Underrepresentation of racial and ethnic minority groups in gynecologic oncology: An analysis of over 250 trials.

OBJECTIVE: To describe the participation of racial and ethnic minority groups (REMGs) in gynecologic oncology trials. METHODS: Gynecologic oncology studies registered on ClinicalTrials.gov between 2007 and 2020 were identified. Trials with published results were analyzed based on reporting of race/ethnicity in relation to disease site and trial characteristics. Expected enrollment by race/ethnicity was calculated and compared to actual enrollment, adjusted for 2010 US Census population data. RESULTS: 2146 gynecologic oncology trials were identified. Of published trials (n = 252), 99 (39.3%) reported race/ethnicity data. Recent trials were more likely to report these data (36% from 2007 to 2009; 51% 2013-2015; and 53% from 2016 to 2018, p = 0.01). Of all trials, ovarian cancer trials were least likely to report race/ethnicity data (32.1% vs 39.3%, p = 0.011). Population-adjusted under-enrollment for Blacks was 7-fold in ovarian cancer, Latinx 10-fold for ovarian and 6-fold in uterine cancer trials, Asians 2.5-fold in uterine cancer trials, and American Indian and Alaska Native individuals 6-fold in ovarian trials. Trials for most disease sites have enrolled more REMGs in recent years - REMGs made up 19.6% of trial participants in 2007-2009 compared to 38.1% in 2016-2018 (p < 0.0001). CONCLUSION: Less than half of trials that published results reported race/ethnicity data. Available data reveals that enrollment of REMGs is significantly below expected rates based on national census data. These disparities persisted even after additionally adjusting for population size. Despite improvement in recent years, additional recruitment of REMGs is needed to achieve more representative and equitable participation in gynecologic cancer clinical trials.

Analysis of Female Participant Representation in Registered Oncology Clinical Trials in the United States from 2008 to 2020.

BACKGROUND: Female underrepresentation in oncology clinical trials can result in outcome disparities. We evaluated female participant representation in US oncology trials by intervention type, cancer site, and funding. MATERIALS AND METHODS: Data were extracted from the publicly available Aggregate Analysis of ClinicalTrials.gov database. Initially, 270,172 studies were identified. Following the exclusion of trials using Medical Subject Heading terms, manual review, those with incomplete status, non-US location, sex-specific organ cancers, or lacking participant sex data, 1650 trials consisting of 240,776 participants remained. The primary outcome was participation to prevalence ratio (PPR): percent females among trial participants divided by percent females in the disease population per US Surveillance, Epidemiology, and End Results Program data. PPRs of 0.8-1.2 reflect proportional female representation. RESULTS: Females represented 46.9% of participants (95% CI, 45.4-48.4); mean PPR for all trials was 0.912. Females were underrepresented in surgical (PPR 0.74) and other invasive (PPR 0.69) oncology trials. Among cancer sites, females were underrepresented in bladder (odds ratio [OR] 0.48, 95% CI 0.26-0.91, P = .02), head/neck (OR 0.44, 95% CI 0.29-0.68, P < .01), stomach (OR 0.40, 95% CI 0.23-0.70, P < .01), and esophageal (OR 0.40 95% CI 0.22-0.74, P < .01) trials. Hematologic (OR 1.78, 95% CI 1.09-1.82, P < .01) and pancreatic (OR 2.18, 95% CI 1.46-3.26, P < .01) trials had higher odds of proportional female representation. Industry-funded trials had greater odds of proportional female representation (OR 1.41, 95% CI 1.09-1.82, P = .01) than US government and academic-funded trials. CONCLUSIONS: Stakeholders should look to hematologic, pancreatic, and industry-funded cancer trials as exemplars of female participant representation and consider female representation when interpreting trial results.

Explaining the description-experience gap in risky decision-making: learning and memory retention during experience as causal mechanisms.

When making decisions based on probabilistic outcomes, people guide their behavior using knowledge gathered through both indirect descriptions and direct experience. Paradoxically, how people obtain information significantly impacts apparent preferences. A ubiquitous example is the description-experience gap: individuals seemingly overweight low probability events when probabilities are described yet underweight them when probabilities must be experienced firsthand. A leading explanation for this fundamental gap in decision-making is that probabilities are weighted differently when learned through description relative to experience, yet a formal theoretical account of the mechanism responsible for such weighting differences remains elusive. We demonstrate how various learning and memory retention models incorporating neuroscientifically motivated learning mechanisms can explain why probability weighting and valuation parameters often are found to vary across description and experience. In a simulation study, we show how learning through experience can lead to systematically biased estimates of probability weighting when using a traditional cumulative prospect theory model. We then use hierarchical Bayesian modeling and Bayesian model comparison to show how various learning and memory retention models capture participants' behavior over and above changes in outcome valuation and probability weighting, accounting for description and experience-based decisions in a within-subject experiment. We conclude with a discussion of how substantive models of psychological processes can lead to insights that heuristic statistical models fail to capture.

