Research

Biomarkers of Suicide Risk
Methods: Task fMRI, Task EEG
Rationale: Major depressive disorder (MDD) is the most common mental disorder among suicide deaths, the ninth leading cause of death in Canada. Currently, suicidal patients respond less favourably to antidepressant treatment than those without, stressing the urgent need to understand behavioural and biological factors underlying suicidality in the hopes of developing tailored treatments. We propose that one way to better understand these factors is to elucidate the neurobiological link between suicide and comorbid symptoms. In projects funded by the American Foundation for Suicide Prevention, and in collaboration with Stanford University, we are using task-based neuroimaging to understand how specific symptoms interplay with suicide risk.

Task-Based rTMS Neuronavigation
Methods: Task fMRI, Resting-State fMRI
Rationale: We are interested in how task-fMRI may improve resting-state fMRI-based targeting approaches in non-invasive brain stimulation. While both resting-state and task-evoked functional connectivity can generate insightful behavioural predictions, models based on task-evoked connectivity are generally more successful at predicting individual differences in behaviour, including specific cognitive traits and psychiatric diagnoses with high accuracy. Projects funded by the St. Michael’s Foundation and CIHR will investigate the role of specific cognitive domains and how they may be used to improve precision psychiatric treatments like fMRI-guided rTMS, or assist with selecting antidepressant treatments (i.e., rTMS vs. SSRIs, or rTMS vs. intravenous ketamine).

Brain Aging in Depression
Methods: Structural MRI
Rationale: Brain aging research harnesses interindividual variability of neural correlates of aging. Machine learning models are trained on large normative structural magnetic resonance imaging datasets to predict age. Depression is associated with accelerated brain aging, but the effect is not related so overall depression severity, and is some cases, not predictive of treatment response. Brain aging studies in MDD do not consider symptom heterogeneity, and it is possible that this factor contributes to low observed effect sizes or null findings in previous brain aging studies studying MDD. A project funded by the Brain and Behavior Fund and the Labatt Family Network will integrate two approaches, brain age normative modeling and subtyping in MDD, which may yield novel insights into the etiology of the disorder and enable personalized treatments.

Batch Effects & fMRI
Methods: Resting-State fMRI
Rationale: Multi-site fMRI studies are critical when aiming to generalize in a heterogeneous population like depression, or when prospectively evaluating candidate biological markers of treatment response or selection. However, large sample sizes alone are insufficient to ensuring strong findings. More specifically, variations in hardware, software, and acquisition parameters lead to nonbiological variability that decrease statistical power and confound results. Multi-band, multi-echo fMRI (MB/ME-fMRI) is a novel imaging sequence that may significantly improve the reliability and reproducibility of functional neuroimaging. In a project funded by Brain Canada via the ENABLE Integrative Discovery Platform, we will otimize, develop, and validate MB/ME-fMRI sequences and statistical harmonization techniques, with an emphasis on improving inter-scanner reliability of resting-state fMRI scans.