Individual Trial Level Research Articles

Sex distribution in clinical trials of radiologic contrast agents: A 27-year review

PurposeClinical trials play a pivotal role in assessing the safety and efficacy of medical therapies. Addressing sex distribution among enrollees in clinical trials of radiologic contrast agents is essential for ensuring the generalizability of trial outcomes. Previous research has highlighted the influence of demographic factors, particularly sex, on treatment responses, emphasizing the need for equitable representation in clinical trials. Our study aim was to determine the sex distribution of enrollees in clinical trials of radiologic contrast agents. MethodsOur retrospective study included a total of 65 clinical trials conducted between 1990 and 2017 identified on clinicaltrials.gov after a comprehensive review including searching individually for all FDA approved contrast agents. Data collected included the year of FDA approval, the number of participants, sex distribution, trial location, trial phase, and study type. Inter-rater validation ensured data accuracy. ResultsOur analysis revealed fluctuations in sex distribution of trial enrollees. Enrollment of males exceeded females in most years, with a shift towards a more equitable representation in recent trials. Trials conducted in the United States had a higher rate of enrollment by females. Phase I trials had the most balanced representation, whereas Phase IV trials had the highest sex disparity. ConclusionAcross all trials, females made up 47.3 % of enrollees [3316 out of 7016 total enrollees]. Enrollment of males exceeded females in 44 of the 65 trials studied, females outnumbered males in 19 trials, and enrollment was equal between the sexes in 2 trials. While the sex distribution observed across all trials represents an equitable representation of enrollees, the wide variance of sex distribution at the level of individual trials has the potential to limit the generalizability of results.

Self-Defense and Political Rage

This Article considers how American political polarization and the substantive issues driving it raise unique challenges for adjudicating self-defense claims in contexts of political protest. We live in an age where roughly a quarter of the population believes it is at least sometimes justifiable to use violence in defense of political positions, making political partisans somewhat more likely to pose a genuine threat of bodily harm to opponents. Furthermore, the psychological literature shows that people are more likely to perceive threats from people with whom they politically disagree and that juries tend to evaluate reasonableness claims according to their own political positions. All three of these phenomena create challenges for the rule of law due to the increased risk that factually similar cases will turn out differently and that the justice system will merely recreate the monomaniacal, us-versus-them polarization of society at large. This Article surveys the relevant political science and psychological literature on partisanship and reasoning and proposes two interrelated solutions: one pragmatic, at the level of individual trials, and the other cultural, at the level of social discourse. It suggests that judges import what we know about the distortive effects of partisanship into the courtroom through the use of court-appointed psychological experts and jury instructions. Both have shown some success—if tailored precisely to the facts of a specific case—in correcting some forms of juror bias and reasoning errors. This Article further argues that incorporating these processes into the adjudication of politicized self-defense claims will have a broader, expressive value for society as a whole. Trials provide a model for truth-finding, which, for better or for worse, impacts how private citizens evaluate culpability in their day-to-day lives. If trials draw even some people’s attention to the ways in which partisan thinking can generate or justify acts of violence, they may be a force for moderation in how people deal with their political disagreements, which will have benefits far beyond the courtroom.

Unifying network model links recency and central tendency biases in working memory

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased toward the average of past observations. It is assumed to be an optimal strategy by the brain and commonly thought of as an expression of the brain’s ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience – producing short-term sensory history biases – naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli and are not due to a gradual shift of the memory toward the sensory distribution’s mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

Unifying network model links recency and central tendency biases in working memory.