Non-monotonic effects of GABAergic synaptic inputs on neuronal firing.

GABA is generally known as the principal inhibitory neurotransmitter in the nervous system, usually acting by hyperpolarizing membrane potential. However, GABAergic currents sometimes exhibit non-inhibitory effects, depending on the brain region, developmental stage or pathological condition. Here, we investigate the diverse effects of GABA on the firing rate of several single neuron models, using both analytical calculations and numerical simulations. We find that GABAergic synaptic conductance and output firing rate exhibit three qualitatively different regimes as a function of GABA reversal potential, EGABA: monotonically decreasing for sufficiently low EGABA (inhibitory), monotonically increasing for EGABA above firing threshold (excitatory); and a non-monotonic region for intermediate values of EGABA. In the non-monotonic regime, small GABA conductances have an excitatory effect while large GABA conductances show an inhibitory effect. We provide a phase diagram of different GABAergic effects as a function of GABA reversal potential and glutamate conductance. We find that noisy inputs increase the range of EGABA for which the non-monotonic effect can be observed. We also construct a micro-circuit model of striatum to explain observed effects of GABAergic fast spiking interneurons on spiny projection neurons, including non-monotonicity, as well as the heterogeneity of the effects. Our work provides a mechanistic explanation of paradoxical effects of GABAergic synaptic inputs, with implications for understanding the effects of GABA in neural computation and development.

The benefits of immature cognitive control: How distributed attention guards against learning traps.

Cognitive control allows one to focus one's attention efficiently on relevant information while filtering out irrelevant information. This ability provides a means of rapid and effective learning, but using this control also brings risks. Importantly, useful information may be ignored and missed, and learners may fall into "learning traps" (e.g., learned inattention) wherein they fail to realize that what they ignore carries important information. Previous research has shown that adults may be more prone to such traps than young children, but the mechanisms underlying this difference are unclear. The current study used eye tracking to examine the role of attentional control during learning in succumbing to these learning traps. The participants, 4-year-old children and adults, completed a category learning task in which an unannounced switch occurred wherein the feature dimensions most relevant to correct categorization became irrelevant and formerly irrelevant dimensions became relevant. After the switch, adults were more likely than children to ignore the new highly relevant dimension and settle on a suboptimal categorization strategy. Furthermore, eye-tracking analyses reveal that greater attentional selectivity during learning (i.e., optimizing attention to focus only on the most relevant sources of information) predicted this tendency to miss important information later. Children's immature cognitive control, leading to broadly distributed attention, appears to protect children from this trap-although at the cost of less efficient and slower learning. These results demonstrate the double-edged sword of cognitive control and suggest that immature control may serve an adaptive function early in development.

Gaussian process linking functions for mind, brain, and behavior.

The link between mind, brain, and behavior has mystified philosophers and scientists for millennia. Recent progress has been made by forming statistical associations between manifest variables of the brain (e.g., electroencephalogram [EEG], functional MRI [fMRI]) and manifest variables of behavior (e.g., response times, accuracy) through hierarchical latent variable models. Within this framework, one can make inferences about the mind in a statistically principled way, such that complex patterns of brain-behavior associations drive the inference procedure. However, previous approaches were limited in the flexibility of the linking function, which has proved prohibitive for understanding the complex dynamics exhibited by the brain. In this article, we propose a data-driven, nonparametric approach that allows complex linking functions to emerge from fitting a hierarchical latent representation of the mind to multivariate, multimodal data. Furthermore, to enforce biological plausibility, we impose both spatial and temporal structure so that the types of realizable system dynamics are constrained. To illustrate the benefits of our approach, we investigate the model's performance in a simulation study and apply it to experimental data. In the simulation study, we verify that the model can be accurately fitted to simulated data, and latent dynamics can be well recovered. In an experimental application, we simultaneously fit the model to fMRI and behavioral data from a continuous motion tracking task. We show that the model accurately recovers both neural and behavioral data and reveals interesting latent cognitive dynamics, the topology of which can be contrasted with several aspects of the experiment.

Characterizing Clinical Trials in Plastic and Reconstructive Surgery: A Systematic Review of ClinicalTrials.gov From 2007 to 2020.

