A Nomothetic Span Approach to the Construct Validation of Sustained Attention Consistency: Re-Analyzing Two Latent-Variable Studies of Performance Variability and Mind-Wandering Self-Reports.

The ability to sustain attention consistency is frequently assessed using either objective behavioral measures, such as reaction time (RT) variability, or subjective self-report measures, such as rates of task-unrelated thought (TUT). The current studies examined whether the individual-difference covariation in these measures provides a more construct valid assessment of attention consistency than does either alone. We argue that performance and self-report measures mutually validate each other; each measurement approach has its own sources of error, so their shared variance should best reflect the attention consistency construct. We reanalyzed two latent-variable studies where RT variability and TUTs were measured in multiple tasks (Kane et al. in J Exp Psychol Gen 145:1017-1048, 2016; Unsworth et al. in J Exp Psychol Gen 150:1303-1331, 2021), along with several nomological network constructs to test the convergent and discriminant validity of a general attention consistency factor. Confirmatory factor analyses assessing bifactor (preregistered) and hierarchical (non-preregistered) models suggested that attention consistency can be modeled as the shared variance among objective and subjective measures. This attention consistency factor was related to working memory capacity, attention (interference) control, processing speed, state motivation and alertness, and self-reported cognitive failures and positive schizotypy. Although bifactor models of general attention consistency provide the most compelling construct validity evidence for a specific ability to sustain attention, multiverse analyses of outlier decisions suggested they are less robust than hierarchical models. The results provide evidence for the general ability to sustain attention consistency and suggestions for improving its measurement.

A compressed large language model embedding dataset of ICD 10 CM descriptions.

This paper presents novel datasets providing numerical representations of ICD-10-CM codes by generating description embeddings using a large language model followed by a dimension reduction via autoencoder. The embeddings serve as informative input features for machine learning models by capturing relationships among categories and preserving inherent context information. The model generating the data was validated in two ways. First, the dimension reduction was validated using an autoencoder, and secondly, a supervised model was created to estimate the ICD-10-CM hierarchical categories. Results show that the dimension of the data can be reduced to as few as 10 dimensions while maintaining the ability to reproduce the original embeddings, with the fidelity decreasing as the reduced-dimension representation decreases. Multiple compression levels are provided, allowing users to choose as per their requirements, download and use without any other setup. The readily available datasets of ICD-10-CM codes are anticipated to be highly valuable for researchers in biomedical informatics, enabling more advanced analyses in the field. This approach has the potential to significantly improve the utility of ICD-10-CM codes in the biomedical domain.

A "Goldilocks zone" for mind-wandering reports? A secondary data analysis of how few thought probes are enough for reliable and valid measurement.

Mind-wandering assessment relies heavily on the thought probe technique as a reliable and valid method to assess momentary task-unrelated thought (TUT), but there is little guidance available to help researchers decide how many probes to include within a task. Too few probes may lead to unreliable measurement, but too many probes might artificially disrupt normal thought flow and produce reactive effects. Is there a "Goldilocks zone" for how few thought probes can be used to reliably and validly assess individual differences in mind-wandering propensity? We address this question by reanalyzing two published datasets (Study 1, n = 541; Study 2, ns ≈ 260 per condition) in which thought probes were presented in multiple tasks. Our primary analyses randomly sampled probes in increments of two for each subject in each task. A series of confirmatory factor analyses for each probe "bin" size tested whether the latent correlations between TUT rate and theoretically relevant constructs like working memory capacity, attention-control ability, disorganized schizotypy, and retrospective self-reported mind wandering changed as more probes assessed the TUT rate. TUT rates were remarkably similar across increasing probe-bin sizes and zero-order correlations within and between tasks stabilized at 8-10 probes; moreover, TUT-rate correlations with other latent variables stabilized at about 8 thought probes. Our provisional recommendation (with caveats) is that researchers may use as few as 8 thought probes in prototypical cognitive tasks to gain reliable and valid information about individual differences in TUT rate.

Bayesian basket trial design with false-discovery rate control.

BACKGROUND: Recent advances in developing "tumor agnostic" oncology therapies have identified molecular targets that define patient subpopulations in a manner that supersedes conventional criteria for cancer classification. These successes have produced effective targeted therapies that are administered to patients regardless of their tumor histology. Trials have evolved as well with master protocol designs. By blending translational and clinical science, basket trials in particular are well-suited to investigate and develop targeted therapies among multiple cancer histologies. However, basket trials intrinsically involve more complex design decisions, including issues of multiple testing across baskets, and guidance for investigators is needed. METHODS: The sensitivity of the multisource exchangeability model to prior specification under differing degrees of response heterogeneity is explored through simulation. Then, a multisource exchangeability model design that incorporates control of the false-discovery rate is presented and a simulation study compares the operating characteristics to a design where the family-wise error rate is controlled and to the frequentist approach of treating the baskets as independent. Simulations are based on the original design of a real-world clinical trial, the SUMMIT trial, which investigated Neratinib treatment for a variety of solid tumors. The methods studied here are specific to single-arm phase II trials with binary outcomes. RESULTS: Values of prior probability of exchangeability in the multisource exchangeability model between 0.1 and 0.3 provide the best trade-offs between gain in precision and bias, especially when per-basket sample size is below 30. Application of these calibration results to a re-analysis of the SUMMIT trial showed that the breast basket exceeded the null response rate with posterior probability of 0.999 while having low posterior probability of exchangeability with all other baskets. Simulations based on the design of the SUMMIT trial revealed that there is meaningful improvement in power even in baskets with small sample size when the false-discovery rate is controlled as opposed to the family-wise error rate. For example, when only the breast basket was active, with a sample size of 25, the power was 0.76 when the false-discovery rate was controlled at 0.05 but only 0.56 when the family-wise error rate was controlled at 0.05, indicating that impractical sample sizes for the phase II setting would be needed to achieve acceptable power while controlling the family-wise error rate in this setting of a trial with 10 baskets. CONCLUSION: Selection of the prior exchangeability probability based on calibration and incorporation of false-discovery rate control result in multisource exchangeability model designs with high power to detect promising treatments in the context of phase II basket trials.

