The D'Esposito Lab is a cognitive neuroscience research laboratory within the
Helen Wills Neuroscience Institute
and the Department of Psychology.

Recent Publications

Toker, D, Sommer FT, D'Esposito M.  2020.  A simple method for detecting chaos in nature., 2020. Communications biology. 3:11. Abstract

Chaos, or exponential sensitivity to small perturbations, appears everywhere in nature. Moreover, chaos is predicted to play diverse functional roles in living systems. A method for detecting chaos from empirical measurements should therefore be a key component of the biologist's toolkit. But, classic chaos-detection tools are highly sensitive to measurement noise and break down for common edge cases, making it difficult to detect chaos in domains, like biology, where measurements are noisy. However, newer tools promise to overcome these limitations. Here, we combine several such tools into an automated processing pipeline, and show that our pipeline can detect the presence (or absence) of chaos in noisy recordings, even for difficult edge cases. As a first-pass application of our pipeline, we show that heart rate variability is not chaotic as some have proposed, and instead reflects a stochastic process in both health and disease. Our tool is easy-to-use and freely available.

Kimbrough, A, Lurie DJ, Collazo A, Kreifeldt M, Sidhu H, Macedo GC, D'Esposito M, Contet C, George O.  2020.  Brain-wide functional architecture remodeling by alcohol dependence and abstinence., 2020 Jan 14. Proceedings of the National Academy of Sciences of the United States of America. Abstract

Alcohol abuse and alcohol dependence are key factors in the development of alcohol use disorder, which is a pervasive societal problem with substantial economic, medical, and psychiatric consequences. Although our understanding of the neurocircuitry that underlies alcohol use has improved, novel brain regions that are involved in alcohol use and novel biomarkers of alcohol use need to be identified. The present study used a single-cell whole-brain imaging approach to 1) assess whether abstinence from alcohol in an animal model of alcohol dependence alters the functional architecture of brain activity and modularity, 2) validate our current knowledge of the neurocircuitry of alcohol abstinence, and 3) discover brain regions that may be involved in alcohol use. Alcohol abstinence resulted in the whole-brain reorganization of functional architecture in mice and a pronounced decrease in modularity that was not observed in nondependent moderate drinkers. Structuring of the alcohol abstinence network revealed three major brain modules: 1) extended amygdala module, 2) midbrain striatal module, and 3) cortico-hippocampo-thalamic module, reminiscent of the three-stage theory. Many hub brain regions that control this network were identified, including several that have been previously overlooked in alcohol research. These results identify brain targets for future research and demonstrate that alcohol use and dependence remodel brain-wide functional architecture to decrease modularity. Further studies are needed to determine whether the changes in coactivation and modularity that are associated with alcohol abstinence are causal features of alcohol dependence or a consequence of excessive drinking and alcohol exposure.

Riddle, J, Scimeca JM, Cellier D, Dhanani S, D'Esposito M.  2020.  Causal Evidence for a Role of Theta and Alpha Oscillations in the Control of Working Memory., 2020 Apr 06. Current biology : CB. Abstract

Working memory (WM) relies on the prioritization of relevant information and suppression of irrelevant information [1, 2]. Prioritizing relevant information has been linked to theta frequency neural oscillations in lateral prefrontal cortex and suppressing irrelevant information has been linked to alpha oscillations in occipito-parietal cortex [3,11]. Here, we used a retrospective-cue WM paradigm to manipulate prioritization and suppression task demands designed to drive theta oscillations in prefrontal cortex and alpha oscillations in parietal cortex, respectively. To causally test the role of these neural oscillations, we applied rhythmic transcranial magnetic stimulation (TMS) in either theta or alpha frequency to prefrontal and parietal regions identified using functional MRI. The effect of rhythmic TMS on WM performance was dependent on whether the TMS frequency matched or mismatched the expected underlying task-driven oscillations of the targeted region. Functional MRI in the targeted regions predicted subsequent TMS effects across subjects supporting a model by which theta oscillations are excitatory to neural activity, and alpha oscillations are inhibitory. Together, these results causally establish dissociable roles for prefrontal theta oscillations and parietal alpha oscillations in the control of internally maintained WM representations.

