I am driven to provide multiple perspectives in the diagnosis and treatment of ADHD, autism, mood disorders, and other related disorders. Specifically, I emphasize the examination of individual differences, the characterization of different phenotypes, and the advantage of linking biomedical, neuronal, and behavioral measures using computational models. My research is organized into three avenues.
Creating a multidimensional “assessment world”
I am eager to develop new cognitive and social-cognitive laboratory paradigms to improve current (neuro)cognitive testing for mental health conditions. I place a strong emphasis on integrating biomarkers such as those collected with eye-tracking and electroencephalogram (EEG) and to develop paradigms suitable for the application of computational models. I also place a strong emphasis on social cognition and on incorporating the latest research from cognitive psychology. I started this line of research as part of my dissertation, and with the help of my first research grant as part of my past Swiss National Science Foundation fellowship.
Utilizing and enhancing joint-modeling approaches to combine behavioral and physiological measures
To date, the links between physiological measures (e.g., task-evoked pupil responses, EEG event-related potentials, blood-oxygen-level-dependent signals) and behavior are not well understood. Computational modeling can help to establish a link between latent mental components and neurophysiological components. In this line of research, I will use and enhance joint-modeling approaches that will integrate different sources of individual differences (e.g., genetics, environmental factors, cognitive abilities) into one modeling framework. This will initially involve using existing clinical datasets to examine how individual differences in behavior relate to individual differences in physiological measures. Aside from utilizing existing clinical datasets, I am also eager to collect data from improved neurocognitive tests as part of new studies and treatment interventions (see point 1).
Assessing the practical usefulness of computational psychiatric tools
Computational psychiatry is a nascent field that needs to prove its usefulness for medical doctors and clinicians not only for diagnostic purposes but also for selecting and tailoring treatments. Therefore, I have applied computational modeling and machine learning to existing datasets from clinical treatments to examine different phenotypes of ADHD, e.g., subjects with ADHD and depression, autism, etc. In another study, I found that cognitive components (identified by pre-treatment cognitive testing and computational modeling) predicted the efficacy of neurofeedback therapy for ADHD patients. Further research is needed to replicate these exploratory results and to examine the usefulness of these computational psychiatric tools. I look forward to eventually making these tools accessible to researchers and clinicians with different backgrounds and skill sets. However, there are multiple barriers that we first need to overcome, some of which have been summarized in a Psychological Bulletin article by me, Dr. Ratcliff, and Dr. Arnold (see: publications). As part of my dissertation, I have created improved neurocognitive tests in the domain of selective attention, cognitive control, cognitive flexibility, and social cognition. I am eager to use these novel tasks in future studies, to assess the robustness of the results and to replicate them in other samples. I am always open to discuss about how to improve neurocognitive testing, and to collaborate in future studies, please contact me if you would like to hear more.