BACKGROUND: Clinical trials form the backbone of evidence-based medicine. ClinicalTrials.gov is the world's largest clinical trial registry, and the state of clinical trials in plastic and reconstructive surgery (PRS) within that database has not been comprehensively studied. To that end, we explored the distribution of therapeutic areas that are under investigation, impact of funding on study design and data reporting, and trends in research patterns of all PRS interventional clinical trials registered with ClinicalTrials.gov. METHODS: Using the ClinicalTrials.gov database, we identified and extracted all clinical trials relevant to PRS that were submitted between 2007 and 2020. Studies were classified based on anatomic locations, therapeutic categories, and specialty topics. Cox proportional hazard was used to calculate adjusted hazard ratios (HRs) for early discontinuation and results reporting. RESULTS: A total of 3224 trials that included 372,095 participants were identified. The PRS trials grew at an annual rate of 7.9%. The therapeutic classes most represented were wound healing (41.3%) and cosmetics (18.1%). Funding for PRS clinical trials is largely provided through academic institutions (72.7%), while industry and US government constituted a minority. Industry-funded studies were more likely to be discontinued early than those funded by academics (HR, 1.89) or government (HR, 1.92) and to be nonblinded and nonrandomized. Academic-funded studies were the least likely to report results data within 3 years of trial completion (odds ratio, 0.87). CONCLUSIONS: A gulf exists in the representation of different PRS specialties among clinical trials. We highlight the role of funding source in trial design and data reporting to identify a potential source of financial waste and to stress the need for continued appropriate oversight.

Distributed Neural Systems Support Flexible Attention Updating during Category Learning.

To accurately categorize items, humans learn to selectively attend to the stimulus dimensions that are most relevant to the task. Models of category learning describe how attention changes across trials as labeled stimuli are progressively observed. The Adaptive Attention Representation Model (AARM), for example, provides an account in which categorization decisions are based on the perceptual similarity of a new stimulus to stored exemplars, and dimension-wise attention is updated on every trial in the direction of a feedback-based error gradient. As such, attention modulation as described by AARM requires interactions among processes of orienting, visual perception, memory retrieval, prediction error, and goal maintenance to facilitate learning. The current study explored the neural bases of attention mechanisms using quantitative predictions from AARM to analyze behavioral and fMRI data collected while participants learned novel categories. Generalized linear model analyses revealed patterns of BOLD activation in the parietal cortex (orienting), visual cortex (perception), medial temporal lobe (memory retrieval), basal ganglia (prediction error), and pFC (goal maintenance) that covaried with the magnitude of model-predicted attentional tuning. Results are consistent with AARM's specification of attention modulation as a dynamic property of distributed cognitive systems.

The quest for simplicity in human learning: Identifying the constraints on attention.

For better or worse, humans live a resource-constrained existence; only a fraction of physical sensations ever reach conscious awareness, and we store a shockingly small subset of these experiences in memory for later use. Here, we examined the effects of attention constraints on learning. Among models that frame selective attention as an optimization problem, attention orients toward information that will reduce errors. Using this framing as a basis, we developed a suite of models with a range of constraints on the attention available during each learning event. We fit these models to both choice and eye-fixation data from four benchmark category-learning data sets, and choice data from another dynamic categorization data set. We found consistent evidence for computations we refer to as "simplicity", where attention is deployed to as few dimensions of information as possible during learning, and "competition", where dimensions compete for selective attention via lateral inhibition.

Clinical Trial Outcomes in Urology: Assessing Early Discontinuation, Results Reporting and Publication in ClinicalTrials.Gov Registrations 2007-2019.

