The Year in Graduate Medical Education: Selected Highlights from 2023.

This special article is the third in an annual series of the Journal of Cardiothoracic and Vascular Anesthesia that highlights significant literature from the world of graduate medical education published over the past year. Major themes addressed in this review include the potential uses and pitfalls of artificial intelligence in graduate medical education, trainee well-being and the rise of unionized house staff, the effect of gender and race/ethnicity on residency application and attrition rates, and the adoption of novel technologies in medical simulation and education. The authors thank the editorial board for again allowing us to draw attention to some of the more interesting work published in the field of graduate medical education during 2023. We hope that the readers find these highlights thought-provoking and informative as we all strive to successfully educate the next generation of anesthesiologists.

Playing three-dimensional video games boosts stereo vision.

Playing two-dimensional video games has been shown to result in improvements in a range of visual and cognitive tasks, and these improvements appear to generalize widely1,2,3,4,5,6. Here we report that young adults with healthy vision, surprisingly, showed a dramatic improvement in stereo vision after playing three-dimensional, but not two-dimensional, video games for a relatively short period of time. Intriguingly, neither group showed any significant improvement in binocular contrast sensitivity. This dissociation suggests that the visual enhancement was specific to genuine stereoscopic processing, not indirectly resulting from enhanced contrast processing, and required engaging in a disparity cue-rich three-dimensional environment.

Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device.

Minimally invasive, high-bandwidth brain-computer-interface (BCI) devices can revolutionize human applications. With orders-of-magnitude improvements in volumetric efficiency over other BCI technologies, we developed a 50-µm-thick, mechanically flexible micro-electrocorticography (µECoG) BCI, integrating 256×256 electrodes, signal processing, data telemetry, and wireless powering on a single complementary metal-oxide-semiconductor (CMOS) substrate containing 65,536 recording and 16,384 stimulation channels, from which we can simultaneously record up to 1024 channels at a given time. Fully implanted below the dura, our chip is wirelessly powered, communicating bi-directionally with an external relay station outside the body. We demonstrated chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from somatosensory, motor, and visual cortices, decoding brain signals at high spatiotemporal resolution.

Update in lung transplantation: anesthetic considerations.

The field of lung transplantation (LTx) has expanded rapidly since its inception in the early 1960s with the work of James Hardy and colleagues at the University of Mississippi from the work of local single specialty physicians into an international multidisciplinary specialty. Advancements throughout the next several decades have led to the completion of over 70,000 lung transplants worldwide. The unique challenges presented by patients with end-stage lung disease have both evolved and remained consistent since then, yet these challenges are being answered with major improvements and advancements in perioperative care in the 21st century. The current practice of LTx medicine is fundamentally multidisciplinary, and members of the LTx team includes surgeons, physicians, and allied health staff. The integration of anesthesiologists into the LTx team as well as the multidisciplinary nature of LTx necessitates anesthetic considerations to be closely incorporated into emerging surgical, medical, and systems techniques for patient care. This review discusses a host of emerging strategies across the spectrum of LTx, including efforts to expand the donor pool, utilization of perioperative extracorporeal life support, perioperative echocardiography, and anesthetic techniques to mitigate primary graft dysfunction that have all contributed to improved long term outcomes in LTx patients.

Energy Guided Diffusion for Generating Neurally Exciting Images.

In recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal activity have become an established method to study tuning properties of biological and artificial visual systems. However, as we move up the visual hierarchy, the complexity of neuronal computations increases. Consequently, it becomes more challenging to model neuronal activity, requiring more complex models. In this study, we introduce a new attention readout for a convolutional data-driven core for neurons in macaque V4 that outperforms the state-of-the-art task-driven ResNet model in predicting neuronal responses. However, as the predictive network becomes deeper and more complex, synthesizing MEIs via straightforward gradient ascent (GA) can struggle to produce qualitatively good results and overfit to idiosyncrasies of a more complex model, potentially decreasing the MEI's model-to-brain transferability. To solve this problem, we propose a diffusion-based method for generating MEIs via Energy Guidance (EGG). We show that for models of macaque V4, EGG generates single neuron MEIs that generalize better across architectures than the state-of-the-art GA while preserving the within-architectures activation and requiring 4.7x less compute time. Furthermore, EGG diffusion can be used to generate other neurally exciting images, like most exciting natural images that are on par with a selection of highly activating natural images, or image reconstructions that generalize better across architectures. Finally, EGG is simple to implement, requires no retraining of the diffusion model, and can easily be generalized to provide other characterizations of the visual system, such as invariances. Thus EGG provides a general and flexible framework to study coding properties of the visual system in the context of natural images.

