This research project aims to characterize the broad cognitive construct of motivation in healthy individuals and patients with major depressive disorder (MDD) and to examine underlying dopaminergic network connectivity stemming from the ventral tegmental area (VTA) using ultra-high field 7-Tesla MRI. Motivational deficits represent a core feature of MDD and are associated with the debilitating symptom of anhedonia. An established measure of motivation, the Effort Expenditure for Rewards Task (EEfRT), which maps onto the reward valuation construct of the Research Domain Criteria (RDoC) framework, does not capture internally-generated motivation, known as volition in classic psychology models. Therefore the dimensional construct of internally-generated motivation has not yet been empirically explored in MDD. Neurally, motivation for rewards, a subjective measure of volition and anhedonia have each been separately linked to VTA projections to nucleus accumbens and medial prefrontal cortex, and insula. However, VTA circuitry has not been fully examined in relation to motivation constructs in MDD, partly due to the limited feasibility of discerning VTA with 3-Tesla MRI. We therefore aim to address these scientific gaps in our understanding of the construct of motivation and VTA circuitry in MDD. We will employ a novel objective measure of internally-generated motivation, an established measure of external motivation (EEfRT) and a validated ultra-high field 7- Tesla MRI protocol for imaging VTA circuitry in patients with MDD and healthy individuals. This combination of novel and established cognitive measures, and high- resolution MRI will allow precise insight into the neurocognitive mechanisms of MDD. Accompanying career development training plans will fine-tune skills in neuroimaging and cognitive neuroscience and facilitate new skills in clinical research methods. With diverse expert mentorship, specific training goals include: 1) Development of skills in clinical diagnostics and phenotyping for translational patient-oriented research; 2) Refinement of existing skills in multi-modal ultra-high field MRI; 3) Refinement of skills in cognitive and computational neuropsychiatry.
Depression is one of the world's largest public health issues to date, but there is a current lack of widely effective treatments partly due to our limited understanding of the etiology and biological mechanisms of this complex disorder. This project aims to refine our understanding of the cognitive and neural network foundations of depression and how they relate to some of the most challenging symptoms. This work will ultimately provide targets for therapeutic intervention, including for cognitive-behavioral therapies or digital cognitive training therapeutics, as well as neural modulators such as pharmaceuticals or next-generation invasive and non-invasive brain stimulation technologies.
