Comparative real-world survival outcomes of muscle-invasive bladder cancer treated with bladder-only vs. whole-pelvis concurrent chemoradiation.

INTRODUCTION: Elective pelvic nodal irradiation for patients with muscle-invasive bladder cancer (MIBC) undergoing trimodal therapy (TMT ) is controversial. In patients with node-negative (N0) MIBC, the benefit of elective whole-pelvis concurrent chemoradiation (WP-CCR) compared to bladder-only (BO )-CCR has not been demonstrated. Using real-world data from the National Cancer Database (NCDB ), we sought to compare the overall survival (OS ) between BO-CCR and WP-CCR for MIBC. METHODS: Using the 2020 NCDB Participant User File, we identified cases of MIBC diagnosed between 2017 and 2019. We selected patients with clinical T2-T4aN0M0 disease receiving CCR as first-line treatment. CCR was defined as transurethral resection of bladder tumor followed by ≥40 Gy radiation to the bladder with concurrent single- or multiple-agent chemotherapy. Based on elective nodal irradiation status, patients were stratified as having received BO-CCR vs. WP-CCR. OS analysis was performed using summary three-month conditional landmark, inverse probability treatment weighting (IPTW)-adjusted Kaplan-Meier estimates, and Cox regression. RESULTS: A total of 604 patients receiving CCR for MIBC were identified: 367 (60.8%) BO-CCR and 237 (39.2%) WP-CCR. Before IPTW, the groups were imbalanced in terms of baseline characteristics. The median followup of the weighted population was 42.3 months (interquartile range 18.1-49.1 months). In IPTW-adjusted Cox proportional hazards regression analysis, WP-CCR was associated with a significant OS benefit compared to BO-CCR (adjusted hazard ratio 0.72, 95% confidence interval 0.54-0.96, p=0.026). CONCLUSIONS: In the setting of CCR for N0 MIBC, this retrospective NCDB analysis revealed that WP-CCR was associated with a benefit in OS compared to BO-CCR.

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.

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.

Comparative real-world survival outcomes of metastatic papillary and clear cell renal cell carcinoma treated with immunotherapy, targeted therapy, and combination therapy.

INTRODUCTION: While there are a plethora of studies supporting novel treatment approaches in metastatic clear cell renal cell carcinoma (ccRCC), much of the data used to inform care of patients with metastatic papillary RCC (pRCC) is extrapolated from ccRCC. Several recent phase III trials have supported the use of immunotherapy (IO) and targeted therapy (TT)+IO in ccRCC, without corresponding data for pRCC. Using ccRCC as a comparison group, we sought to describe real-world trends in the utilization of systemic therapy and its impact on overall survival (OS) among patients with metastatic pRCC. METHODS: Using the National Cancer Database (NCDB), we identified cases of metastatic pRCC and ccRCC between 2015 and 2018. Patients were stratified into groups based on histology and first-line treatments (TT, IO, TT + IO). Differences in baseline characteristics were assessed using the Kruskal-Wallis test for continuous variables, and the Chi-square or Fisher's exact test for categorical variables. Survival analysis was performed using Kaplan-Meier estimates and multivariable Cox regression analyses. RESULTS: A total of 6,920 patients with a diagnosis of metastatic RCC were identified: 594 (8.6%) with pRCC and 6,326 (91.4%) with ccRCC. Overall, 4,710 patients received TT (455 pRCC and 4,255 ccRCC), 1,585 received IO (77 pRCC and 1,508 ccRCC), and 625 received TT+IO (62 pRCC and 563 ccRCC). Temporal trend between 2015 and 2018 revealed an increased utilization of IO and TT + IO for pRCC and ccRCC. In patients with metastatic pRCC, neither IO (HR 1.03; 95% CI 0.75-1.42) nor TT+IO (HR 0.90, 95% CI 0.63-1.28) were associated with better OS compared to TT alone. In contrast, both IO and combination TT and IO were associated with significantly better OS than TT for patients with metastatic ccRCC (IO group: hazard ratio [HR] 0.75, 95% confidence interval [CI] 0.68-0.82; TT+IO group: HR 0.82, 95% CI 0.72-0.93). Cytoreductive nephrectomy was associated with better OS in both pRCC (HR 0.59, 95% CI 0.46-0.77) and ccRCC (HR 0.54, 95% CI 0.50-0.58). CONCLUSIONS: Although IO and TT + IO were associated with better OS among patients with metastatic ccRCC, this same effect was not observed among patients with pRCC.

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.

Clinical outcomes and patterns of population-based management of urachal carcinoma of the bladder: An analysis of the National Cancer Database.

BACKGROUND: Given the low incidence of urachal carcinoma of the bladder (UCB), there is limited published data from contemporary population-based cohorts. This study aimed to describe demographic, clinicopathological features, and survival outcomes of patients diagnosed with UCB. METHODS: The National Cancer Database (2004-2016) was queried for UCB patients. Descriptive analyses characterized demographics and clinicopathologic features. We assessed 5-year overall survival (OS) rates of the entire cohort and subgroups of localized/locally advanced and metastatic disease. We utilized Cox proportional hazards models to assess the association between covariates of interest and all-cause mortality and to examine the impact of surgical technique and chemotherapy. RESULTS: We identified 841 patients with UCB. The most common histologic subtype was non-mucinous adenocarcinoma (39.6%). Approximately 50% had ≥cT2 disease, and 14.3% were metastatic at diagnosis. Altogether, partial cystectomy (60%) was most performed, and lymph node dissection was performed in 377 patients (44.8%), with specific temporal increase in utilization over the study period (p < 0.001). Overall, median OS was 59 months, and 5-year OS was 49%. In patients with localized/locally advanced disease, we found no association between partial and radical cystectomy (Hazards ratio [HR] 1.75; 95% CI 0.72-4.3) as well as receipt of perioperative chemotherapy (HR 1.97, 95% CI 0.79-4.90) and outcomes. Lastly, receipt of systemic therapy was not associated with survival benefit (HR 0.785, 95% CI 0.37-1.65) in metastatic disease cohort. CONCLUSION: This large population-based cohort provides insight into the surgical management and systemic therapy, without clear evidence on the association of chemotherapy and survival in the perioperative and metastatic setting.

Inception loops discover what excites neurons most using deep predictive models.

Finding sensory stimuli that drive neurons optimally is central to understanding information processing in the brain. However, optimizing sensory input is difficult due to the predominantly nonlinear nature of sensory processing and high dimensionality of the input. We developed 'inception loops', a closed-loop experimental paradigm combining in vivo recordings from thousands of neurons with in silico nonlinear response modeling. Our end-to-end trained, deep-learning-based model predicted thousands of neuronal responses to arbitrary, new natural input with high accuracy and was used to synthesize optimal stimuli-most exciting inputs (MEIs). For mouse primary visual cortex (V1), MEIs exhibited complex spatial features that occurred frequently in natural scenes but deviated strikingly from the common notion that Gabor-like stimuli are optimal for V1. When presented back to the same neurons in vivo, MEIs drove responses significantly better than control stimuli. Inception loops represent a widely applicable technique for dissecting the neural mechanisms of sensation.