Research

Exploring the feasibility of predicting substance use disorder treatment outcomes by combining continual physiological measures in naturalistic settings with clinical data.

Cell type specific tPRS for MDD in adolescents

Using genomic SEM, I will explore the genetic overlap between major depressive disorder and posttraumatic stress disorder symptomatology to visualize their genetic similarities and differences.

Our study aims to investigate the interaction between genetic risk for four neurodevelopmental and psychiatric disorders and prenatal air pollution exposure on child cognition, behavior, and brain development.

This exploratory analysis aims to characterize the TAY Cohort regarding differential categories of self-injurious thoughts and behaviours using clinical, demographic, and lifestyle factors of treatment-seeking youth in Toronto. Using a series of multivariable linear models, we compare different contrasts of SITB to identify unique and overlapping associations with a suite of measures collected at baseline.

We indexed genetic risk for depression using polygenic risk scores and associated these scores with various circulating biomarkers to identify candidate biomarkers for depression. We then built complex linear models with a focus on age and sex effects to examine differences across human characteristcs and the human lifespan.

The use of meta association-boosted similarity network fusion to identify groupings enriched for psychosis spectrum symptoms.

Using linear modeling and differential expression analysis I have defined transcriptomic and pathway signatures that are significantly associated with depressive symptoms in late-life.

I use machine learning to characterize the biopsychosocial measures associated with youth psychosis and better understand ther combined effects in confering risk and resilience

We applied an iterative modelling approach with different domains of data to fit machine learning models to self-reported self-injurious thoughts and behaviours (SITB) to evaluate the impact of each bio- psycho- and social- input features. We then applied post-hoc feature importance analyses to understand the stability and robustness of relative risk factors for SITB.

How do brain dynamics change in association with sleep habits, cognitive abilites, and depression symptoms