Functional neuroimaging as a catalyst for integrated neuroscience.

Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.

Individuals who see the good in the bad engage distinctive default network coordination during post-encoding rest.

Focusing on the upside of negative events often promotes resilience. Yet, the underlying mechanisms that allow some people to spontaneously see the good in the bad remain unclear. The broaden-and-build theory of positive emotion has long suggested that positive affect, including positivity in the face of negative events, is linked to idiosyncratic thought patterns (i.e., atypical cognitive responses). Yet, evidence in support of this view has been limited, in part, due to difficulty in measuring idiosyncratic cognitive processes as they unfold. To overcome this barrier, we applied Inter-Subject Representational Similarity Analysis to test whether and how idiosyncratic neural responding supports positive reactions to negative experience. We found that idiosyncratic functional connectivity patterns in the brain's default network while resting after a negative experience predicts more positive descriptions of the event. This effect persisted when controlling for connectivity 1) before and during the negative experience, 2) before, during, and after a neutral experience, and 3) between other relevant brain regions (i.e., the limbic system). The relationship between idiosyncratic default network responding and positive affect was largely driven by functional connectivity patterns between the ventromedial prefrontal cortex and the rest of the default network and occurred relatively quickly during rest. We identified post-encoding rest as a key moment and the default network as a key brain system in which idiosyncratic responses correspond with seeing the good in the bad.

Brain decoding of spontaneous thought: Predictive modeling of self-relevance and valence using personal narratives.

The contents and dynamics of spontaneous thought are important factors for personality traits and mental health. However, assessing spontaneous thoughts is challenging due to their unconstrained nature, and directing participants' attention to report their thoughts may fundamentally alter them. Here, we aimed to decode two key content dimensions of spontaneous thought-self-relevance and valence-directly from brain activity. To train functional MRI-based predictive models, we used individually generated personal stories as stimuli in a story-reading task to mimic narrative-like spontaneous thoughts (n = 49). We then tested these models on multiple test datasets (total n = 199). The default mode, ventral attention, and frontoparietal networks played key roles in the predictions, with the anterior insula and midcingulate cortex contributing to self-relevance prediction and the left temporoparietal junction and dorsomedial prefrontal cortex contributing to valence prediction. Overall, this study presents brain models of internal thoughts and emotions, highlighting the potential for the brain decoding of spontaneous thought.

Neural unscrambling of temporal information during a nonlinear narrative.

Although we must experience our lives chronologically, storytellers often manipulate the order in which they relay events. How the brain processes temporal information while encoding a nonlinear narrative remains unclear. Here, we use functional magnetic resonance imaging during movie watching to investigate which brain regions are sensitive to information about time in a narrative and test whether the representation of temporal context across a narrative is more influenced by the order in which events are presented or their underlying chronological sequence. Results indicate that medial parietal regions are sensitive to cued jumps through time over and above other changes in context (i.e., location). Moreover, when processing non-chronological narrative information, the precuneus and posterior cingulate engage in on-the-fly temporal unscrambling to represent information chronologically. Specifically, days that are closer together in chronological time are represented more similarly regardless of when they are presented in the movie, and this representation is consistent across participants. Additional analyses reveal a strong spatial signature associated with higher magnitude jumps through time. These findings are consistent with prior theorizing on medial parietal regions as central to maintaining and updating narrative situation models, and suggest the priority of chronological information when encoding narrative events.

Individual differences in neural event segmentation of continuous experiences.

Event segmentation is a spontaneous part of perception, important for processing continuous information and organizing it into memory. Although neural and behavioral event segmentation show a degree of inter-subject consistency, meaningful individual variability exists atop these shared patterns. Here we characterized individual differences in the location of neural event boundaries across four short movies that evoked variable interpretations. Event boundary alignment across subjects followed a posterior-to-anterior gradient that was tightly correlated with the rate of segmentation: slower-segmenting regions that integrate information over longer time periods showed more individual variability in boundary locations. This relationship held irrespective of the stimulus, but the degree to which boundaries in particular regions were shared versus idiosyncratic depended on certain aspects of movie content. Furthermore, this variability was behaviorally significant in that similarity of neural boundary locations during movie-watching predicted similarity in how the movie was ultimately remembered and appraised. In particular, we identified a subset of regions in which neural boundary locations are both aligned with behavioral boundaries during encoding and predictive of stimulus interpretation, suggesting that event segmentation may be a mechanism by which narratives generate variable memories and appraisals of stimuli.