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased toward the average of past observations. It is assumed to be an optimal strategy by the brain and commonly thought of as an expression of the brain's ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience - producing short-term sensory history biases - naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli and are not due to a gradual shift of the memory toward the sensory distribution's mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

Modelling EEG Dynamics with Brain Sources

An electroencephalogram (EEG), recorded on the surface of the scalp, serves to characterize the distribution of electric potential during brain activity. This method finds extensive application in investigating brain functioning and diagnosing various diseases. Event-related potential (ERP) is employed to delineate visual, motor, and other activities through cross-trial averages. Despite its utility, interpreting the spatiotemporal dynamics in EEG data poses challenges, as they are inherently subject-specific and highly variable, particularly at the level of individual trials. Conventionally associated with oscillating brain sources, these dynamics raise questions regarding how these oscillations give rise to the observed dynamical regimes on the brain surface. In this study, we propose a model for spatiotemporal dynamics in EEG data using the Poisson equation, with the right-hand side corresponding to the oscillating brain sources. Through our analysis, we identify primary dynamical regimes based on factors such as the number of sources, their frequencies, and phases. Our numerical simulations, conducted in both 2D and 3D, revealed the presence of standing waves, rotating patterns, and symmetric regimes, mirroring observations in EEG data recorded during picture naming experiments. Notably, moving waves, indicative of spatial displacement in the potential distribution, manifested in the vicinity of brain sources, as was evident in both the simulations and experimental data. In summary, our findings support the conclusion that the brain source model aptly describes the spatiotemporal dynamics observed in EEG data.

A generative model of electrophysiological brain responses to stimulation.

Each brain response to a stimulus is, to a large extent, unique. However this variability, our perceptual experience feels stable. Standard decoding models, which utilise information across several areas to tap into stimuli representation and processing, are fundamentally based on averages. Therefore, they can focus precisely on the features that are most stable across stimulus presentations. But which are these features exactly is difficult to address in the absence of a generative model of the signal. Here, I introduce genephys, a generative model of brain responses to stimulation publicly available as a Python package that, when confronted with a decoding algorithm, can reproduce the structured patterns of decoding accuracy that we observe in real data. Using this approach, I characterise how these patterns may be brought about by the different aspects of the signal, which in turn may translate into distinct putative neural mechanisms. In particular, the model shows that the features in the data that support successful decoding-and, therefore, likely reflect stable mechanisms of stimulus representation-have an oscillatory component that spans multiple channels, frequencies, and latencies of response; and an additive, slower response with a specific (cross-frequency) relation to the phase of the oscillatory component. At the individual trial level, still, responses are found to be highly variable, which can be due to various factors including phase noise and probabilistic activations.

A generative model of electrophysiological brain responses to stimulation

Each brain response to a stimulus is, to a large extent, unique. However this variability, our perceptual experience feels stable. Standard decoding models, which utilise information across several areas to tap into stimuli representation and processing, are fundamentally based on averages. Therefore, they can focus precisely on the features that are most stable across stimulus presentations. But which are these features exactly is difficult to address in the absence of a generative model of the signal. Here, I introduce genephys, a generative model of brain responses to stimulation publicly available as a Python package that, when confronted with a decoding algorithm, can reproduce the structured patterns of decoding accuracy that we observe in real data. Using this approach, I characterise how these patterns may be brought about by the different aspects of the signal, which in turn may translate into distinct putative neural mechanisms. In particular, the model shows that the features in the data that support successful decoding—and, therefore, likely reflect stable mechanisms of stimulus representation—have an oscillatory component that spans multiple channels, frequencies, and latencies of response; and an additive, slower response with a specific (cross-frequency) relation to the phase of the oscillatory component. At the individual trial level, still, responses are found to be highly variable, which can be due to various factors including phase noise and probabilistic activations.

Classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan

The aim of this study was to extend previous findings on selective attention over a lifetime using machine learning procedures. By decoding group membership and stimulus type, we aimed to study differences in the neural representation of inhibitory control across age groups at a single-trial level. We re-analyzed data from 211 subjects from six age groups between 8 and 83 years of age. Based on single-trial EEG recordings during a flanker task, we used support vector machines to predict the age group as well as to determine the presented stimulus type (i.e., congruent, or incongruent stimulus). The classification of group membership was highly above chance level (accuracy: 55%, chance level: 17%). Early EEG responses were found to play an important role, and a grouped pattern of classification performance emerged corresponding to age structure. There was a clear cluster of individuals after retirement, i.e., misclassifications mostly occurred within this cluster. The stimulus type could be classified above chance level in ~ 95% of subjects. We identified time windows relevant for classification performance that are discussed in the context of early visual attention and conflict processing. In children and older adults, a high variability and latency of these time windows were found. We were able to demonstrate differences in neuronal dynamics at the level of individual trials. Our analysis was sensitive to mapping gross changes, e.g., at retirement age, and to differentiating components of visual attention across age groups, adding value for the diagnosis of cognitive status across the lifespan. Overall, the results highlight the use of machine learning in the study of brain activity over a lifetime.Graphical

“Yixie” is likely not created equal for everyone: Distinct reasoning procedures in interpreting the same scalar quantifier

The present study examines the reasoning procedures involved in interpreting the scalar quantifier yixie (‘some’) in Mandarin Chinese via a mouse-tracking technique. Results showed that the aggregate mouse movement in accessing the pragmatic interpretation involved an extra step compared with that in accessing the logical interpretation. However, on the individual-trial level, participants differed significantly in their reasoning procedures. Some of the participants who consistently preferred the logical interpretation followed one-step processes; for those who consistently preferred the pragmatic interpretation, some of them followed two-step processes and some others followed one-step processes. These findings provided evidence for both the contextualist models and defaultist models, but raised concern over the constraint-based accounts. The distinct reasoning processes among the participants may reflect their different views on the nature of scalar implicatures, even for the same scalar quantifier. This extends the observation of scalar diversity to the level of language users. Not only are scalar implicatures from various scales accessed at different rates, but also the scalar implicature for the same scalar term may be represented with distinct status among language users.

Timing of steering actions in locomotor interception of targets following curving trajectories.

Recent work on the visual guidance of locomotor interception of nonuniformly moving targets argued for an early reliance on first-order (velocity-based) changes in the target's bearing angle that was complemented approximately 1 second later with reliance on second-order (acceleration-based) changes. Here we provide further support for this hypothesis in a virtual driving task, in which 19 participants steered a vehicle to intercept targets moving along receding circular trajectories. Adopting a set of carefully designed target trajectories, we tested discriminating predictions with respect to the timing and direction of the first steering action. Analyses of temporal and directional characteristics of first steering events revealed a pattern of results that was fully compatible with our predictions. Moreover, application of the recently developed QuID method, focusing on the temporal co-evolution of steering behavior and the potential information sources driving it, confirmed the operative progression from early reliance on first-order changes to subsequent (after approximately 1 second) reliance on a combination of first- and second-order changes in the target's bearing angle over the course of action at the individual-trial level. The finding of an evolution over time toward higher-order informational variables, potentially captured by a fractional-order time derivative, may have consequences for other locomotor interception tasks such as running to catch a fly ball.

Correlation Between Imaging-Based Intermediate Endpoints and Overall Survival in Men With Metastatic Castration-Resistant Prostate Cancer: Analysis of 28 Randomized Trials Using the Prostate Cancer Clinical Trials Working Group (PCWG2) Criteria in 16,511 Patients.

