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Background Team leadership during medical emergencies like cardiac arrest resuscitation is cognitively demanding, especially for trainees. These cognitive processes remain poorly characterized due to measurement challenges. Using virtual reality simulation, this study aimed to elucidate and compare communication and cognitive processes-such as decision-making, cognitive load, perceived pitfalls, and strategies-between expert and novice code team leaders to inform strategies for accelerating proficiency development. Methods A simulation-based mixed methods approach was utilized within a single large academic medical center, involving twelve standardized virtual reality cardiac arrest simulations. These 10- to 15-minutes simulation sessions were performed by seven experts and five novices. Following the simulations, a cognitive task analysis was conducted using a cued-recall protocol to identify the challenges, decision-making processes, and cognitive load experienced across the seven stages of each simulation. Results The analysis revealed 250 unique cognitive processes. In terms of reasoning patterns, experts used inductive reasoning, while novices tended to use deductive reasoning, considering treatments before assessments. Experts also demonstrated earlier consideration of potential reversible causes of cardiac arrest. Regarding team communication, experts reported more critical communications, with no shared subthemes between groups. Experts identified more teamwork pitfalls, and suggested more strategies compared to novices. For cognitive load, experts reported lower median cognitive load (53) compared to novices (80) across all stages, with the exception of the initial presentation phase. Conclusions The identified patterns of expert performance — superior teamwork skills, inductive clinical reasoning, and distributed cognitive strategiesn — can inform training programs aimed at accelerating expertise development. 

This study introduces AutoCLC, an AI-powered system designed to assess and provide feedback on closed-loop communication (CLC) in professional learning environments. CLC, where a sender’s Call-Out statement is acknowledged by the receiver’s Check-Back statement, is a critical safety protocol in high-reliability domains, including emergency medicine resuscitation teams. Existing methods for evaluating CLC lack quantifiable metrics and depend heavily on human observation. AutoCLC addresses these limitations by leveraging natural language processing and large language models to analyze audio recordings from Advanced Cardiovascular Life Support (ACLS) simulation training. The system identifies CLC instances, measures their frequency and rate per minute, and categorizes communications as effective, incomplete, or missed. Technical evaluations demonstrate AutoCLC achieves 78.9% precision for identifying Call-Outs and 74.3% for Check-Backs, with a performance gap of only 5% compared to human annotations. A user study involving 11 cardiac arrest instructors across three training sites supported the need for automated CLC assessment. Instructors found AutoCLC reports valuable for quantifying CLC frequency and quality, as well as for providing actionable, example-based feedback. Participants rated AutoCLC highly, with a System Usability Scale score of 76.4%, reflecting above-average usability. This work represents a significant step toward developing scalable, data-driven feedback systems that enhance individual skills and team performance in high-reliability settings. 

Simulation-based learning has become a cornerstone of healthcare education, fostering essential skills like communication, teamwork or decision-making in safe, controlled environments. However, participants’ reflection on simulations often rely on subjective recollections, limiting their effectiveness in promoting learning. This symposium explores how multimodal analytics and AI can enhance simulation-based education by automating teamwork analysis data, providing structured feedback, and supporting reflective practices. The papers examine real-time analytics for closed-loop communication in cardiac arrest simulations, multimodal data use to refine feedback in ICU nursing simulations, generative AI-powered chatbots facilitating nursing students' interpretation of multimodal learning analytics dashboards, and culturally sensitive, AI-based scenarios for Breaking Bad News in an Indian context. Collectively, these contributions highlight the transformative potential of using data and AI-enhanced solutions, emphasizing personalization, cultural sensitivity, and human-centered design, and invite dialogue on the pedagogical, technological and ethical implications of introducing data-based practices and AI-based tools in medical education. 

