Fusing multiple neuroimaging modalities to assess group differences in perception-action coupling.

In the last few decades, non-invasive neuroimaging has revealed macro-scale brain dynamics that underlie perception, cognition and action. Advances in non-invasive neuroimaging target two capabilities; 1) increased spatial and temporal resolution of measured neural activity, and 2) innovative methodologies to extract brain-behavior relationships from evolving neuroimaging technology. We target the second. Our novel methodology integrated three neuroimaging methodologies and elucidated expertise-dependent differences in functional (fused EEG-fMRI) and structural (dMRI) brain networks for a perception-action coupling task. A set of baseball players and controls performed a Go/No-Go task designed to mimic the situation of hitting a baseball. In the functional analysis, our novel fusion methodology identifies 50ms windows with predictive EEG neural correlates of expertise and fuses these temporal windows with fMRI activity in a whole-brain 2mm voxel analysis, revealing time-localized correlations of expertise at a spatial scale of millimeters. The spatiotemporal cascade of brain activity reflecting expertise differences begins as early as 200ms after the pitch starts and lasting up to 700ms afterwards. Network differences are spatially localized to include motor and visual processing areas, providing evidence for differences in perception-action coupling between the groups. Furthermore, an analysis of structural connectivity revealed that the players have significantly more connections between cerebellar and left frontal/motor regions, and many of the functional activation differences between the groups are located within structurally defined network modules that differentiate expertise. In short, our novel method illustrates how multimodal neuroimaging can provide specific macro-scale insights into the functional and structural correlates of expertise development.

Brain dynamics of post-task resting state are influenced by expertise: Insights from baseball players.

Post-task resting state dynamics can be viewed as a task-driven state where behavioral performance is improved through endogenous, non-explicit learning. Tasks that have intrinsic value for individuals are hypothesized to produce post-task resting state dynamics that promote learning. We measured simultaneous fMRI/EEG and DTI in Division-1 collegiate baseball players and compared to a group of controls, examining differences in both functional and structural connectivity. Participants performed a surrogate baseball pitch Go/No-Go task before a resting state scan, and we compared post-task resting state connectivity using a seed-based analysis from the supplementary motor area (SMA), an area whose activity discriminated players and controls in our previous results using this task. Although both groups were equally trained on the task, the experts showed differential activity in their post-task resting state consistent with motor learning. Specifically, we found (1) differences in bilateral SMA-L Insula functional connectivity between experts and controls that may reflect group differences in motor learning, (2) differences in BOLD-alpha oscillation correlations between groups suggests variability in modulatory attention in the post-task state, and (3) group differences between BOLD-beta oscillations that may indicate cognitive processing of motor inhibition. Structural connectivity analysis identified group differences in portions of the functionally derived network, suggesting that functional differences may also partially arise from variability in the underlying white matter pathways. Generally, we find that brain dynamics in the post-task resting state differ as a function of subject expertise and potentially result from differences in both functional and structural connectivity. Hum Brain Mapp 37:4454-4471, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Knowing when not to swing: EEG evidence that enhanced perception-action coupling underlies baseball batter expertise.

Given a decision that requires less than half a second for evaluating the characteristics of the incoming pitch and generating a motor response, hitting a baseball potentially requires unique perception-action coupling to achieve high performance. We designed a rapid perceptual decision-making experiment modeled as a Go/No-Go task yet tailored to reflect a real scenario confronted by a baseball hitter. For groups of experts (Division I baseball players) and novices (non-players), we recorded electroencephalography (EEG) while they performed the task. We analyzed evoked EEG single-trial variability, contingent negative variation (CNV), and pre-stimulus alpha power with respect to the expert vs. novice groups. We found strong evidence for differences in inhibitory processes between the two groups, specifically differential activity in supplementary motor areas (SMA), indicative of enhanced inhibitory control in the expert (baseball player) group. We also found selective activity in the fusiform gyrus (FG) and orbital gyrus in the expert group, suggesting an enhanced perception-action coupling in baseball players that differentiates them from matched controls. In sum, our results show that EEG correlates of decision formation can be used to identify neural markers of high-performance athletes.