Neural signatures of attentional engagement during narratives and its consequences for event memory.

As we comprehend narratives, our attentional engagement fluctuates over time. Despite theoretical conceptions of narrative engagement as emotion-laden attention, little empirical work has characterized the cognitive and neural processes that comprise subjective engagement in naturalistic contexts or its consequences for memory. Here, we relate fluctuations in narrative engagement to patterns of brain coactivation and test whether neural signatures of engagement predict subsequent memory. In behavioral studies, participants continuously rated how engaged they were as they watched a television episode or listened to a story. Self-reported engagement was synchronized across individuals and driven by the emotional content of the narratives. In functional MRI datasets collected as different individuals watched the same show or listened to the same story, engagement drove neural synchrony, such that default mode network activity was more synchronized across individuals during more engaging moments of the narratives. Furthermore, models based on time-varying functional brain connectivity predicted evolving states of engagement across participants and independent datasets. The functional connections that predicted engagement overlapped with a validated neuromarker of sustained attention and predicted recall of narrative events. Together, our findings characterize the neural signatures of attentional engagement in naturalistic contexts and elucidate relationships among narrative engagement, sustained attention, and event memory.

User-Centered Framework for Implementation of Technology (UFIT): Development of an Integrated Framework for Designing Clinical Decision Support Tools Packaged With Tailored Implementation Strategies.

BACKGROUND: Electronic health record-based clinical decision support (CDS) tools can facilitate the adoption of evidence into practice. Yet, the impact of CDS beyond single-site implementation is often limited by dissemination and implementation barriers related to site- and user-specific variation in workflows and behaviors. The translation of evidence-based CDS from initial development to implementation in heterogeneous environments requires a framework that assures careful balancing of fidelity to core functional elements with adaptations to ensure compatibility with new contexts. OBJECTIVE: This study aims to develop and apply a framework to guide tailoring and implementing CDS across diverse clinical settings. METHODS: In preparation for a multisite trial implementing CDS for pediatric overweight or obesity in primary care, we developed the User-Centered Framework for Implementation of Technology (UFIT), a framework that integrates principles from user-centered design (UCD), human factors/ergonomics theories, and implementation science to guide both CDS adaptation and tailoring of related implementation strategies. Our transdisciplinary study team conducted semistructured interviews with pediatric primary care clinicians and a diverse group of stakeholders from 3 health systems in the northeastern, midwestern, and southeastern United States to inform and apply the framework for our formative evaluation. RESULTS: We conducted 41 qualitative interviews with primary care clinicians (n=21) and other stakeholders (n=20). Our workflow analysis found 3 primary ways in which clinicians interact with the electronic health record during primary care well-child visits identifying opportunities for decision support. Additionally, we identified differences in practice patterns across contexts necessitating a multiprong design approach to support a variety of workflows, user needs, preferences, and implementation strategies. CONCLUSIONS: UFIT integrates theories and guidance from UCD, human factors/ergonomics, and implementation science to promote fit with local contexts for optimal outcomes. The components of UFIT were used to guide the development of Improving Pediatric Obesity Practice Using Prompts, an integrated package comprising CDS for obesity or overweight treatment with tailored implementation strategies. TRIAL REGISTRATION: ClinicalTrials.gov NCT05627011; https://clinicaltrials.gov/study/NCT05627011.

Seeing Social: A Neural Signature for Conscious Perception of Social Interactions.

