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

Recent Publications

Newton, M, Cookson SL, D'Esposito M, Kayser A.  2022.  Connectivity-Defined Subdivisions of the Intraparietal Sulcus Respond Differentially to Abstraction during Decision Making., 2022 Aug 29. The Journal of neuroscience : the official journal of the Society for Neuroscience. Abstract

The intraparietal sulcus (IPS) has been implicated in numerous functions that range from representation of visual stimuli to action planning, but its role in abstract decision-making has been unclear, in part because low-level functions often act as confounds. Here, we address this problem using a task that dissociates abstract decision-making from sensory salience, attentional control, motor planning, and motor output. Functional MRI data were collected from healthy female and male human subjects while they performed a policy abstraction task requiring use of a more abstract (second-order) rule to select between two less abstract (first order) rules that informed the motor response. By identifying IPS subdivisions with preferential connectivity to prefrontal regions that are differentially responsive to task abstraction, we found that a caudal IPS (cIPS) subregion with strongest connectivity to the pre-premotor cortex was preferentially active for second-order cues, whereas a rostral IPS subregion (rIPS) with strongest connectivity to the dorsal premotor cortex was active during attentional control over first-order cues. These effects for abstraction were seen in addition to cIPS activity that was specific to sensory salience, and rIPS activity that was specific to motor output. Notably, topographic responses to the second-order cue were detected along the caudal-rostral axis of IPS, mirroring the broader organization seen in lateral prefrontal cortex (Badre and D'Esposito, 2007). Together, these data demonstrate that subregions within IPS exhibit activity responsive to policy abstraction, and they suggest that IPS may be organized into frontoparietal subnetworks that support hierarchical cognitive control.Abstract decision-making allows us to flexibly adapt our behavior to new contexts. Although much previous work has focused on the role of lateral prefrontal cortex in such decisions, the contributions of parietal cortex have been relatively understudied. Here, we demonstrate that spatially segregated subregions of human IPS with strong functional connections to lateral prefrontal cortex demonstrate activity selective for abstract decisions. This activity can be distinguished from responses because of cognitive processes related to sensory salience, attentional control, motor planning, and movement. Together, these findings indicate that different task demands are reflected in the topography of IPS, and they explicitly reveal a role in abstract decision-making.

Miller, JA, Tambini A, Kiyonaga A, D'Esposito M.  2022.  Long-term learning transforms prefrontal cortex representations during working memory., 2022 Oct 07. Neuron. Abstract

The role of the lateral prefrontal cortex (lPFC) in working memory (WM) is debated. Non-human primate (NHP) electrophysiology shows that the lPFC stores WM representations, but human neuroimaging suggests that the lPFC controls WM content in sensory cortices. These accounts are confounded by differences in task training and stimulus exposure. We tested whether long-term training alters lPFC function by densely sampling WM activity using functional MRI. Over 3 months, participants trained on both a WM and serial reaction time (SRT) task, wherein fractal stimuli were embedded within sequences. WM performance improved for trained (but not novel) fractals and, neurally, delay activity increased in distributed lPFC voxels across learning. Item-level WM representations became detectable within lPFC patterns, and lPFC activity reflected sequence relationships from the SRT task. These findings demonstrate that human lPFC develops stimulus-selective responses with learning, and WM representations are shaped by long-term experience, which could reconcile competing accounts of WM functioning.

Cookson, SL, D'Esposito M.  2022.  Evaluating the reliability, validity, and utility of overlapping networks: Implications for network theories of cognition., 2022 Nov 01. Human brain mapping. Abstract

Brain network definitions typically assume nonoverlap or minimal overlap, ignoring regions' connections to multiple networks. However, new methods are emerging that emphasize network overlap. Here, we investigated the reliability and validity of one assignment method, the mixed membership algorithm, and explored its potential utility for identifying gaps in existing network models of cognition. We first assessed between-sample reliability of overlapping assignments with a split-half design; a bootstrapped Dice similarity analysis demonstrated good agreement between the networks from the two subgroups. Next, we assessed whether overlapping networks captured expected nonoverlapping topographies; overlapping networks captured portions of one to three nonoverlapping topographies, which aligned with canonical network definitions. Following this, a relative entropy analysis showed that a majority of regions participated in more than one network, as is seen biologically, and many regions did not show preferential connection to any one network. Finally, we explored overlapping network membership in regions of the dual-networks model of cognitive control, showing that almost every region was a member of multiple networks. Thus, the mixed membership algorithm produces consistent and biologically plausible networks, which presumably will allow for the development of more complete network models of cognition.

Cookson, SL, D'Esposito M.  2022.  Evaluating the reliability, validity, and utility of overlapping networks: Implications for network theories of cognition., 2022 Nov 01. Human brain mapping. Abstract

Brain network definitions typically assume nonoverlap or minimal overlap, ignoring regions' connections to multiple networks. However, new methods are emerging that emphasize network overlap. Here, we investigated the reliability and validity of one assignment method, the mixed membership algorithm, and explored its potential utility for identifying gaps in existing network models of cognition. We first assessed between-sample reliability of overlapping assignments with a split-half design; a bootstrapped Dice similarity analysis demonstrated good agreement between the networks from the two subgroups. Next, we assessed whether overlapping networks captured expected nonoverlapping topographies; overlapping networks captured portions of one to three nonoverlapping topographies, which aligned with canonical network definitions. Following this, a relative entropy analysis showed that a majority of regions participated in more than one network, as is seen biologically, and many regions did not show preferential connection to any one network. Finally, we explored overlapping network membership in regions of the dual-networks model of cognitive control, showing that almost every region was a member of multiple networks. Thus, the mixed membership algorithm produces consistent and biologically plausible networks, which presumably will allow for the development of more complete network models of cognition.

Riddle, J, Scimeca JM, Pagnotta MF, Inglis B, Sheltraw D, Muse-Fisher C, D'Esposito M.  2022.  A guide for concurrent TMS-fMRI to investigate functional brain networks., 2022. Frontiers in human neuroscience. 16:1050605. Abstract

Transcranial Magnetic Stimulation (TMS) allows for the direct activation of neurons in the human neocortex and has proven to be fundamental for causal hypothesis testing in cognitive neuroscience. By administering TMS concurrently with functional Magnetic Resonance Imaging (fMRI), the effect of cortical TMS on activity in distant cortical and subcortical structures can be quantified by varying the levels of TMS output intensity. However, TMS generates significant fluctuations in the fMRI time series, and their complex interaction warrants caution before interpreting findings. We present the methodological challenges of concurrent TMS-fMRI and a guide to minimize induced artifacts in experimental design and post-processing. Our study targeted two frontal-striatal circuits: primary motor cortex (M1) projections to the putamen and lateral prefrontal cortex (PFC) projections to the caudate in healthy human participants. We found that TMS parametrically increased the BOLD signal in the targeted region and subcortical projections as a function of stimulation intensity. Together, this work provides practical steps to overcome common challenges with concurrent TMS-fMRI and demonstrates how TMS-fMRI can be used to investigate functional brain networks.