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.

Functional connectomics reveals general wiring rule in mouse visual cortex.

To understand how the brain computes, it is important to unravel the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.

Towards a Foundation Model of the Mouse Visual Cortex.

Understanding the brain's perception algorithm is a highly intricate problem, as the inherent complexity of sensory inputs and the brain's nonlinear processing make characterizing sensory representations difficult. Recent studies have shown that functional models-capable of predicting large-scale neuronal activity in response to arbitrary sensory input-can be powerful tools for characterizing neuronal representations by enabling high-throughput in silico experiments. However, accurately modeling responses to dynamic and ecologically relevant inputs like videos remains challenging, particularly when generalizing to new stimulus domains outside the training distribution. Inspired by recent breakthroughs in artificial intelligence, where foundation models-trained on vast quantities of data-have demonstrated remarkable capabilities and generalization, we developed a "foundation model" of the mouse visual cortex: a deep neural network trained on large amounts of neuronal responses to ecological videos from multiple visual cortical areas and mice. The model accurately predicted neuronal responses not only to natural videos but also to various new stimulus domains, such as coherent moving dots and noise patterns, underscoring its generalization abilities. The foundation model could also be adapted to new mice with minimal natural movie training data. We applied the foundation model to the MICrONS dataset: a study of the brain that integrates structure with function at unprecedented scale, containing nanometer-scale morphology, connectivity with >500,000,000 synapses, and function of >70,000 neurons within a ~1mm3 volume spanning multiple areas of the mouse visual cortex. This accurate functional model of the MICrONS data opens the possibility for a systematic characterization of the relationship between circuit structure and function. By precisely capturing the response properties of the visual cortex and generalizing to new stimulus domains and mice, foundation models can pave the way for a deeper understanding of visual computation.

Cellular composition and circuit organization of the locus coeruleus of adult mice.

The locus coeruleus (LC) houses the vast majority of noradrenergic neurons in the brain and regulates many fundamental functions, including fight and flight response, attention control, and sleep/wake cycles. While efferent projections of the LC have been extensively investigated, little is known about its local circuit organization. Here, we performed large-scale multipatch recordings of noradrenergic neurons in adult mouse LC to profile their morpho-electric properties while simultaneously examining their interactions. LC noradrenergic neurons are diverse and could be classified into two major morpho-electric types. While fast excitatory synaptic transmission among LC noradrenergic neurons was not observed in our preparation, these mature LC neurons connected via gap junction at a rate similar to their early developmental stage and comparable to other brain regions. Most electrical connections form between dendrites and are restricted to narrowly spaced pairs or small clusters of neurons of the same type. In addition, more than two electrically coupled cell pairs were often identified across a cohort of neurons from individual multicell recording sets that followed a chain-like organizational pattern. The assembly of LC noradrenergic neurons thus follows a spatial and cell-type-specific wiring principle that may be imposed by a unique chain-like rule.

The Year in Cardiothoracic and Vascular Anesthesia: Selected Highlights from 2022.

This special article is the 15th in an annual series for the Journal of Cardiothoracic and Vascular Anesthesia. The authors thank the editor-in-chief Dr. Kaplan and the editorial board for the opportunity to continue this series, namely the research highlights of the past year in the specialties of cardiothoracic and vascular anesthesiology. The major themes selected for 2022 are outlined in this introduction, and each highlight is reviewed in detail in the main body of the article. The literature highlights, in the specialties for 2022, begin with an update on COVID-19 therapies, with a focus on the temporal updates in a wide range of therapies, progressing from medical to the use of extracorporeal membrane oxygenation and, ultimately, with lung transplantation in this high-risk group. The second major theme is focused on medical cardiology, with the authors discussing new insights into the life cycle of coronary disease, heart failure treatments, and outcomes related to novel statin therapy. The third theme is focused on mechanical circulatory support, with discussions focusing on both right-sided and left-sided temporary support outcomes and the optimal timing of deployment. The fourth and final theme is an update on cardiac surgery, with a discussion of the diverse aspects of concomitant valvular surgery and the optimal approach to procedural treatment for coronary artery disease. The themes selected for this 15th special article are only a few of the diverse advances in the specialties during 2022. These highlights will inform the reader of key updates on a variety of topics, leading to the improvement of perioperative outcomes for patients with cardiothoracic and vascular disease.

State-dependent pupil dilation rapidly shifts visual feature selectivity.