Social information is some of the most ambiguous content we encounter in our daily lives, yet in experimental contexts, percepts of social interactions-that is, whether an interaction is present and if so, the nature of that interaction-are often dichotomized as correct or incorrect based on experimenter-assigned labels. Here, we investigated the behavioral and neural correlates of subjective (or conscious) social perception using data from the Human Connectome Project in which participants (n = 1049; 486 men, 562 women) viewed animations of geometric shapes during fMRI and indicated whether they perceived a social interaction or random motion. Critically, rather than experimenter-assigned labels, we used observers' own reports of "Social" or "Non-social" to classify percepts and characterize brain activity, including leveraging a particularly ambiguous animation perceived as "Social" by some but "Non-social" by others to control for visual input. Behaviorally, observers were biased toward perceiving information as social (vs non-social); and neurally, observer reports (compared with experimenter labels) explained more variance in activity across much of the brain. Using "Unsure" reports, we identified several regions that responded parametrically to perceived socialness. Neural responses to social versus non-social content diverged early in time and in the cortical hierarchy. Finally, individuals with higher internalizing trait scores showed both a higher response bias toward "Social" and an inverse relationship with activity in default mode and visual association areas while scanning for social information. Findings underscore the subjective nature of social perception and the importance of using observer reports to study percepts of social interactions.SIGNIFICANCE STATEMENT Simple animations involving two or more geometric shapes have been used as a gold standard to understand social cognition and impairments therein. Yet, experimenter-assigned labels of what is social versus non-social are frequently used as a ground truth, despite the fact that percepts of such ambiguous social stimuli are highly subjective. Here, we used behavioral and fMRI data from a large sample of neurotypical individuals to show that participants' responses reveal subtle behavioral biases, help us study neural responses to social content more precisely, and covary with internalizing trait scores. Our findings underscore the subjective nature of social perception and the importance of considering observer reports in studying behavioral and neural dynamics of social perception.

Inter-subject correlation during long narratives reveals widespread neural correlates of reading ability.

Recent work using fMRI inter-subject correlation analysis has provided new information about the brain's response to video and audio narratives, particularly in frontal regions not typically activated by single words. This approach is very well suited to the study of reading, where narrative is central to natural experience. But since past reading paradigms have primarily presented single words or phrases, the influence of narrative on semantic processing in the brain - and how that influence might change with reading ability - remains largely unexplored. In this study, we presented coherent stories to adolescents and young adults with a wide range of reading abilities. The stories were presented in alternating visual and auditory blocks. We used a dimensional inter-subject correlation analysis to identify regions in which better and worse readers had varying levels of consistency with other readers. This analysis identified a widespread set of brain regions in which activity timecourses were more similar among better readers than among worse readers. These differences were not detected with standard block activation analyses. Worse readers had higher correlation with better readers than with other worse readers, suggesting that the worse readers had "idiosyncratic" responses rather than using a single compensatory mechanism. Close inspection confirmed that these differences were not explained by differences in IQ or motion. These results suggest an expansion of the current view of where and how reading ability is reflected in the brain, and in doing so, they establish inter-subject correlation as a sensitive tool for future studies of reading disorders.

Functional connectivity predicts changes in attention observed across minutes, days, and months.

The ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same patterns capture fluctuations in attention within individuals remains unclear. Here, across five independent studies, we demonstrate that the sustained attention connectome-based predictive model (CPM), a validated model of sustained attention function, generalizes to predict attentional state from data collected across minutes, days, weeks, and months. Furthermore, the sustained attention CPM is sensitive to within-subject state changes induced by propofol as well as sevoflurane, such that individuals show functional connectivity signatures of stronger attentional states when awake than when under deep sedation and light anesthesia. Together, these results demonstrate that fluctuations in attentional state reflect variability in the same functional connectivity patterns that predict individual differences in sustained attention.

Beyond fingerprinting: Choosing predictive connectomes over reliable connectomes.

Recent years have seen a surge of research on variability in functional brain connectivity within and between individuals, with encouraging progress toward understanding the consequences of this variability for cognition and behavior. At the same time, well-founded concerns over rigor and reproducibility in psychology and neuroscience have led many to question whether functional connectivity is sufficiently reliable, and call for methods to improve its reliability. The thesis of this opinion piece is that when studying variability in functional connectivity-both across individuals and within individuals over time-we should use behavior prediction as our benchmark rather than optimize reliability for its own sake. We discuss theoretical and empirical evidence to compel this perspective, both when the goal is to study stable, trait-level differences between people, as well as when the goal is to study state-related changes within individuals. We hope that this piece will be useful to the neuroimaging community as we continue efforts to characterize inter- and intra-subject variability in brain function and build predictive models with an eye toward eventual real-world applications.

Movie-watching outperforms rest for functional connectivity-based prediction of behavior.

A major goal of human neuroscience is to relate differences in brain function to differences in behavior across people. Recent work has established that whole-brain functional connectivity patterns are relatively stable within individuals and unique across individuals, and that features of these patterns predict various traits. However, while functional connectivity is most often measured at rest, certain tasks may enhance individual signals and improve sensitivity to behavior differences. Here, we show that compared to the resting state, functional connectivity measured during naturalistic viewing-i.e., movie watching-yields more accurate predictions of trait-like phenotypes in the domains of both cognition and emotion. Traits could be predicted using less than three minutes of data from single video clips, and clips with highly social content gave the most accurate predictions. Results suggest that naturalistic stimuli amplify individual differences in behaviorally relevant brain networks.