Pre-stimulus functional networks modulate task performance in time-pressured evidence gathering and decision-making.

Rapid perceptual decision-making is believed to depend upon efficient allocation of neural resources to the processing of transient stimuli within task-relevant contexts. Given decision-making under severe time pressure, it is reasonable to posit that the brain configures itself, prior to processing stimulus information, in a way that depends upon prior beliefs and/or anticipation. However, relatively little is known about such configuration processes, how they might be manifested in the human brain, or ultimately how they mediate task performance. Here we show that network configuration, defined via pre-stimulus functional connectivity measures estimated from functional magnetic resonance imaging (fMRI) data, is predictive of performance in a time-pressured Go/No-Go task. Specifically, using connectivity measures to summarize network properties, we show that pre-stimulus brain state can be used to discriminate behaviorally correct and incorrect trials, as well as behaviorally correct commission and omission trial categories. More broadly, our results show that pre-stimulus functional configurations of cortical and sub-cortical networks can be a major determiner of task performance.

Musical experts recruit action-related neural structures in harmonic anomaly detection: evidence for embodied cognition in expertise.

Humans are extremely good at detecting anomalies in sensory input. For example, while listening to a piece of Western-style music, an anomalous key change or an out-of-key pitch is readily apparent, even to the non-musician. In this paper we investigate differences between musical experts and non-experts during musical anomaly detection. Specifically, we analyzed the electroencephalograms (EEG) of five expert cello players and five non-musicians while they listened to excerpts of J.S. Bach's Prelude from Cello Suite No. 1. All subjects were familiar with the piece, though experts also had extensive experience playing the piece. Subjects were told that anomalous musical events (AMEs) could occur at random within the excerpts of the piece and were told to report the number of AMEs after each excerpt. Furthermore, subjects were instructed to remain still while listening to the excerpts and their lack of movement was verified via visual and EEG monitoring. Experts had significantly better behavioral performance (i.e. correctly reporting AME counts) than non-experts, though both groups had mean accuracies greater than 80%. These group differences were also reflected in the EEG correlates of key-change detection post-stimulus, with experts showing more significant, greater magnitude, longer periods of, and earlier peaks in condition-discriminating EEG activity than novices. Using the timing of the maximum discriminating neural correlates, we performed source reconstruction and compared significant differences between cellists and non-musicians. We found significant differences that included a slightly right lateralized motor and frontal source distribution. The right lateralized motor activation is consistent with the cortical representation of the left hand - i.e. the hand a cellist would use, while playing, to generate the anomalous key-changes. In general, these results suggest that sensory anomalies detected by experts may in fact be partially a result of an embodied cognition, with a model of the action for generating the anomaly playing a role in its detection.

Scaling virtual health at the epicentre of coronavirus disease 2019: A case study from NYU Langone Health.

The coronavirus disease 2019 (COVID-19) pandemic has accelerated the drive of health-care delivery towards virtual-care platforms. While the potential of virtual care is significant, there are challenges to the implementation and scalability of virtual care as a platform, and health-care organisations are at risk of building and deploying non-strategic, costly or unsustainable virtual-health systems. In this article, we share the NYU Langone Health enterprise approach to building and scaling an integrated virtual-health platform prior to and during the COVID-19 pandemic, and offer lessons learned and recommendations for health systems that need to undertake or are currently undertaking the transition to virtual-care delivery.

Standardizing Quality of Virtual Urgent Care: Using Standardized Patients in a Unique Experiential Onboarding Program.