Radiographic progression-free survival (rPFS) based on Prostate Cancer Working Group 2 (PCWG2) has been increasingly used as a meaningful imaging-based intermediate endpoint (IBIE) for overall survival (OS) in patients with metastatic castration-resistant prostate cancer (mCRPC). In randomized phase III trials, rPFS showed good correlation with OS at the individual trial level. We aimed to assess the correlation between the hazard ratios (HR) of IBIE and OS among PCWG2-based randomized trials. PubMed and EMBASE databases were systematically searched for randomized trials evaluating systemic treatments on mCRPC using PCWG2 up to April 15, 2020. Hazard ratios for OS and IBIEs were extracted and their correlation was assessed using weighted linear regression. Subgroup analyses were performed according to various clinical settings: prior chemotherapy, drug category, type of IBIE (rPFS vs. composite IBIE, latter defined as progression by imaging and one or a combination of PSA, pain, skeletal-related events, and performance status), and publication year. Twenty-eight phase II-III randomized trials (16,511 patients) were included. Correlation between OS and IBIE was good (R2=0.57, 95% confidence interval [CI], 0.35-0.78). Trials using rPFS showed substantially higher correlation than those using a composite IBIE (R2=0.58, 95% CI, 0.32-0.82 vs. 0.00, 95% CI, -0.01 to 0.01). Correlations between OS and IBIE in other subgroups were at least moderate in nearly all subgroups (R2=0.32-0.91). IBIEs in the era of PCWG2 correlate well with OS in randomized trials for systemic drugs in patients with mCRPC. PCWG2-based rPFS should be used instead of a composite IBIE that includes PSA and other clinical variables.

Pilot test of an accrual Common Metric for the NIH Clinical and Translational Science Awards (CTSA) Consortium: Metric usefulness.

The Common Metrics Initiative aims to develop and field metrics to improve research processes within the national Clinical and Translational Science Award (CTSA) Consortium. A Median Accrual Ratio (MAR) common metric was developed to assess the results of efforts to increase subject accrual into a set of clinical trials within the expected time period. A pilot test of the MAR was undertaken at Tufts Clinical and Translational Science Institute (CTSI) with eight CTSA Consortium hubs. Post-pilot interviews were conducted with 9 CTSA Principal Investigators (PIs) and 23 pilot team members. Over three-quarters (78%) of respondents reported that the MAR could be useful for performance improvement, but also described limitations or concerns. The most commonly cited barrier to MAR use for performance improvement was difficulty in interpreting the single value that is produced. Most respondents were interested in using the MAR to assess recruitment at an individual trial level. Majority of respondents (63%) had mixed opinions about aggregating metric results across the CTSA Consortium for comparison or benchmarking. Collecting data about additional contextual factors, and comparing accrual between subgroups, were cited as potentially helping address concerns about aggregation. Significant challenges remain in ensuring that the MAR can be sufficiently useful for collaborative process improvement. We offer recommendations to potentially improve metric usefulness.

Context Effects in the Judgment of Visual Relative-Frequency: Trial-by-Trial Adaptation and Non-linear Sequential Effect.

Humans' judgment of relative-frequency, similar to their use of probability in decision-making, is often distorted as an inverted-S-shape curve—small relative-frequency overestimated and large relative-frequency underestimated. Here we investigated how the judgment of relative-frequency, despite its natural reference points (0 and 1) and stereotyped distortion, may adapt to the environmental statistics. The task was to report the relative-frequency of black (or white) dots in a visual array of black and white dots. We found that participants' judgment was distorted in the typical inverted-S-shape, but the distortion curve was influenced by both the central tendency and spread of the distribution of objective relative-frequencies: the lower the central tendency, the higher the overall judgment (contrast effect); the higher the spread, the more curved the inverted-S-shape (curvature effect). These context effects are in the spirit of efficient coding but opposite to what would be predicted by Bayesian inference. We further modeled the context effects on the level of individual trials, through which we found not only a trial-by-trial adaptation, but also the non-linear sequential effects that were recently reported mainly in circularly distributed visual stimuli.