This paper introduces iREACT, a novel VR simulation addressing key limitations in traditional cardiac arrest (CA) training. Conventional methods struggle to replicate the dynamic nature of real CA events, hindering Crew Resource Management (CRM) skill development. iREACT provides a non-linear, collaborative environment where teams respond to changing patient states, mirroring real CA complexities. By capturing multi-modal data (user actions, cognitive load, visual gaze) and offering real-time and post-session feedback, iREACT enhances CRM assessment beyond traditional methods. A formative evaluation with medical experts underscores its usability and educational value, with potential applications in other high-stakes training scenarios to improve teamwork, communication, and decision-making. 

Our ability to overcome habitual responses in favor of goal-driven novel responses depends on frontoparietal cognitive control networks (CCNs). Recent and ongoing work is revealing the brain network and information processes that allow CCNs to generate cognitive flexibility. First, working memory processes necessary for flexible maintenance and manipulation of goal-relevant representations were recently found to depend on short-term network plasticity (in contrast to persistent activity) within CCN regions. Second, compositional (i.e. abstract and reusable) rule representations maintained within CCNs have been found to reroute network activity flows from stimulus to response, enabling flexible behavior. Together, these findings suggest cognitive flexibility is enhanced by CCN-coordinated network mechanisms, utilizing compositional reuse of neural representations and network flows to flexibly accomplish task goals. 

Representational geometry and connectivity-based studies offer complementary insights into neural information processing, but it is unclear how representations and networks interact to generate neural information. Using a multi-task fMRI dataset, we investigate the role of intrinsic connectivity in shaping diverse representational geometries across the human cortex. Activity flow modeling, which generates neural activity based on connectivity-weighted propagation from other regions, successfully recreated similarity structure and a compression-then-expansion pattern of task representation dimensionality. We introduce a novel measure, convergence, quantifying the degree to which connectivity converges onto target regions. As hypothesized, convergence corresponded with compression of representations and helped explain the observed compression-then-expansion pattern of task representation dimensionality along the cortical hierarchy. These results underscore the generative role of intrinsic connectivity in sculpting representational geometries and suggest that structured connectivity properties, such as convergence, contribute to representational transformations. By bridging representational geometry and connectivity-based frameworks, this work offers a more unified understanding of neural information processing and the computational relevance of brain architecture. 

A central goal of neuroscience is to understand how function-relevant brain activations are generated. Here we test the hypothesis that function-relevant brain activations are generated primarily by distributed network flows. We focused on visual processing in human cortex, given the long-standing literature supporting the functional relevance of brain activations in visual cortex regions exhibiting visual category selectivity. We began by using fMRI data from N = 352 human participants to identify category-specific responses in visual cortex for images of faces, places, body parts, and tools. We then systematically tested the hypothesis that distributed network flows can generate these localized visual category selective responses. This was accomplished using a recently developed approach for simulating – in a highly empirically constrained manner – the generation of task-evoked brain activations by modeling activity flowing over intrinsic brain connections. We next tested refinements to our hypothesis, focusing on how stimulus-driven network interactions initialized in V1 generate downstream visual category selectivity. We found evidence that network flows directly from V1 were sufficient for generating visual category selectivity, but that additional, globally distributed (whole-cortex) network flows increased category selectivity further. Using null network architectures we also found that each region’s unique intrinsic “connectivity fingerprint” was key to the generation of category selectivity. These results generalized across regions associated with all four visual categories tested (bodies, faces, places, and tools), and provide evidence that the human brain’s intrinsic network organization plays a prominent role in the generation of functionally relevant, localized responses. 

Abstract Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which, in turn, modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (n = 149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects’ cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system. 

This NSF-funded study aims to develop and evaluate a novel debriefing system that aims to capture and visualize multimodal data streams from multi-user VR environment that evaluate learners’ cognitive (clinical decision-making) and behavioral (situational awareness, communication) processes to provide data-informed feedback focused on improving team-based care of patients who suffer sudden medical emergencies. Through this new multimodal debriefing system, instructors will be able to provide personalized feedback to clinicians during post-simulation debriefing sessions.