PURPOSE: Clinical trials require significant resources, but benefits are only realized after trial completion and dissemination of results. We comprehensively assessed early discontinuation, registry results reporting, and publication by trial sponsor and subspecialty in urology trials. MATERIALS AND METHODS: We assessed trial registrations from 2007 to 2019 on ClinicalTrials.gov and publication data from PubMed®/MEDLINE®. Associations between sponsor or subspecialty with early discontinuation were assessed using Cox proportional hazards and results reporting or publication with logistic regression at 3 years after completion. RESULTS: Of 8,636 trials 3,541 (41.0%) were completed and 999 (11.6%) were discontinued. Of completed trials 26.9% reported results and 21.6% were published. Sponsors included academic institutions (53.1%), industry (37.1%) and the U.S. government (9.8%). Academic-sponsored (adjusted HR 0.81, 95% CI 0.69-0.96, p=0.012) and government-sponsored trials (adjusted HR 0.62, 95% CI 0.49-0.78, p <0.001) were less likely than industry to discontinue early. Government-sponsored trials were more likely to report (adjusted OR 1.72, 95% CI 1.17-2.54, p=0.006) and publish (adjusted OR 1.89, 95% CI 1.23-2.89, p=0.004). Academic-sponsored trials were less likely to report (adjusted OR 0.65, CI:0.48-0.88, p=0.006) but more likely to publish (adjusted OR 1.72, 95% CI 1.25-2.37, p <0.001). These outcomes were similar across subspecialties. However, endourology was more likely to discontinue early (adjusted HR 2.00, 95% CI 1.53-2.95, p <0.001), general urology was more likely to report results (adjusted OR 1.54, 95% CI 1.13-2.11, p=0.006) and andrology was less likely to publish (adjusted OR 0.53, 95% CI 0.35-0.81, p=0.003). CONCLUSIONS: Sponsor type is significantly associated with trial completion and dissemination. Government-sponsored trials had the best performance, while industry and academic-sponsored trials lagged in completion and results reporting, respectively. Subspecialty played a lesser role. Lack of dissemination remains a problem for urology trials.

Bile diversion, a bariatric surgery, and bile acid signaling reduce central cocaine reward.

The gut-to-brain axis exhibits significant control over motivated behavior. However, mechanisms supporting this communication are poorly understood. We reveal that a gut-based bariatric surgery chronically elevates systemic bile acids and attenuates cocaine-induced elevations in accumbal dopamine. Notably, this surgery reduces reward-related behavior and psychomotor sensitization to cocaine. Utilizing a knockout mouse model, we have determined that a main mediator of these post-operative effects is the Takeda G protein-coupled bile acid receptor (TGR5). Viral restoration of TGR5 in the nucleus accumbens of TGR5 knockout animals is sufficient to restore cocaine reward, centrally localizing this TGR5-mediated modulation. These findings define TGR5 and bile acid signaling as pharmacological targets for the treatment of cocaine abuse and reveal a novel mechanism of gut-to-brain communication.

Score-based tests for detecting heterogeneity in linear mixed models.

Cross-level interactions among fixed effects in linear mixed models (also known as multilevel models) can be complicated by heterogeneity stemming from random effects and residuals. When heterogeneity is present, tests of fixed effects (including cross-level interaction terms) are subject to inflated type I or type II error. While the impact of variance change/heterogeneity has been noticed in the literature, few methods have been proposed to detect this heterogeneity in a simple, systematic way. In addition, when heterogeneity among clusters is detected, researchers often wish to know which clusters' variances differed from the others. In this study, we utilize a recently proposed family of score-based tests to distinguish between cross-level interactions and heterogeneity in variance components, also providing information about specific clusters that exhibit heterogeneity. These score-based tests only require estimation of the null model (when variance homogeneity is assumed to hold), and they have been previously applied to psychometric models to detect measurement invariance. In this paper, we extend the tests to linear mixed models, allowing us to use the tests to differentiate between interaction and heterogeneity. We detail the tests' implementation and performance via simulation, provide an empirical example of the tests' use in practice, and provide code illustrating the tests' general application.

Quantifying mechanisms of cognition with an experiment and modeling ecosystem.

Although there have been major strides toward uncovering the neurobehavioral mechanisms involved in cognitive functions like memory and decision making, methods for measuring behavior and accessing latent processes through computational means remain limited. To this end, we have created SUPREME (Sensing to Understanding and Prediction Realized via an Experiment and Modeling Ecosystem): a toolbox for comprehensive cognitive assessment, provided by a combination of construct-targeted tasks and corresponding computational models. SUPREME includes four tasks, each developed symbiotically with a mechanistic model, which together provide quantified assessments of perception, cognitive control, declarative memory, reward valuation, and frustrative nonreward. In this study, we provide validation analyses for each task using two sessions of data from a cohort of cognitively normal participants (N = 65). Measures of test-retest reliability (r: 0.58-0.75), stability of individual differences (ρ: 0.56-0.70), and internal consistency (α: 0.80-0.86) support the validity of our tasks. After fitting the models to data from individual subjects, we demonstrate each model's ability to capture observed patterns of behavioral results across task conditions. Our computational approaches allow us to decompose behavior into cognitively interpretable subprocesses, which we can compare both within and between participants. We discuss potential future applications of SUPREME, including clinical assessments, longitudinal tracking of cognitive functions, and insight into compensatory mechanisms.

On the Neural and Mechanistic Bases of Self-Control.