Failure Mode and Effect Analysis: Engineering Safer Neurocritical Care Transitions.

BACKGROUND/OBJECTIVE: Inter-hospital patient transfers for neurocritical care are increasingly common due to increased regionalization for acute care, including stroke and intracerebral hemorrhage. This process of transfer is uniquely vulnerable to errors and risk given numerous handoffs involving multiple providers, from several disciplines, located at different institutions. We present failure mode and effect analysis (FMEA) as a systems engineering methodology that can be applied to neurocritical care transitions to reduce failures in communication and improve patient safety. Specifically, we describe our local implementation of FMEA to improve the safety of inter-hospital transfer for patients with intracerebral and subarachnoid hemorrhage as evidence of success. METHODS: We describe the conceptual basis for and specific use-case example for each formal step of the FMEA process. We assembled a multi-disciplinary team, developed a process map of all components required for successful transfer, and identified "failure modes" or errors that hinder completion of each subprocess. A risk or hazard analysis was conducted for each failure mode, and ones of highest impact on patient safety and outcomes were identified and prioritized for implementation. Interventions were then developed and implemented into an action plan to redesign the process. Importantly, a comprehensive evaluation method was established to monitor outcomes and reimplement interventions to provide for continual improvement. RESULTS: This intervention was associated with significant reductions in emergency department (ED) throughput (ED length of stay from 300 to 149 min, (p < .01), and improvements in inter-disciplinary communication (increase from pre-intervention (10%) to post- (64%) of inter-hospital transfers where the neurological intensive care unit and ED attendings discussed care for the patient prior to their arrival). CONCLUSIONS: Application of the FMEA approach yielded meaningful and sustained process change for patients with neurocritical care needs. Utilization of FMEA as a change instrument for quality improvement is a powerful tool for programs looking to improve timely communication, resource utilization, and ultimately patient safety.

Idiosynchrony: From shared responses to individual differences during naturalistic neuroimaging.

Two ongoing movements in human cognitive neuroscience have researchers shifting focus from group-level inferences to characterizing single subjects, and complementing tightly controlled tasks with rich, dynamic paradigms such as movies and stories. Yet relatively little work combines these two, perhaps because traditional analysis approaches for naturalistic imaging data are geared toward detecting shared responses rather than between-subject variability. Here, we review recent work using naturalistic stimuli to study individual differences, and advance a framework for detecting structure in idiosyncratic patterns of brain activity, or "idiosynchrony". Specifically, we outline the emerging technique of inter-subject representational similarity analysis (IS-RSA), including its theoretical motivation and an empirical demonstration of how it recovers brain-behavior relationships during movie watching using data from the Human Connectome Project. We also consider how stimulus choice may affect the individual signal and discuss areas for future research. We argue that naturalistic neuroimaging paradigms have the potential to reveal meaningful individual differences above and beyond those observed during traditional tasks or at rest.

Untangling the relatedness among correlations, part III: Inter-subject correlation analysis through Bayesian multilevel modeling for naturalistic scanning.

While inter-subject correlation (ISC) analysis is a powerful tool for naturalistic scanning data, drawing appropriate statistical inferences is difficult due to the daunting task of accounting for the intricate relatedness in data structure as well as handling the multiple testing issue. Although the linear mixed-effects (LME) modeling approach (Chen et al., 2017a) is capable of capturing the relatedness in the data and incorporating explanatory variables, there are a few challenging issues: 1) it is difficult to assign accurate degrees of freedom for each testing statistic, 2) multiple testing correction is potentially over-penalizing due to model inefficiency, and 3) thresholding necessitates arbitrary dichotomous decisions. Here we propose a Bayesian multilevel (BML) framework for ISC data analysis that integrates all regions of interest into one model. By loosely constraining the regions through a weakly informative prior, BML dissolves multiplicity through conservatively pooling the effect of each region toward the center and improves collective fitting and overall model performance. In addition to potentially achieving a higher inference efficiency, BML improves spatial specificity and easily allows the investigator to adopt a philosophy of full results reporting. A dataset of naturalistic scanning is utilized to illustrate the modeling approach with 268 parcels and to showcase the modeling capability, flexibility and advantages in results reporting. The associated program will be available as part of the AFNI suite for general use.

