A recently published study suggests that using computational models to analyze neurocognitive testing data may help clinicians improve the diagnosis and treatment of attention-deficit/hyperactivity disorder (ADHD). Here, lead author Nadja Ging-Jehli, a doctoral candidate in computational psychiatry, explains the findings of the review, how clinicians can use it in practice, and her ongoing work in the field.
What led you and your colleagues to review the use of computational models to understand cognitive processes in ADHD?
My passion is helping people with ADHD (and other mental health conditions) by using my skills in mathematics and neuroscience and my experience in studying human behavior in laboratory experiments (eg, social and cognitive tasks). When I started reviewing the findings in the field of neurocognitive testing for ADHD, I noticed two aspects that motivated me to write this review article.
First, neurocognitive testing for ADHD seemed a confusing field because many different tests have been used and many different contradicting findings have been reported. At the same time, most studies concentrated on specific clinical samples (eg, boys with ADHD in a specific age range). I then realized that the findings became less contradictory the more I investigated the details of each study. For instance, specifics in the cognitive tasks conducted by clinicians can lead to important behavioral differences that can be understood if we consider the findings of research from cognitive psychology. Moreover, I noticed that certain tasks (eg, stop signal tasks) have been used to measure specific cognitive concepts (eg, inhibition failure) without compelling evidence that these tasks indeed measure those concepts.
Second, computational modeling has long been used in nonclinical research areas (eg, cognitive psychology, cognitive neuroscience, behavioral economics) to understand how people make decisions in cognitive and social tasks within a laboratory setting. Unfortunately, cognitive research after 1970 has had little influence on the neurocognitive tests used for clinical purposes.
Based on all of these observations, our review article focuses on integrating findings from cognitive psychology and computational modeling into clinical neurocognitive testing. We also provide a thorough overview of the current field of neurocognitive testing and we discuss barriers that need to be overcome to better utilize neurocognitive testing for clinical practices.
Please briefly describe the study method and your most significant finding(s).
We reviewed 50 clinical studies on a broad range of cognitive tasks. In so doing, we provide a synopsis of differences in sample characteristics, task procedures, and effect sizes across all studies (found in the appendix of our article).
The synthesis of our review suggests that there is more information available from neurocognitive testing, if its data are analyzed with computational models. This additional information may benefit clinicians not only in diagnosing ADHD, but also in selecting and assessing treatments. However, different research fields (eg, clinical science, cognitive psychology, computational modelers) need to work together to: 1. set new standards on how to report findings from those tests (eg, some report test scores and others report aggregated measures, which makes comparisons across studies impossible); 2. understand the difference between analyzing neurocognitive data with summary statistics (eg, mean response times) versus analyzing data with computational models; 3. adjust tasks to make them suitable for model applications. In our article, we provide an introduction to 3 commonly used computational models and we list the requirements that need to be met to apply those models.
Eventually, by integrating the findings of the reviewed studies, we also found evidence that computational models could be promising tools to characterize different ADHD endophenotypes.
How do you think computational psychiatry can change the process of diagnosing ADHD?
Only a few studies have used computational modeling to inform clinical practices such as diagnosis, treatment selection, and treatment assessment. Those studies which did use computational modeling with neurocognitive testing mostly focused on quantifying cognitive differences between individuals with and without ADHD. However, it is not enough for the field of computational psychiatry to simply apply models to clinical data from neurocognitive tests. It will be necessary for the field to show that the results of those models provide insights of clinical relevance; there is a difference between statistical significance and clinical significance. I hope that clinicians will find neurocognitive testing more useful in their daily practice if they start to combine it with computational model analyses.