Introduction: Virtual urgent care (VUC) provides real-time evaluation, triage, and treatment of low-acuity medical problems; however, VUC physicians have varying levels of telemedicine training. We created a workplace-based experiential onboarding program that deployed standardized patients (SPs) into a VUC clinic to evaluate and deliver feedback to independently practicing physicians, providing quality assurance and identifying areas for improvement. Methods: We simulated evaluation of an adult with upper respiratory symptoms. To replicate a real-life encounter, we developed a mock electronic medical entry with demographic and medical information and scheduled SPs into the clinic's actual patient queue. SPs provided seamless, realistic training within the real-world virtual clinic environment. Using an adapted assessment tool anchored to not done, partly done, or well done, SPs evaluated communication, disease-specific, and telemedicine skills by observing behaviors. We surveyed participants to evaluate the program. Results: Twenty-one physicians participated. All performed well in core communication and disease management domains. Ninety-three percent of behaviors (SD = 11%) were rated well done within the information gathering domain, 90% (SD = 8%) within relationship development, and 95% (SD = 5%) within disease management. Physicians struggled with telemedicine-specific skills-55% (SD = 38%) well done-and education and counseling-32% (SD = 34%) well done-highlighting specific behaviors most ripe for improvement. All queried participants indicated that this simulation improved communication and telemedicine skills. Discussion: This workplace-based experiential onboarding program uncovered knowledge gaps within telemedicine skills and patient education domains. Identification of these gaps can help drive new virtual care curricula.

Technology-Assisted Virtual Consultation for Medical Imaging.

PURPOSE: The aim of this study was to report the investigators' preliminary experience in the implementation of a "virtual consult" (VC) system enabling consultations between radiologists and referring physicians in physically remote locations throughout their enterprise. METHODS: Referrers and radiologists directly access the VC through the electronic medical record and PACS, respectively. Referrers may click a VC link associated with any examination report to instant message the appropriate subspecialist radiologist, who receives an alert allowing automatic loading of the examination. The radiologist and referrer may then discuss the examination via instant messaging as well as launch a real-time screen-share of the radiologist's PACS display, with the option for either participant to control the display. Radiologists' and referrers' feedback was evaluated after the institution's first 110 VC sessions. RESULTS: Referrers' most common specialties were emergency medicine (27.3%) and internal medicine (13.6%); radiologists' most common subspecialties were abdominal (33.6%) and thoracic (16.4%) imaging. Screen-shares lasted on average 12 ± 16 minutes. From 80% to 90% of referrers agreed that the VC was easy to use, improved their understanding of the radiology report, affected patient management, and enhanced radiologists' role. Referrers found the VC to be particularly useful when traditional consultation was difficult because of location or time constraints or when seeking a quick response to a targeted question. Radiologists recognized referrers' positive response to the VC, although they tended to view the VC as disruptive to normal workflow. CONCLUSIONS: The VC addresses a key challenge in the current era of digital radiology practice and provides added value to referrers, though continued radiologists' workflow optimization is warranted.

Experience does not equal expertise in recognizing infrequent incoming gunfire: neural markers for experience and task expertise at peak behavioral performance.

For a soldier, decisions to use force can happen rapidly and sometimes lead to undesired consequences. In many of these situations, there is a rapid assessment by the shooter that recognizes a threat and responds to it with return fire. But the neural processes underlying these rapid decisions are largely unknown, especially amongst those with extensive weapons experience and expertise. In this paper, we investigate differences in weapons experts and non-experts during an incoming gunfire detection task. Specifically, we analyzed the electroencephalography (EEG) of eleven expert marksmen/soldiers and eleven non-experts while they listened to an audio scene consisting of a sequence of incoming and non-incoming gunfire events. Subjects were tasked with identifying each event as quickly as possible and committing their choice via a motor response. Contrary to our hypothesis, experts did not have significantly better behavioral performance or faster response time than novices. Rather, novices indicated trends of better behavioral performance than experts. These group differences were more dramatic in the EEG correlates of incoming gunfire detection. Using machine learning, we found condition-discriminating EEG activity among novices showing greater magnitude and covering longer periods than those found in experts. We also compared group-level source reconstruction on the maximum discriminating neural correlates and found that each group uses different neural structures to perform the task. From condition-discriminating EEG and source localization, we found that experts perceive more categorical overlap between incoming and non-incoming gunfire. Consequently, the experts did not perform as well behaviorally as the novices. We explain these unexpected group differences as a consequence of experience with gunfire not being equivalent to expertise in recognizing incoming gunfire.