Sub-millimeter fMRI reveals multiple topographical digit representations that form action maps in human motor cortex.

The human brain coordinates a wide variety of motor activities. On a large scale, the cortical motor system is topographically organized such that neighboring body parts are represented by neighboring brain areas. This homunculus-like somatotopic organization along the central sulcus has been observed using neuroimaging for large body parts such as the face, hands and feet. However, on a finer scale, invasive electrical stimulation studies show deviations from this somatotopic organization that suggest an organizing principle based on motor actions rather than body part moved. It has not been clear how the action-map organization principle of the motor cortex in the mesoscopic (sub-millimeter) regime integrates into a body map organization principle on a macroscopic scale (cm). Here we developed and applied advanced mesoscopic (sub-millimeter) fMRI and analysis methodology to non-invasively investigate the functional organization topography across columnar and laminar structures in humans. Compared to previous methods, in this study, we could capture locally specific blood volume changes across entire brain regions along the cortical curvature. We find that individual fingers have multiple mirrored representations in the primary motor cortex depending on the movements they are involved in. We find that individual digits have cortical representations up to 3 â€‹mm apart from each other arranged in a column-like fashion. These representations are differentially engaged depending on whether the digits' muscles are used for different motor actions such as flexion movements, like grasping a ball or retraction movements like releasing a ball. This research provides a starting point for non-invasive investigation of mesoscale topography across layers and columns of the human cortex and bridges the gap between invasive electrophysiological investigations and large coverage non-invasive neuroimaging.

Identification of Patients with Nontraumatic Intracranial Hemorrhage Using Administrative Claims Data.

INTRODUCTION: Nontraumatic intracranial hemorrhage (ICH) is a neurological emergency of research interest; however, unlike ischemic stroke, has not been well studied in large datasets due to the lack of an established administrative claims-based definition. We aimed to evaluate both explicit diagnosis codes and machine learning methods to create a claims-based definition for this clinical phenotype. METHODS: We examined all patients admitted to our tertiary medical center with a primary or secondary International Classification of Disease version 9 (ICD-9) or 10 (ICD-10) code for ICH in claims from any portion of the hospitalization in 2014-2015. As a gold standard, we defined the nontraumatic ICH phenotype based on manual chart review. We tested explicit definitions based on ICD-9 and ICD-10 that had been previously published in the literature as well as four machine learning classifiers including support vector machine (SVM), logistic regression with LASSO, random forest and xgboost. We report five standard measures of model performance for each approach. RESULTS: A total of 1830 patients with 2145 unique ICD-10 codes were included in the initial dataset, of which 437 (24%) were true positive based on manual review. The explicit ICD-10 definition performed best (Sensitivity = 0.89 (95% CI 0.85-0.92), Specificity = 0.83 (0.81-0.85), F-score = 0.73 (0.69-0.77)) and improves on an explicit ICD-9 definition (Sensitivity = 0.87 (0.83-0.90), Specificity = 0.77 (0.74-0.79), F-score = 0.67 (0.63-0.71). Among machine learning classifiers, SVM performed best (Sensitivity = 0.78 (0.75-0.82), Specificity = 0.84 (0.81-0.87), AUC = 0.89 (0.87-0.92), F-score = 0.66 (0.62-0.69)). CONCLUSIONS: An explicit ICD-10 definition can be used to accurately identify patients with a nontraumatic ICH phenotype with substantially better performance than ICD-9. An explicit ICD-10 based definition is easier to implement and quantitatively not appreciably improved with the additional application of machine learning classifiers. Future research utilizing large datasets should utilize this definition to address important research gaps.

A Qualitative Study of Risks Related to Interhospital Transfer of Patients with Nontraumatic Intracranial Hemorrhage.

