Current & Prospective Research Interests

I'm passionate about translational science that uses innovations in computational modeling to advance our understanding of psychiatric illnesses - specifically transdiagnostic symptom structures that cut across affective and psychotic illnesses. Currently, I use human neuroimaging data to study the extent that information processing dynamics are exhibited across the functional connectome, cognitive control processes, flexible learning, neural mechanisms based in brain network interactions, abstract representations, and the link between local and distributed neural processes. I'm also interested in mutlimodal imaging and modulation techniques, cognitive science, and neurogenomics.

My CV

cognitive control

Cognitive control - or the flexible implementation of processes supporting goal-directed cognition and behavior - is pivotal for adaptive thought and action. It's also negatively impacted across many mental health diagnoses and concerns. Thus, understanding the neural mechanisms of control, as well as how they are disrupted across the transdiagnostic spectrum, are essential topics in neuroscience. I'm particularly interested in three related lines of inquiry: (1) how brain network interactions reconfigure to enable control representations, (2) the representational geometry(ies) that efficiently enable the computationally varied functions of control, and (3) neural dynamics across multiple timescales.

network neuroscience

The brain is a remarkably networked system. This enables the propagation of information as well as the transformation of that information. Advances in network neuroscience have demonstrated that properties exhibited by the brain's network architecture are linked with cognition and behavior. However, important questions remain about the mechanisms carried by network interactions. For example: (1) How is information transformed by network interactions? (2) Do brain connections merely relay information, or do they enact an essential computation? (3) Is this computation linear or nonlinear? These are just some of the questions I'm excited about.

organizing principles

The brain exhibits highly localized computations (e.g., visual category selectivity) as well as a functionally-relevant distributed organization (e.g., large-scale network topography). However, the brain is one unitary system, so across neuroscience, it is fundamental to ask: (1) What organizing principles enable a local-to-distributed continuum in information processing? (2) What computational demands does this organization help resolve? (3) Are there energetic costs/benefits to a system organized in this manner? (4) To what extent are local and distributed processes linked? (5) What are the brain mechanisms that link them?

multimodal tools

The integrative use of neuroimaging and modulation technologies is at the frontier of both basic neuroscience research and clinical application. For example, the combined use of fMRI and TMS has the potential to greatly improve causal inference about information processing over brain network interactions. Additionally, this same approach has already shown great promise as a clinical treatment for some individuals who have not responded to traditional treatments, such as those diagnosed with depression.

computational neuropsychiatry

The tools and insights of computational neuroscience have greatly advanced our understanding of neurocognition. Moving forward, it will be important to apply a computational framework to neuropsychiatric questions. This will critically enable us to discover previously intractable neural mechanisms that underlie mental illnesses, and then use that knowledge to develop better diagnostics and treatment.

neurogenomics

The burgeoning field of neurogenomics aims to address neuroscientific questions with functional genomic and neuroimaging data. This approach is particularly promising for understanding individual differences, which may be under genetic control. For example, recent work has shown that network topography varies across individuals. Building on this, others have shown that this variation is heritable. I am interested in building on this further by asking whether individual differences in dynamic network reconfiguration(s) are also heritable? And relatedly, does this link with the heterogeneous expression of clinical phenotypes?