In an effort to better identify symptoms of attention deficit hyperactivity disorder (ADHD) in children, researchers said that adding computational models to cognitive tests in gauging the presence and severity of behavioral problems in kids would be helpful.
Cognitive tests are used in ADHD diagnosis to identify varied symptoms and deficits of the child. This could include selective attention, poor working memory, altered time perception, difficulty maintaining attention, and impulsive behavior.
Researchers from Ohio State University studying ADHD said that cognitive tests alone do not capture the complexity of ADHD, so computational models in psychiatry would help. It could be an important supplement to ADHD diagnosis, said the researchers in their new review published in Psychological Bulletin.
How Will Computational Models Help
Researchers said that computational models could better capture the complexity of the symptoms and deficits associated with ADHD. They investigated 50 studies from a broad range of ADHD tests and identified four areas that computational models could improve.
According to Kenny Walter's article in HCP Live, these include "requirements for appropriate application of the computational models, the consideration of sample characteristics and neurophysiological measures, the integration of findings from cognitive psychology into the literature of cognitive testing to reconcile mixed evidence, and future directions for the study of ADHD endophenotypes."
Their study shows that computational models and neuropsychological measures help yield a more accurate diagnosis for ADHD by identifying the cognitive characteristics present in ADHD. It is helpful for clinicians, parents, and teachers to make life easier for those with ADHD.
The specific cognitive characteristics of ADHD endophenotypes can be present even beyond the available research because many cognitive tests lack the sensitivity needed to detect clinical characteristics of ADHD.
Study lead author Nadja Ging-Jehli, a graduate student in psychology at Ohio State, said that computer simulations could show the decision process and see how decision-making happens over time. It would help do better at figuring out why children with ADHD take longer to make their decisions.
Future Uses of Computational Models
The researchers said that computational models could help achieve three goals: better ADHD diagnoses and other psychological disorders, improving treatment outcomes of ADHD patients that do not respond to medical treatment, and predicting which children will lose the ADHD diagnosis as they become adults.
Moreover, the computational models also yielded externally evident symptoms and subtle characteristics that are difficult to detect using common testing methods. This shows how complex diagnosing ADHD could be as a single task-based test is insufficient to make the proper diagnosis.
Therefore, the researchers recommended using various new methods in diagnosing ADHD patients by using a combination of cognitive tests with other methods, such as eye-tracking and EEGs. In that way, ADHD diagnosis would be more reliable and could better help clinicians shape treatment decisions.
Check out more news and information on Psychology in Science Times.