Put your hands up! Gesturing improves preschoolers’ executive function

This study addressed the causal direction of a previously reported relation between preschoolers’ gesturing and their executive functioning on the Dimensional Change Card Sort (DCCS) sorting–switch task. Gesturing the relevant dimension for sorting was induced in a Gesture group through instructions, imitation, and prompts. In contrast, the Control group was instructed to “think hard” when sorting. Preschoolers (N = 50) performed two DCCS tasks: (a) sort by size and then spatial orientation of two objects and (b) sort by shape and then proximity of the two objects. An examination of performance over trials permitted a fine-grained depiction of patterns of younger and older children in the Gesture and Control conditions. After the relevant dimension was switched, the Gesture group had more accurate sorts than the Control group, particularly among younger children on the second task. Moreover, the amount of gesturing predicted the number of correct sorts among younger children on the second task. The overall association between gesturing and sorting was not reflected at the level of individual trials, perhaps indicating covert gestural representation on some trials or the triggering of a relevant verbal representation by the gesturing. The delayed benefit of gesturing, until the second task, in the younger children may indicate a utilization deficiency. Results are discussed in terms of theories of gesturing and thought. The findings open up a new avenue of research and theorizing about the possible role of gesturing in emerging executive function.

Developing a Nonstationary Computational Framework With Application to Modeling Dynamic Modulations in Neural Spiking Responses.

This paper aims to develop a computational model that incorporates the functional effects of modulatory covariates (such as context, task, or behavior), which dynamically alter the relationship between the stimulus and the neural response. We develop a general computational approach along with an efficient estimation procedure in the widely used generalized linear model (GLM) framework to characterize such nonstationary dynamics in spiking response and spatiotemporal characteristics of a neuron at the level of individual trials. The model employs a set of modulatory components, which nonlinearly interact with other stimulus-related signals to reproduce such nonstationary effects. The model is tested for its ability to predict the responses of neurons in the middle temporal cortex of macaque monkeys during an eye movement task. The fitted model proves successful in capturing the fast temporal modulations in the response, reproducing the spike response temporal statistics, and accurately accounting for the neurons' dynamic spatiotemporal sensitivities, during eye movements. The nonstationary GLM framework developed in this study can be used in cases where a time-varying behavioral or cognitive component makes GLM-based models insufficient to describe the dependencies of neural responses on the stimulus-related covariates. In addition to being quite powerful in encoding time-varying response modulations, this general framework also enables a readout of the neural code while dissociating the influence of other nonstimulus covariates. This framework will advance our ability to understand sensory processing in higher brain areas when modulated by several behavioral or cognitive variables.

Guessing versus Choosing an Upcoming Task.

We compared the effects of guessing vs. choosing an upcoming task. In a task-switching paradigm with four tasks, two groups of participants were asked to either guess or choose which task will be presented next under otherwise identical conditions. The upcoming task corresponded to participants’ guesses or choices in 75 % of the trials. However, only participants in the Choosing condition were correctly informed about this, whereas participants in the Guessing condition were told that tasks were determined at random. In the Guessing condition, we replicated previous findings of a pronounced reduction of switch costs in case of incorrect guesses. This switch cost reduction was considerably less pronounced with denied choices in the Choosing condition. We suggest that in the Choosing condition, the signaling of prediction errors associated with denied choices is attenuated because a certain proportion of denied choices is consistent with the overall representation of the situation as conveyed by task instructions. In the Guessing condition, in contrast, the mismatch of guessed and actual task is resolved solely on the level of individual trials by strengthening the representation of the actual task.

Neoadjuvant trials in early breast cancer: pathological response at surgery and correlation to longer term outcomes - what does it all mean?