Soldiers and marksmen under fire: monitoring performance with neural correlates of small arms fire localization.

Important decisions in the heat of battle occur rapidly and a key aptitude of a good combat soldier is the ability to determine whether he is under fire. This rapid decision requires the soldier to make a judgment in a fraction of a second, based on a barrage of multisensory cues coming from multiple modalities. The present study uses an oddball paradigm to examine listener ability to differentiate shooter locations from audio recordings of small arms fire. More importantly, we address the neural correlates involved in this rapid decision process by employing single-trial analysis of electroencephalography (EEG). In particular, we examine small arms expert listeners as they differentiate the sounds of small arms firing events recorded at different observer positions relative to a shooter. Using signal detection theory, we find clear neural signatures related to shooter firing angle by identifying the times of neural discrimination on a trial-to-trial basis. Similar to previous results in oddball experiments, we find common windows relative to the response and the stimulus when neural activity discriminates between target stimuli (forward fire: observer 0° to firing angle) vs. standards (off-axis fire: observer 90° to firing angle). We also find, using windows of maximum discrimination, that auditory target vs. standard discrimination yields neural sources in Brodmann Area 19 (BA 19), i.e., in the visual cortex. In summary, we show that single-trial analysis of EEG yields informative scalp distributions and source current localization of discriminating activity when the small arms experts discriminate between forward and off-axis fire observer positions. Furthermore, this perceptual decision implicates brain regions involved in visual processing, even though the task is purely auditory. Finally, we utilize these techniques to quantify the level of expertise in these subjects for the chosen task, having implications for human performance monitoring in combat.

You Can't Think and Hit at the Same Time: Neural Correlates of Baseball Pitch Classification.

Hitting a baseball is often described as the most difficult thing to do in sports. A key aptitude of a good hitter is the ability to determine which pitch is coming. This rapid decision requires the batter to make a judgment in a fraction of a second based largely on the trajectory and spin of the ball. When does this decision occur relative to the ball's trajectory and is it possible to identify neural correlates that represent how the decision evolves over a split second? Using single-trial analysis of electroencephalography (EEG) we address this question within the context of subjects discriminating three types of pitches (fastball, curveball, slider) based on pitch trajectories. We find clear neural signatures of pitch classification and, using signal detection theory, we identify the times of discrimination on a trial-to-trial basis. Based on these neural signatures we estimate neural discrimination distributions as a function of the distance the ball is from the plate. We find all three pitches yield unique distributions, namely the timing of the discriminating neural signatures relative to the position of the ball in its trajectory. For instance, fastballs are discriminated at the earliest points in their trajectory, relative to the two other pitches, which is consistent with the need for some constant time to generate and execute the motor plan for the swing (or inhibition of the swing). We also find incorrect discrimination of a pitch (errors) yields neural sources in Brodmann Area 10, which has been implicated in prospective memory, recall, and task difficulty. In summary, we show that single-trial analysis of EEG yields informative distributions of the relative point in a baseball's trajectory when the batter makes a decision on which pitch is coming.

Toward an integrated approach to perception and action: conference report and future directions.

This article was motivated by the conference entitled "Perception & Action - An Interdisciplinary Approach to Cognitive Systems Theory," which took place September 14-16, 2010 at the Santa Fe Institute, NM, USA. The goal of the conference was to bring together an interdisciplinary group of neuroscientists, roboticists, and theorists to discuss the extent and implications of action-perception integration in the brain. The motivation for the conference was the realization that it is a widespread approach in biological, theoretical, and computational neuroscience to investigate sensory and motor function of the brain in isolation from one another, while at the same time, it is generally appreciated that sensory and motor processing cannot be fully separated. Our article summarizes the key findings of the conference, provides a hypothetical model that integrates the major themes and concepts presented at the conference, and concludes with a perspective on future challenges in the field.