To increase computational flexibility, the processing of sensory inputs changes with behavioural context. In the visual system, active behavioural states characterized by motor activity and pupil dilation1,2 enhance sensory responses, but typically leave the preferred stimuli of neurons unchanged2-9. Here we find that behavioural state also modulates stimulus selectivity in the mouse visual cortex in the context of coloured natural scenes. Using population imaging in behaving mice, pharmacology and deep neural network modelling, we identified a rapid shift in colour selectivity towards ultraviolet stimuli during an active behavioural state. This was exclusively caused by state-dependent pupil dilation, which resulted in a dynamic switch from rod to cone photoreceptors, thereby extending their role beyond night and day vision. The change in tuning facilitated the decoding of ethological stimuli, such as aerial predators against the twilight sky10. For decades, studies in neuroscience and cognitive science have used pupil dilation as an indirect measure of brain state. Our data suggest that, in addition, state-dependent pupil dilation itself tunes visual representations to behavioural demands by differentially recruiting rods and cones on fast timescales.

Sustained deep-tissue voltage recording using a fast indicator evolved for two-photon microscopy.

Genetically encoded voltage indicators are emerging tools for monitoring voltage dynamics with cell-type specificity. However, current indicators enable a narrow range of applications due to poor performance under two-photon microscopy, a method of choice for deep-tissue recording. To improve indicators, we developed a multiparameter high-throughput platform to optimize voltage indicators for two-photon microscopy. Using this system, we identified JEDI-2P, an indicator that is faster, brighter, and more sensitive and photostable than its predecessors. We demonstrate that JEDI-2P can report light-evoked responses in axonal termini of Drosophila interneurons and the dendrites and somata of amacrine cells of isolated mouse retina. JEDI-2P can also optically record the voltage dynamics of individual cortical neurons in awake behaving mice for more than 30 min using both resonant-scanning and ULoVE random-access microscopy. Finally, ULoVE recording of JEDI-2P can robustly detect spikes at depths exceeding 400 µm and report voltage correlations in pairs of neurons.

The Year in Graduate Medical Education: Selected Highlights From 2021.

This special article is the first in a planned annual series for the Journal of Cardiothoracic and Vascular Anesthesia that will highlight significant literature from the world of graduate medical education (GME) that was published over the past year. The major themes selected for this inaugural review are the educational value of simulation and training workshops, the expanding role of social media and other information technologies in GME and recruitment, the state of residency and fellowship training before the COVID-19 pandemic, and the inevitable effects COVID-19 has had on graduate medical education. The authors would like to thank the editorial board for allowing us to shine a light on a small subset of the writing and research produced in this field, so that educators may understand how best to educate and train the next generation of anesthesiologists.

Spatial and temporal proximity of objects for maximal crowding.

Crowding refers to the deleterious visual interaction among nearby objects. Does maximal crowding occur when objects are closest to one another in space and time? We examined how crowding depends on the spatial and temporal proximity, retinally and perceptually, between a target and flankers. Our target was a briefly flashed T-stimulus presented at 10° right of fixation (3-o'clock position). It appeared at different target-onset-to-flanker asynchronies with respect to the instant when a pair of flanking Ts, revolving around the fixation target, reached the 3-o'clock position. Observers judged the orientation of the target-T (the crowding task), or its position relative to the revolving flankers (the flash-lag task). Performance was also measured in the absence of flanker motion: target and flankers were either presented simultaneously (closest retinal temporal proximity) with different angular spatial offsets, or were presented collinearly (closest retinal spatial proximity) with different temporal onset asynchronies. We found that neither retinal nor perceptual spatial or temporal proximity could account for when maximal crowding occurred. Simulations using a model based on feed-forward interactions between sustained and transient channels in static and motion pathways, taking into account the differential response latencies, can explain the crowding functions observed under various spatio-temporal conditions between the target and flankers.

The Year in Cardiothoracic and Vascular Anesthesia: Selected Highlights from 2021.

This special article is the fourteenth in an annual series for the Journal of Cardiothoracic and Vascular Anesthesia. The authors thank the Editor-in-Chief, Dr. Kaplan, and the editorial board for the opportunity to continue this series; namely, the research highlights of the past year in the specialty of cardiothoracic and vascular anesthesiology. The major themes selected for 2021 are outlined in this introduction, and each highlight is reviewed in detail in the main body of the article. The literature highlights in the specialty for 2021 begin with an update on structural heart disease, with a focus on updates in arrhythmia and aortic valve disorders. The second major theme is an update on coronary artery disease, with discussion of both medical and procedural management. The third major theme is focused on the perioperative management of patients with COVID-19, with the authors highlighting literature discussing the impact of the disease on the right ventricle and thromboembolic events. The fourth and final theme is an update in heart failure, with discussion of diverse aspects of this area. The themes selected for this fourteenth special article are only a few of the diverse advances in the specialty during 2021. These highlights will inform the reader of key updates on a variety of topics, leading to improvement of perioperative outcomes for patients with cardiothoracic and vascular disease.