Intertemporal choice requires a dynamic interaction between valuation and deliberation processes. While evidence identifying candidate brain areas for each of these processes is well established, the precise mechanistic role carried out by each brain region is still debated. In this article, we present a computational model that clarifies the unique contribution of frontoparietal cortex regions to intertemporal decision making. The model we develop samples reward and delay information stochastically on a moment-by-moment basis. As preference for the choice alternatives evolves, dynamic inhibitory processes are executed by way of asymmetric lateral inhibition. We find that it is these lateral inhibition processes that best explain the contribution of frontoparietal regions to intertemporal decision making exhibited in our data.

Individual Differences in the Neural Dynamics of Response Inhibition.

Response inhibition is a widely studied aspect of cognitive control that is particularly interesting because of its applications to clinical populations. Although individual differences are integral to cognitive control, so too is our ability to aggregate information across a group of individuals, so that we can powerfully generalize and characterize the group's behavior. Hence, an examination of response inhibition would ideally involve an accurate estimation of both group- and individual-level effects. Hierarchical Bayesian analyses account for individual differences by simultaneously estimating group and individual factors and compensate for sparse data by pooling information across participants. Hierarchical Bayesian models are thus an ideal tool for studying response inhibition, especially when analyzing neural data. We construct hierarchical Bayesian models of the fMRI neural time series, models assuming hierarchies across conditions, participants, and ROIs. Here, we demonstrate the advantages of our models over a conventional generalized linear model in accurately separating signal from noise. We then apply our models to go/no-go and stop signal data from 11 participants. We find strong evidence for individual differences in neural responses to going, not going, and stopping and in functional connectivity across the two tasks and demonstrate how hierarchical Bayesian models can effectively compensate for these individual differences while providing group-level summarizations. Finally, we validated the reliability of our findings using a larger go/no-go data set consisting of 179 participants. In conclusion, hierarchical Bayesian models not only account for individual differences but allow us to better understand the cognitive dynamics of response inhibition.

Bayesian statistics to test Bayes optimality.

We agree with the authors that putting forward specific models and examining their agreement with experimental data are the best approach for understanding the nature of decision making. Although the authors only consider the likelihood function, prior, cost function, and decision rule (LPCD) framework, other choices are available. Bayesian statistics can be used to estimate essential parameters and assess the degree of optimality.

Factor analysis linking functions for simultaneously modeling neural and behavioral data.

A growing number of researchers have advocated for the advancement of cognitive neuroscience by blending cognitive models with neurophysiology. The recently proposed joint modeling framework is one way to bridge the gap between the abstractions assumed by cognitive models and the neurophysiology obtained by modern methods in neuroscience. Despite this advancement, the current method for linking the two domains is hindered by the dimensionality of the neural data. In this article, we present a new linking function based on factor analysis that allows joint models to grow linearly in complexity with increases in the number of neural features. The new linking function is then evaluated in two simulation studies. The first simulation study shows how the model parameters can be accurately recovered when there are many neural features, that mimics real-world applications. The second simulation shows how the new linking function can (1) properly recover a representation of the data generating model, even in the case of model misspecification, and (2) outperform the previous linking function in a cross-validation test. We close by applying a model equipped with the new linking function to real-world data from a perceptual decision making task. The model allows us to understand how differences in the model parameters emerge as a function of differences in brain function across speed and accuracy instruction.

Chemical Space Mimicry for Drug Discovery.

We describe a new library generation method, Machine-based Identification of Molecules Inside Characterized Space (MIMICS), that generates sets of molecules inspired by a text-based input. MIMICS-generated libraries were found to preserve distributions of properties while simultaneously increasing structural diversity. Newly identified MIMICS-generated compounds were found to be bioactive as inhibitors of specific components of the unfolded protein response (UPR) and the VEGFR2 pathway in cell-based assays, thus confirming the applicability of this methodology toward drug design applications. Wider application of MIMICS could facilitate the efficient utilization of chemical space.

Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data.

The need to test a growing number of theories in cognitive science has led to increased interest in inferential methods that integrate multiple data modalities. In this manuscript, we show how a method for integrating three data modalities within a single framework provides (1) more detailed descriptions of cognitive processes and (2) more accurate predictions of unobserved data than less integrative methods. Specifically, we show how combining either EEG and fMRI with a behavioral model can perform substantially better than a behavioral-data-only model in both generative and predictive modeling analyses. We then show how a trivariate model - a model including EEG, fMRI, and behavioral data - outperforms bivariate models in both generative and predictive modeling analyses. Together, these results suggest that within an appropriate modeling framework, more data can be used to better constrain cognitive theory, and to generate more accurate predictions for behavioral and neural data.