Research

I use multimodal cognitive neuroscience methods and biophysical models  of electrophysiological signals to study the neural mechanisms that underlie inhibitory control in humans. ​

My research program aims to fill the gap between behavioral-cognitive theories of inhibitory control and its neural mechanisms, lending a multi-level understanding of this type of control that will better facilitate clinical application in neuropsychiatric disorders marked by inhibitory control deficits (e.g., OCD, ADHD, Parkinson's, schizophrenia, etc.). Inhibitory control of ongoing motor actions is most often studied using the Stop-Signal Task, in which participants initiate responses to "Go" cues, but must attempt to cancel those responses following "Stop" cues (Logan and Cowan, 1984). Despite a lack of a button press during successful Stops, the task allows for estimation of how long stopping requires (Verbruggen et al., 2009). However, squaring the predictions and assumptions of behavioral-cognitive models of this task with neural recordings has challenged the field. I am confident that fruitful approaches for constructing such a cross-level understanding include: testing mechanistically-informed frameworks of stopping (e.g., two-stage models; Diesburg and Wessel, 2021), applying cutting-edge analytic techniques to reveal the true nature of neural signals of control (e.g., spectral events; Diesburg, Greenlee, and Wessel, 2021), and interrogating the plausibility of behavioral-cognitive theory with regard to neural correlates (e.g., "Do Stop-Signal EEG signals align with the horse-race model?"; Diesburg et al., 2024).

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My methodological toolbox has included electroencephalography (EEG), local field potentials (LFP) from deep subcortical and surface cortical regions (i.e., stereotactic and ECog arrays), electromyography (EMG), single-pulse and repetitive transcranial magnetic stimulation (TMS), deep brain stimulation (DBS), and magnetic resonance spectroscopy (MRS). I leverage these approaches to perform observational and "causal" experiments in regions involved in fronto-basal ganglia circuits implicated in action-stopping and motor conflict. 

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During my postdoctoral training, I have leveraged Human Neocortical Neurosolver - a biophysically-realistic model of the canonical neocortical column under exogenous drive (Neymotin et al., 2020) - to predict thalamocortical dynamics that generate human EEG signals associated with inhibitory control. Ongoing and future research will further integrate this tool within my research pipeline through empirical tests of model-based predictions about mechanisms.