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

Recent Publications

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. 30(9):1748-1754. Abstract2020_riddle_cb.pdf

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. Abstract2020_peters.pdf

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. Journal of Neuroscience. 40(25):4945-4953. Abstract2020_riddle_jn.pdf

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.

Lorenc, ES, Vandenbroucke ARE, Nee DE, de Lange FP, D'Esposito M.  2020.  Dissociable neural mechanisms underlie currently-relevant, future-relevant, and discarded working memory representations., 2020 Jul 08. Scientific Reports. 10(1):11195. Abstract2020_lorenc.pdf

In daily life, we use visual working memory (WM) to guide our actions. While attending to currently-relevant information, we must simultaneously maintain future-relevant information, and discard information that is no longer relevant. However, the neural mechanisms by which unattended, but future-relevant, information is maintained in working memory, and future-irrelevant information is discarded, are not well understood. Here, we investigated representations of these different information types, using functional magnetic resonance imaging in combination with multivoxel pattern analysis and computational modeling based on inverted encoding model simulations. We found that currently-relevant WM information in the focus of attention was maintained through representations in visual, parietal and posterior frontal brain regions, whereas deliberate forgetting led to suppression of the discarded representations in early visual cortex. In contrast, future-relevant information was neither inhibited nor actively maintained in these areas. These findings suggest that different neural mechanisms underlie the WM representation of currently- and future-relevant information, as compared to information that is discarded from WM.

Eichenbaum, A, Scimeca JM, D'Esposito M.  2020.  Dissociable Neural Systems Support the Learning and Transfer of Hierarchical Control Structure., 2020 Jul 20. Journal of Neuroscience. 40(34):6624-6637. Abstract2020_eichenbaum.pdf

Humans can draw insight from previous experiences in order to quickly adapt to novel environments that share a common underlying structure. Here we combine functional imaging and computational modeling to identify the neural systems that support the discovery and transfer of hierarchical task structure. Human subjects (male and female) completed multiple blocks of a reinforcement learning task that contained a global hierarchical structure governing stimulus-response action mapping. First, behavioral and computational evidence showed that humans successfully discover and transfer the hierarchical rule structure embedded within the task. Next, analysis of fMRI BOLD data revealed activity across a frontal-parietal network that was specifically associated with the discovery of this embedded structure. Finally, activity throughout a cingulo-opercular network supported the transfer and implementation of this discovered structure. Together, these results reveal a division of labor in which dissociable neural systems support the learning and transfer of abstract control structures.A fundamental and defining feature of human behavior is the ability to generalize knowledge from the past in order to support future action. Although the neural circuits underlying more direct forms of learning have been well established over the last century, we still lack a solid framework from which to investigate more abstract, higher order human learning and knowledge generalization. We designed a novel behavioral paradigm in order to specifically isolate a learning process in which previous knowledge, rather than directly indicating the correct action, instead guides the search for the correct action. Moreover, we identify that this learning process is achieved via the coordinated and temporally specific activity of two prominent cognitive control brain networks.