in the journal Radiology.
ADHD, characterized by age-inappropriate inattention, hyperactivity and impulsive behavior, is one of the most common neurodevelopmental disorders, affecting five percent to seven percent of children and adolescents worldwide. The condition is currently diagnosed using patient and teacher behavior reports and assessment of behavioral problems.
However, that form of diagnosis is relatively subjective, and ADHD behaviors can overlap with other disorders. Now, there’s evidence that using neuro imaging data in the developing field of psychoradiology may be able to provide objectivity and clarity in diagnosing the disorder.
Psychoradiology applies imaging data analysis to mental health and neurological conditions. Imaging has long been seen as a promising way to attempt to diagnose ADHD. However, previous attempts used less comprehensive neuroanatomical evaluation, according to the study’s authors.
The researchers, from West China Hospital of Sichuan University in Chengdu, China, introduced an analytical framework for psychoradiology that uses machine learning and cerebral radiomics, the extraction of a large amount of information from digital imaging, that can be mined for disease traits and other properties.
They took brain images of 83 children newly diagnosed with ADHD but never treated and compared them with the images of 87 age- and sex-matched healthy children. They screened for more than 3,100 features, such as shape, thickness and curvature of different brain regions, in both the gray and white matter.
While there was no overall difference in total brain volume between the healthy and ADHD children, there were alterations in the shape in three brain regions—the left temporal lobe, bilateral cuneus and the left central sulcus. These brain regions are involved in motor skills and in processing visual information, both affected by ADHD. In addition, other features showed differences between the two most common subtypes of ADHD, the inattentive subtype and the combined inattentive/hyperactive subtype.
The scans were 74 percent accurate in discriminating between ADHD and healthy children and 80 percent accurate in distinguishing between the two subtypes.
“Our findings establish the potential utility of classification strategies with MR imaging data for diagnosis of a common mental disorder and provide new insights into the specific neurologic-anatomic features related to the disorder and its subtypes,” the study authors conclude.
The researchers plan to recruit more patients to validate the results and apply this approach to other mental or neurological disorders and test its feasibility in a clinical environment, says study co-author Qiyong Gong, MD.
"This imaging-based classification model could be an objective adjunct to facilitate better clinical decision making," Gong says. "Additionally, the present study adds to the developing field of psychoradiology, which seems primed to play a major clinical role in guiding diagnostic and treatment planning decisions in patients with psychiatric disorders."
ADHD, characterized by age-inappropriate inattention, hyperactivity and impulsive behavior, is one of the most common neurodevelopmental disorders, affecting five percent to seven percent of children and adolescents worldwide. The condition is currently diagnosed using patient and teacher behavior reports and assessment of behavioral problems.
However, that form of diagnosis is relatively subjective, and ADHD behaviors can overlap with other disorders. Now, there’s evidence that using neuro imaging data in the developing field of psychoradiology may be able to provide objectivity and clarity in diagnosing the disorder.
Psychoradiology applies imaging data analysis to mental health and neurological conditions. Imaging has long been seen as a promising way to attempt to diagnose ADHD. However, previous attempts used less comprehensive neuroanatomical evaluation, according to the study’s authors.
The researchers, from West China Hospital of Sichuan University in Chengdu, China, introduced an analytical framework for psychoradiology that uses machine learning and cerebral radiomics, the extraction of a large amount of information from digital imaging, that can be mined for disease traits and other properties.
They took brain images of 83 children newly diagnosed with ADHD but never treated and compared them with the images of 87 age- and sex-matched healthy children. They screened for more than 3,100 features, such as shape, thickness and curvature of different brain regions, in both the gray and white matter.
While there was no overall difference in total brain volume between the healthy and ADHD children, there were alterations in the shape in three brain regions—the left temporal lobe, bilateral cuneus and the left central sulcus. These brain regions are involved in motor skills and in processing visual information, both affected by ADHD. In addition, other features showed differences between the two most common subtypes of ADHD, the inattentive subtype and the combined inattentive/hyperactive subtype.
The scans were 74 percent accurate in discriminating between ADHD and healthy children and 80 percent accurate in distinguishing between the two subtypes.
“Our findings establish the potential utility of classification strategies with MR imaging data for diagnosis of a common mental disorder and provide new insights into the specific neurologic-anatomic features related to the disorder and its subtypes,” the study authors conclude.
The researchers plan to recruit more patients to validate the results and apply this approach to other mental or neurological disorders and test its feasibility in a clinical environment, says study co-author Qiyong Gong, MD. "This imaging-based classification model could be an objective adjunct to facilitate better clinical decision making," Gong says. "Additionally, the present study adds to the developing field of psychoradiology, which seems primed to play a major clinical role in guiding diagnostic and treatment planning decisions in patients with psychiatric disorders."
More for you
Loading data for hdm_tax_topic #better-outcomes...