The use of computer simulation in the identification of symptoms in children with attention-deficit/hyperactivity disorder (ADHD) has the potential to provide an additional objective tool for determining the presence and severity of behavioral problems, according to Ohio State University researchers in a new publication.
Most mental health disorders are diagnosed and treated using clinical interviews and questionnaires, and for nearly a century, data from cognitive tests has been added to the diagnostic process to help clinicians learn more about how and why people behave in certain ways.
ADHD cognitive testing is used to identify symptoms and deficits such as selective attention, poor working memory, altered time perception, difficulties maintaining attention, and impulsive behavior. In the most common type of performance test, children are instructed to either press or avoid pressing a computer key when they see a specific word, symbol, or other stimuli.
However, in the case of ADHD, these cognitive tests frequently fail to capture the complexities of symptoms. Computational psychiatry, which compares a computer-simulated model of normal brain processes to dysfunctional processes observed in tests, could be an important supplement to the ADHD diagnostic process, according to Ohio State researchers in a new review published in the journal Psychological Bulletin.
The researchers examined 50 studies of cognitive tests for ADHD and described how three types of computational models could supplement these tests. Children with ADHD take longer to make decisions while performing tasks than children without the disorder, and tests have relied on average response times to explain the difference. However, there are complexities to that dysfunction that a computational model could help identify, providing information that clinicians, parents, and teachers could use to make life easier for children with ADHD.
In our review, we show that this method has multiple flaws that prevent us from understanding the underlying characteristics of a mental-health disorder like ADHD and from finding the best treatment for different individuals. We can think about the factors that cause the observed behavior using computational modeling.
Ging-Jehli
“We can use models to simulate the decision-making process and see how decision-making occurs over time and do a better job of figuring out why children with ADHD take longer to make decisions,” said Nadja Ging-Jehli, lead author of the review and a graduate student in psychology at Ohio State.
Ging-Jehli worked with Ohio State faculty members Roger Ratcliff, professor of psychology, and L. Eugene Arnold, professor emeritus of psychiatry and behavioral health, to complete the review. The researchers make testing and clinical practice recommendations to achieve three main goals: better-characterizing ADHD and any associated mental health diagnoses such as anxiety and depression, improving treatment outcomes (about one-third of patients with ADHD do not respond to medical treatment), and potentially predicting which children will “lose” the ADHD diagnosis as adults.
Decision-making behind the wheel of a car exemplifies the issue: Drivers understand that when a red light turns green, they can proceed through an intersection; however, not everyone applies the brakes at the same time. A common cognitive test for this behavior would involve repeatedly exposing drivers to the same red light-green light scenario in order to arrive at an average reaction time and then using that average, as well as deviations from it, to classify the typical versus disordered driver.
This method has been used to determine that people with ADHD are typically slower to “start driving” than those who do not have ADHD. However, that conclusion excludes a number of possibilities that could explain why they’re slower: they could be distracted, daydreaming, or nervous in a lab setting. The wide range of reactions captured by computer modeling could provide more and more useful information.
“In our review, we show that this method has multiple flaws that prevent us from understanding the underlying characteristics of a mental-health disorder like ADHD and from finding the best treatment for different individuals,” Ging-Jehli said. “We can think about the factors that cause the observed behavior using computational modeling.” These factors will broaden our understanding of a disorder, recognizing that different types of people have different deficits that require different treatments. We propose using the entire distribution of reaction times, taking into account the slowest and fastest reaction times, to differentiate between different types of ADHD.”
The review also identified a complicating factor for future ADHD research: a broader range of externally visible symptoms as well as subtle characteristics that are difficult to detect using the most commonly used testing methods. The fact that children with ADHD have so many biological differences suggests that a single task-based test is insufficient to make a meaningful ADHD diagnosis, according to the researchers.
“ADHD affects more than just the child who fidgets and squirms in his or her chair. It is also the child who is distracted by daydreaming. Even if the child is more introverted and does not exhibit as many symptoms as a child with hyperactivity, this does not mean that the child is not suffering “Ging-Jehli explained. Daydreaming is especially common in girls, who are not as frequently enrolled in ADHD studies as boys, according to her.
Ging-Jehli described computational psychiatry as a tool that could take into account mechanical differences in the car and how that might influence driver behavior. These dynamics can make ADHD more difficult to understand, but they also open the door to a broader range of treatment options.
“We must account for the various types of drivers and comprehend the various conditions to which they are subjected. We cannot draw conclusions about diagnosis and treatment options based on a single observation “She stated.
“However, cognitive testing and computational modeling should not be viewed as a replacement for existing clinical interviews and questionnaire-based procedures, but rather as supplements that add value by providing new information.”
According to the researchers, rather than just one task assessing social and cognitive characteristics, a battery of tasks assessing social and cognitive characteristics should be assigned for a diagnosis, and more consistency across studies is required to ensure the same cognitive tasks are used to assess the appropriate cognitive concepts.
Finally, combining cognitive testing with physiological tests, particularly eye-tracking and EEGs, which record electrical activity in the brain, could provide powerful objective and quantifiable data to help clinicians make more reliable diagnoses and better predict which medicines would be most effective.
Ging-Jehli is putting these ideas to the test in her own research, using a computational model to study a specific neurological intervention in children with ADHD.
“The goal of our analysis was to demonstrate that there is a lack of standardization and so much complexity, and symptoms are difficult to measure using existing tools,” Ging-Jehli explained. “We need to better understand ADHD in order for children and adults to have a better quality of life and receive the most appropriate treatment.”