BackgroundNeoadjuvant breast cancer trials are important for speeding up the introduction of new treatments for patients with early breast cancer and for the highly productive translational research which they facilitate. Meta-analysis of trial data shows clear correlation between pathological response at surgery after neoadjuvant chemotherapy and longer-term outcomes at an individual patient level. However, this does not appear to be present on individual trial level analysis, when correlating improved outcome for the investigational arm for the primary endpoint (pathological response) with longer-term outcomes.DiscussionThe correlation between pathological response and longer-term outcomes in trials is dependent on many factors. These include definitions of pathological response, both complete and partial; assessment methods for pathological response at surgery; subtype and prognosis of breast cancer at diagnosis; number of patients recruited; adjuvant treatments; the mechanism of action of the investigational drug; the length of follow-up at the time of reporting; the definitions used in longer-term outcomes analysis; clonal heterogeneity; and new adaptive trial designs with additional neo/adjuvant treatments. Future developments of neoadjuvant breast cancer trials are discussed. With so many factors influencing the correlation of longer-term outcomes for trial-level data, we conclude that the main focus of neoadjuvant trials should remain the primary endpoint of pathological response.SummaryNeoadjuvant breast cancer trials are very important investigational studies that will continue to increase our understanding of the disease and offer the potential of more rapid introduction of new treatments for women with high-risk early breast cancer. In the future, we are likely to see both novel trial designs adopted in the neoadjuvant context and modifications of neo/adjuvant treatments for pathological non-responders within clinical trials. Both of these have the intention of improving longer-term outcomes for patients who do not have a good pathological response to first-line neoadjuvant treatment. If successful, these developments are likely to reduce further any positive correlation between pathological response and longer-term outcomes.

Relations between running memory and fluid intelligence

A total of 1734 adults performed two running memory tasks and a battery of cognitive tests representing four cognitive abilities. Simultaneous analyses were used to identify unique relations of each cognitive ability, including fluid intelligence, on the running memory measures. The large sample size allowed powerful analyses of the relations at the level of individual trials, separate list lengths, and different serial positions. The results indicated that the relations of running memory performance with cognitive abilities were remarkably constant from the first to the last trial, across different list lengths, and on successive input positions. It is proposed that an important aspect of fluid intelligence is the ability to cope with novelty and complexity, and that running memory tasks may merely be one of many ways in which those processes can be operationalized.

A graphical user interface for infant ERP analysis

Recording of event-related potentials (ERPs) is one of the best-suited technologies for examining brain function in human infants. Yet the existing software packages are not optimized for the unique requirements of analyzing artifact-prone ERP data from infants. We developed a new graphical user interface that enables an efficient implementation of a two-stage approach to the analysis of infant ERPs. In the first stage, video records of infant behavior are synchronized with ERPs at the level of individual trials to reject epochs with noncompliant behavior and other artifacts. In the second stage, the interface calls MATLAB and EEGLAB (Delorme & Makeig, Journal of Neuroscience Methods 134(1):9–21, 2004) functions for further preprocessing of the ERP signal itself (i.e., filtering, artifact removal, interpolation, and rereferencing). Finally, methods are included for data visualization and analysis by using bootstrapped group averages. Analyses of simulated and real EEG data demonstrated that the proposed approach can be effectively used to establish task compliance, remove various types of artifacts, and perform representative visualizations and statistical comparisons of ERPs. The interface is available for download from http://www.uta.fi/med/icl/methods/eeg.html in a format that is widely applicable to ERP studies with special populations and open for further editing by users.Electronic supplementary materialThe online version of this article (doi:10.3758/s13428-013-0404-4) contains supplementary material, which is available to authorized users.

Measuring Memory Reactivation With Functional MRI

Environmental cues often remind us of earlier experiences by triggering the reactivation of memories of events past. Recent evidence suggests that memory reactivation can be observed using functional MRI and that distributed pattern analyses can even provide evidence of reactivation on individual trials. The ability to measure memory reactivation offers unique and powerful leverage on theoretical issues of long-standing interest in cognitive psychology, providing a means to address questions that have proven difficult to answer with behavioral data alone. In this article, we consider three instances. First, reactivation measures can indicate whether memory-based inferences (i.e., generalization) arise through the encoding of integrated cross-event representations or through the flexible expression of separable event memories. Second, online measures of memory reactivation may inform theories of forgetting by providing information about when competing memories are reactivated during competitive retrieval situations. Finally, neural reactivation may provide a window onto the role of replay in memory consolidation. The ability to track memory reactivation, including at the individual trial level, provides unique leverage that is not afforded by behavioral measures and thus promises to shed light on such varied topics as generalization, integration, forgetting, and consolidation.