GOAL: Interhospital transfer (IHT) facilitates access to specialized neurocritical care but may also introduce unique risk. Our goal was to describe providers' perceptions of safety threats during IHT for patients with nontraumatic intracranial hemorrhage. MATERIALS AND METHODS: We employed qualitative, semi-structured interviews at an academic medical center receiving critically-ill neurologic transfers, and 5 referring hospitals. Interviewees included physicians, nurses, and allied health professionals with experience caring for patients transferred between hospitals for nontraumatic intracranial hemorrhage. Interviews continued until data saturation was reached. Coding occurred concurrently with interviews. Analysis was inductive, using the constant comparative method. FINDINGS: The predominant impediments to safe, high-quality neurocritical care transitions between hospitals are insufficient communication, gaps in clinical practice, and lack of IHT structure. Insufficient communication highlights the unique communication challenges specific to IHT, which overlay and compound known intrahospital communication barriers. Gaps in clinical practice revolve primarily around the provision of neurocritical care for this patient population, often subject to resource availability, by receiving hospital emergency medicine providers. Lack of structure outlines providers' questions that emerge when institutions fail to identify process channels, expectations, and accountability during complex neurocritical care transitions. CONCLUSIONS: The predominant impediments to safe, high-quality neurocritical care transitions between hospitals are insufficient communication, gaps in clinical practice, and lack of IHT structure. These themes serve as fundamental targets for quality improvement initiatives. To our knowledge, this is the first description of challenges to quality and safety in high-risk neurocritical care transitions through clinicians' voices.

Considering factors affecting the connectome-based identification process: Comment on Waller et al.

A recent study by Waller and colleagues evaluated the reliability, specificity, and generalizability of using functional connectivity data to identify individuals from a group. The authors note they were able to replicate identification rates in a larger version of the original Human Connectome Project (HCP) dataset. However, they also report lower identification accuracies when using historical neuroimaging acquisitions with low spatial and temporal resolution. The authors suggest that their results indicate connectomes derived from historical imaging data may be similar across individuals, to the extent that this connectome-based approach may be inappropriate for precision psychiatry and the goal of drawing inferences based on subject-level data. Here we note that the authors did not take into account factors affecting data quality and hence identification rates, independent of whether a low spatiotemporal resolution acquisition or a high spatiotemporal resolution acquisition is used. Specifically, we show here that the amount of data collected per subject and in-scanner motion are the predominant factors influencing identification rates, not the spatiotemporal resolution of the acquisition. To do this, we investigated identification rates in the HCP dataset as a function of the amount of data and motion. Using a dataset from the Consortium for Reliability and Reproducibility (CoRR), we investigated the impact of multiband versus non-multiband imaging parameters; that is, high spatiotemporal resolution versus low spatiotemporal resolution acquisitions. We show scan length and motion affect identification, whereas the imaging protocol does not affect these rates. Our results suggest that motion and amount of data per subject are the primary factors impacting individual connectivity profiles, but that within these constraints, individual differences in the connectome are readily observable.

Emergency department boarding and adverse hospitalization outcomes among patients admitted to a general medical service.

OBJECTIVE: Overcrowding in the emergency department (ED) has been associated with patient harm, yet little is known about the association between ED boarding and adverse hospitalization outcomes. We sought to examine the association between ED boarding and three common adverse hospitalization outcomes: rapid response team activation (RRT), escalation in care, and mortality. METHOD: We conducted an observational analysis of consecutive patient encounters admitted from the ED to the general medical service between February 2013 and June 2015. This study was conducted in an urban, academic hospital with an annual adult ED census over 90,000. We defined boarding as patients with greater than 4h from ED bed order to ED departure to hospital ward. The primary outcome was a composite of adverse outcomes in the first 24h of admission, including RRT activation, care escalation to intensive care, or in-hospital mortality. RESULTS: A total of 31,426 patient encounters were included of which 3978 (12.7%) boarded in the ED for 4h or more. Adverse outcomes occurred in 1.92% of all encounters. Comparing boarded vs. non-boarded patients, 41 (1.03%) vs. 244 (0.90%) patients experienced a RRT activation, 53 (1.33%) vs. 387 (1.42%) experienced a care escalation, and 1 (0.03%) vs.12 (0.04%) experienced unanticipated in-hospital death, within 24h of ED admission. In unadjusted analysis, there was no difference in the composite outcome between boarding and non-boarding patients (1.91% vs. 1.91%, p=0.994). Regression analysis adjusted for patient demographics, acuity, and comorbidities also showed no association between boarding and the primary outcome. A sensitivity analysis showed an association between ED boarding and the composite outcome inclusive of the entire inpatient hospital stay (5.8% vs. 4.7%, p=0.003). CONCLUSION: Within the first 24h of hospital admission to a general medicine service, adverse hospitalization outcomes are rare and not associated with ED boarding.