My research interests consist of investigating the nature of social deficits in autism spectrum disorder (ASD), finding biologically relevant biomarkers, and uncovering potential treatments that can help individuals with ASD.

Throughout the past 14 years, I adopted a multimodal approach by combining behavioral research with neuroimaging research to study brain function and behavioral mechanisms in ASD. I developed new outcome measures that can capture explicitly and implicitly participants’ capacities to interact with others and to empathize with others. I conducted biomedical research and performed clinical trials both in France (during my PhD training) and in the United States (during my postdoctoral fellowship at Emory University in Atlanta). I am passionate about training, mentoring, and promoting inclusive, diverse, and supportive research environments for graduate students and trainees. 

My academic and research training, as well as my clinical experience, have provided me with a unique background in multiple neuroscience disciplines, including behavioral neuroscience, cognitive science, clinical science, animal and human behavior, neuroimaging, and pharmacology. During my Master’s degree in Neuroscience, I was able to conduct behavioral research and eye-tracking studies (via an eye tracker system) in ASD with Dr. Sirigu (PhD). During my PhD, I designed and conducted a behavioral and pharmacological study that showed positive effects of oxytocin on social behavior and eye gaze in patients with autism. I also developed a new adapted version of the Cyberball to measure participants’ capacity to differentiate between trustworthy and untrustworthy players. This work led to one of the first papers in the field of oxytocin and social functioning in autism (Andari et al. 2010). I have also conducted structural and functional neuroimaging research (fMRI) and used voxel-based morphometry to assess correlations between brain grey matter in amygdala areas and social personalities. I conducted neuroimaging and pharmacological studies and studied the effects of drug treatment on modulating the BOLD activity in key social brain areas involved in face processing. The neuroimaging work to two publications in cerebral cortex and cortex journals.

During my postdoctoral work, I implemented new behavioral paradigms with new versions of the Cyberball task that aim to study salience to social cues and empathic responses in participants with autism. I also used the implicit association test to study implicit social affiliations. I conducted a very complex clinical trial during which adults with autism participated in several MRI scanning visits to evaluate the effect of drug treatment on brain function. I also measured participants eye-movements inside the scanner. Following oxytocin intake, participants conducted a series of social tasks and resting state inside the MRI scanner. I also conducted epigenetic work to study the role of biomarker in predicting brain function and social severity of symptoms. This work has already led to several papers, and more work will be published in the next few months with a high impact on the field.

During my faculty position, I am concentrating on uncovering biologically relevant biomarkers and tackling the heterogeneity of ASD by conducting a deep screening on all the main core of dysfunctions in ASD (such as social functioning, affective behaviors, and negative valence) in parents of ASD and in ASD. I believe that an intergenerational approach will help alleviate some of the unknowns about the nature of the disorder and its phenotypical variability. I am also dedicated to the transdiagnostic approach and comparing ASD performance not only to neurotypical controls, but also to other developmental deficits such attention-deficit/hyperactivity disorder (ADHD) and others. My research associate and I have been conducting fMRI resting-state analysis as well as task-based BOLD activity. Along with the computer specialist in my lab, we have been implementing and validating new proper computational approaches using machine learning, random forest, SHAP, and hierarchical clustering to predict ASD diagnosis and ASD biotypes. A major goal is to develop personalized treatments for ASD biotypes, using behavioral and pharmacological approaches.