Peters, J, D'Esposito M.  2020.  The drift diffusion model as the choice rule in inter-temporal and risky choice: A┬ácase study in medial orbitofrontal cortex lesion patients and controls., 2020 Apr 20. PLoS computational biology. 16(4):e1007615. Abstract

Sequential sampling models such as the drift diffusion model (DDM) have a long tradition in research on perceptual decision-making, but mounting evidence suggests that these models can account for response time (RT) distributions that arise during reinforcement learning and value-based decision-making. Building on this previous work, we implemented the DDM as the choice rule in inter-temporal choice (temporal discounting) and risky choice (probability discounting) using hierarchical Bayesian parameter estimation. We validated our approach in data from nine patients with focal lesions to the ventromedial prefrontal cortex / medial orbitofrontal cortex (vmPFC/mOFC) and nineteen age- and education-matched controls. Model comparison revealed that, for both tasks, the data were best accounted for by a variant of the drift diffusion model including a non-linear mapping from value-differences to trial-wise drift rates. Posterior predictive checks confirmed that this model provided a superior account of the relationship between value and RT. We then applied this modeling framework and 1) reproduced our previous results regarding temporal discounting in vmPFC/mOFC patients and 2) showed in a previously unpublished data set on risky choice that vmPFC/mOFC patients exhibit increased risk-taking relative to controls. Analyses of DDM parameters revealed that patients showed substantially increased non-decision times and reduced response caution during risky choice. In contrast, vmPFC/mOFC damage abolished neither scaling nor asymptote of the drift rate. Relatively intact value processing was also confirmed using DDM mixture models, which revealed that in both groups >98% of trials were better accounted for by a DDM with value modulation than by a null model without value modulation. Our results highlight that novel insights can be gained from applying sequential sampling models in studies of inter-temporal and risky decision-making in cognitive neuroscience.

Riddle, J, Vogelsang DA, Hwang K, Cellier D, D'Esposito M.  2020.  Distinct oscillatory dynamics underlie different components of hierarchical cognitive control., 2020 May 19. The Journal of neuroscience : the official journal of the Society for Neuroscience. Abstract

Hierarchical cognitive control enables us to execute actions guided by abstract goals. Previous research has suggested that neuronal oscillations at different frequency bands are associated with top-down cognitive control, however, whether distinct neural oscillations have similar or different functions for cognitive control is not well understood. The aim of the current study was to investigate the oscillatory neuronal mechanisms underlying two distinct components of hierarchical cognitive control: the level of abstraction of a rule, and the number of rules that must be maintained (set-size). We collected electroencephalography (EEG) data in 31 men and women who performed a hierarchical cognitive control task that varied in levels of abstraction and set-size. Results from time-frequency analysis in frontal electrodes showed an increase in theta amplitude for increased set-size, whereas an increase in delta was associated with increased abstraction. Both theta and delta amplitude correlated with behavioral performance in the tasks but in an opposite manner: theta correlated with response time slowing when the number of rules increased whereas delta correlated with response time when rules became more abstract. Phase amplitude coupling analysis revealed that delta phase coupled with beta amplitude during conditions with a higher level of abstraction, whereby beta band may potentially represent motor output that was guided by the delta phase. These results suggest that distinct neural oscillatory mechanisms underlie different components of hierarchical cognitive control.Cognitive control allows us to perform immediate actions while maintaining more abstract, overarching goals in mind and to choose between competing actions. We found distinct oscillatory signatures that correspond to two different components of hierarchical control: the level of abstraction of a rule and the number of rules in competition. An increase in the level of abstraction was associated with delta oscillations, whereas theta oscillations were observed when the number of rules increased. Oscillatory amplitude correlated with behavioral performance in the task. Finally, the expression of beta amplitude was coordinated via the phase of delta oscillations, and theta phase coupled with gamma amplitude. These results suggest that distinct neural oscillatory mechanisms underlie different components of hierarchical cognitive control.