Bipartite invariance in mouse primary visual cortex.

A defining characteristic of intelligent systems, whether natural or artificial, is the ability to generalize and infer behaviorally relevant latent causes from high-dimensional sensory input, despite significant variations in the environment. To understand how brains achieve generalization, it is crucial to identify the features to which neurons respond selectively and invariantly. However, the high-dimensional nature of visual inputs, the non-linearity of information processing in the brain, and limited experimental time make it challenging to systematically characterize neuronal tuning and invariances, especially for natural stimuli. Here, we extended "inception loops" - a paradigm that iterates between large-scale recordings, neural predictive models, and in silico experiments followed by in vivo verification - to systematically characterize single neuron invariances in the mouse primary visual cortex. Using the predictive model we synthesized Diverse Exciting Inputs (DEIs), a set of inputs that differ substantially from each other while each driving a target neuron strongly, and verified these DEIs' efficacy in vivo. We discovered a novel bipartite invariance: one portion of the receptive field encoded phase-invariant texture-like patterns, while the other portion encoded a fixed spatial pattern. Our analysis revealed that the division between the fixed and invariant portions of the receptive fields aligns with object boundaries defined by spatial frequency differences present in highly activating natural images. These findings suggest that bipartite invariance might play a role in segmentation by detecting texture-defined object boundaries, independent of the phase of the texture. We also replicated these bipartite DEIs in the functional connectomics MICrONs data set, which opens the way towards a circuit-level mechanistic understanding of this novel type of invariance. Our study demonstrates the power of using a data-driven deep learning approach to systematically characterize neuronal invariances. By applying this method across the visual hierarchy, cell types, and sensory modalities, we can decipher how latent variables are robustly extracted from natural scenes, leading to a deeper understanding of generalization.

NEURD: automated proofreading and feature extraction for connectomics.

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows for state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other features that can enable many downstream analyses of neural morphology and connectivity. NEURD can make these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

Pattern completion and disruption characterize contextual modulation in mouse visual cortex.

A key role of sensory processing is integrating information across space. Neuronal responses in the visual system are influenced by both local features in the receptive field center and contextual information from the surround. While center-surround interactions have been extensively studied using simple stimuli like gratings, investigating these interactions with more complex, ecologically-relevant stimuli is challenging due to the high dimensionality of the stimulus space. We used large-scale neuronal recordings in mouse primary visual cortex to train convolutional neural network (CNN) models that accurately predicted center-surround interactions for natural stimuli. These models enabled us to synthesize surround stimuli that strongly suppressed or enhanced neuronal responses to the optimal center stimulus, as confirmed by in vivo experiments. In contrast to the common notion that congruent center and surround stimuli are suppressive, we found that excitatory surrounds appeared to complete spatial patterns in the center, while inhibitory surrounds disrupted them. We quantified this effect by demonstrating that CNN-optimized excitatory surround images have strong similarity in neuronal response space with surround images generated by extrapolating the statistical properties of the center, and with patches of natural scenes, which are known to exhibit high spatial correlations. Our findings cannot be explained by theories like redundancy reduction or predictive coding previously linked to contextual modulation in visual cortex. Instead, we demonstrated that a hierarchical probabilistic model incorporating Bayesian inference, and modulating neuronal responses based on prior knowledge of natural scene statistics, can explain our empirical results. We replicated these center-surround effects in the multi-area functional connectomics MICrONS dataset using natural movies as visual stimuli, which opens the way towards understanding circuit level mechanism, such as the contributions of lateral and feedback recurrent connections. Our data-driven modeling approach provides a new understanding of the role of contextual interactions in sensory processing and can be adapted across brain areas, sensory modalities, and species.