RESEARCH

The TNN lab seeks to develop a hypothesis-driven mechanistic understanding of the neural computations that motivate behavior in order to understand, predict, and treat depression. Specifically, our lab seeks to:

OBJECTIVE 1

Delineate a model-based theory of antidepressant effects that accounts for the interaction between drug effects, mood, expectancies, and learning mechanisms

A computationally-informed model of antidepressant treatment responses will help to reliably reflect the richness and variability of this complex phenomenon. Insights from this work may extend beyond depression to explain the mechanisms that lead to symptom recovery broadly, significantly impacting health care delivery. In addition, we seek to develop a model of reinforcement learning that would help predict individual expectancies of improvement and their impact in mood responses.

OBJECTIVE 2

Identify novel targets for antidepressant effects

The identification of neural and molecular targets for mood improvement opens the possibility of modulating these networks as novel mechanisms of mood improvement. Our lab uses pharmacological approaches to modulate the opioid system in order to understand its implication in mood disorders. Furthermore, we use transcranial magnetic stimulation (TMS) to target prefrontal regions implicated in the response to antidepressant treatments.

OBJECTIVE 3

Understand sources of treatment response variability

Treatment responses are subject to both, within- and between-subject variability. Our lab seeks to identify stable traits and biological markers of treatment response that could eventually be used both clinically and in drug development.

OBJECTIVE 4

Understand sources of vulnerability to depression and suicidal behavior among teens

In recent years, social media has emerged as a significant predictor of mental health, contributing to both peer pressure and connectedness among adolescents. Our lab seeks to investigate neural markers of vulnerability to depression and suicidal behavior among teens exposed to social media.