Previous studies have often looked at whether children with attention deficit hyperactivity disorder (ADHD) have differences in specific areas of the brain. A new study aimed to question this method, using a global approach. It opens the way to possible improvement of diagnostic tools for this neurodevelopmental disorder.
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by cognitive, behavioral, and emotional differences. The structure and functioning of the brains of people with it differ from the rest of the population.
The brain as a whole
A new study published in the Journal of Neuroscience put in lightlight the complex relationship between brain connectivity and hyperactive behavior disorders. US researchers used a large national data set (on nearly 12,000 children) and neuroimaging to confirm the cumulative effects of ADHD on the entire brain, instead of only considering specific areas .
Concretely, the researchers used thefunctional magnetic resonance imagingfunctional magnetic resonance imaging in a resting state in order to visualize the overall brain activity of children. It also allowed them to identify patterns of brain connectivity specific to ADHD using a polyneuro risk score (or PNRS), a method that calculates the likelihood of a health outcome based on brain activity. of a patient.
Facilitate the diagnosis of children
The study’s holistic approach confirms that ADHD involves multiple areas of the brain that interact in complex ways. “ By assessing the cumulative effects of regions across the entire brain, we now view ADHD as a whole brain problem, which may make it easier to predict which children have ADHD and how much gravitygravity of this disorderexplained Michael A. Mooney, corresponding author of the study. Ultimately, we hope this will contribute to the early identification of children most at risk, so that they can receive the help they need as soon as possible. »
Researchers are also interested inapplicationapplication of the PNRS method to predict the risk of other neurological disorders, such as depression or anxiety.
