The introduction of computer simulation to the identification of symptoms in children with attention deficit/hyperactivity disorder (ADHD) has potential to provide an additional objective tool to gauge the presence and severity of behavioral problems, Ohio State University researchers suggest in a new publication.
Most mental disorders are diagnosed and treated using questionnaires and clinical interviews. For about a century, cognitive tests data has been used in the diagnostic process to help clinicians understand how and why people behave a certain way.
Cognitive testing in ADHD is used for diagnosing a variety symptoms and deficiencies, including poor working memory, altered perception, poor time perception, difficulty maintaining attention and impulsive behaviour. Children are asked to press a keyboard key or avoid hitting it when they see a particular word, symbol, and/or stimulus. This is the most common kind of performance test.
However, cognitive tests for ADHD often fail to capture the complexity and severity of ADHD symptoms. Researchers from Ohio State have published a review in which they report that computational psychotherapy — which simulates normal brain functions and compares them to dysfunctional ones observed in tests — could be a valuable addition to ADHD diagnosis. Psychological Bulletin.
The research team reviewed 50 studies on cognitive tests for ADHD and identified three types of computational models that could be used to supplement these tests.
It is well known that children with ADHD have a slower ability to make decisions and complete tasks than children without the disorder. Tests rely on average response times to explain this difference. There are many aspects to ADHD that a computational modeling could help pinpoint. It could also provide information for parents and teachers to make ADHD easier for children.
“We can use models for simulation of decision-making and see how it happens over time — and do better job of figuring why children with ADHD take more time to make decisions,” said Nadja Geing-Jehli (lead author of the review, and a graduate student of psychology at Ohio State).
Ging-Jehli reviewed the data with Roger Ratcliff (professor of psychology) and L. Eugene Arnold (professor emeritus psychiatry & behavioral health).
Researchers offer recommendations for clinical practice and testing to help achieve three main goals: better characterizing ADHD, anxiety, and depression, and improving treatment outcomes (about one third of ADHD patients do not respond to medical treatment), as well as predicting which ADHD children will “lose” their diagnosis as adults.
The problem is illustrated by the decision-making behind the wheel. Drivers know that a green light means they can cross an intersection. But not everyone hits the pedal at the same moment. This behavior could be compared to a common cognitive test that would expose drivers to the same red-green light scenario repeatedly to determine their average reaction time. The average and deviations from it would then be used to classify the typical driver as well as the disordered driver.
This method has been used to find that people with ADHD are more likely to “start driving” slower than those without ADHD. This determination does not take into account the possibility that they may be distracted, daydreaming, nervous, or feeling anxious in a lab setting. Computer modeling can provide more information, including a wider range of reactions.
Ging-Jehli stated that the method is not able to understand the underlying characteristics of mental-health disorders like ADHD and that it does not provide the best treatment options for each individual. “We can use computational modelling to understand the factors that cause the observed behavior. These factors will help us understand a disorder better.
“We propose using the whole distribution of reaction times, taking into account the slowest and fastest reaction times to distinguish between different types ADHD.”
The review also found a complicating factor in ADHD research moving forward. This is a wider range externally apparent symptoms as well subtle characteristics that are difficult with the most common testing methods. The researchers believe that ADHD children are complex biologically based and that one task-based test will not be enough to diagnose them.
“ADHD does not just refer to the child who is restless in their chair or fidgeting. It also includes the child who daydreams, but isn’t as attentive. Ging-Jehli explained that although the child is less introverted and has fewer symptoms than a child with hyperactivity it doesn’t mean the child doesn’t suffer. She said that daydreaming is more common in girls than in boys.
Ging-Jehli described computational psychotherapy as a tool that could also consider — keeping the analogy going — mechanical differences in a car and how they could affect driver behavior. These dynamics can make ADHD harder to understand but can also open the door for a wider range of treatment options.
“We must consider the different types and conditions that drivers are exposed to. She said that based on one observation, it is impossible to draw conclusions about diagnosis or treatment options.
Cognitive testing and computational modeling should not replace existing clinical interviews or questionnaire-based procedures. They should be seen as complements that add value and provide new information.
According to researchers, a number of tasks that assess cognitive and social characteristics should be assigned to diagnoses. Additionally, more consistency is required across studies to ensure that the cognitive tasks used to evaluate the appropriate cognitive concepts are used.
The combination of cognitive testing and physiological tests, including eye-tracking and EEGs that measure brain electrical activity, could provide powerful and quantifiable data that can help to improve the accuracy of a diagnosis and predict which medications will be most effective.
Ging-Jehli will put these suggestions to the test by applying a computational model to a study of a specific neurological intervention for children with ADHD.
Ging-Jehli stated that the purpose of their analysis was to demonstrate that there is a lack in standardization and so much complexity. Symptoms are difficult to measure using existing tools. For children and adults with ADHD to enjoy a better quality and get the best treatment, we need to better understand it.
This research was supported by both the Swiss National Science Foundation as well as the National Institute on Aging.