Individual differences in task-unrelated thought in university classrooms.

This study investigated what academic traits, attitudes, and habits predict individual differences in task-unrelated thought (TUT) during lectures, and whether this TUT propensity mediates associations between academic individual differences and course outcomes (final grade and situational interest evoked by material). Undergraduates (N = 851) from ten psychology classes at two US universities responded to thought probes presented during two early-course lectures; they also indicated sitting in the front, middle, or back of the classroom. At each probe, students categorized their thought content, such as indicating on-task thought or TUT. Students also completed online, academic-self-report questionnaires at the beginning of the course and a situational interest questionnaire at the end. Average TUT rate was 24% but individuals' rates varied widely (SD = 18%). TUT rates also increased substantially from the front to back of the classroom, and modestly from the first to second half of class periods. Multiple-group analyses (with ten classroom groups) indicated that: (a) classroom media-multitasking habits, initial interest in the course topic, and everyday propensity for mind-wandering and boredom accounted for unique variance in TUT rate (beyond other predictors); (b) TUT rate accounted for unique (modest) variance in course grades and situational interest; and (c) classroom media multitasking and propensity for mind-wandering and boredom had indirect associations with course grades via TUT rate, and these predictor variables, along with initial interest, had indirect associations with end-of-term situational interest via TUT rate. Some academic traits and behaviors predict course outcomes in part because they predict off-task thought during class.

Robust prediction of individual creative ability from brain functional connectivity.

People's ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis-connectome-based predictive modeling-to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences (r = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems-intrinsic functional networks that tend to work in opposition-suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.

Testing the construct validity of competing measurement approaches to probed mind-wandering reports.

Psychology faces a measurement crisis, and mind-wandering research is not immune. The present study explored the construct validity of probed mind-wandering reports (i.e., reports of task-unrelated thought [TUT]) with a combined experimental and individual-differences approach. We examined laboratory data from over 1000 undergraduates at two U.S. institutions, who responded to one of four different thought-probe types across two cognitive tasks. We asked a fundamental measurement question: Do different probe types yield different results, either in terms of average reports (average TUT rates, TUT-report confidence ratings), or in terms of TUT-report associations, such as TUT rate or confidence stability across tasks, or between TUT reports and other consciousness-related constructs (retrospective mind-wandering ratings, executive-control performance, and broad questionnaire trait assessments of distractibility-restlessness and positive-constructive daydreaming)? Our primary analyses compared probes that asked subjects to report on different dimensions of experience: TUT-content probes asked about what they'd been mind-wandering about, TUT-intentionality probes asked about why they were mind-wandering, and TUT-depth probes asked about the extent (on a rating scale) of their mind-wandering. Our secondary analyses compared thought-content probes that did versus didn't offer an option to report performance-evaluative thoughts. Our findings provide some "good news"-that some mind-wandering findings are robust across probing methods-and some "bad news"-that some findings are not robust across methods and that some commonly used probing methods may not tell us what we think they do. Our results lead us to provisionally recommend content-report probes rather than intentionality- or depth-report probes for most mind-wandering research.

Interpolated retrieval effects on list isolation: Individual differences in working memory capacity.

We examined the effects of interpolated retrieval from long-term memory (LTM) and short-term memory (STM) on list isolation in dual-list free recall and whether individual differences in working memory capacity (WMC) moderated those effects. Ninety-seven subjects completed study-test trials that included two study lists separated by either an exemplar generation task (LTM retrieval) or a two-back task (STM retrieval). Subjects then completed an externalized free recall task that allowed for the examination of response accessibility and monitoring. Individual differences in WMC were assessed using three complex span tasks: operation span, reading span, and rotation span. Correct recall and intratrial intrusion summary scores showed no effect of interpolated retrieval on either response accessibility or monitoring. However, serial-position curves for correct recall of List 1 showed larger primacy in the two-back than in the exemplar generation task for high-WMC subjects. We interpreted these results from a context change perspective, as showing that interpolated LTM retrieval accelerated context change for subjects who processed the context more effectively. We consider the implications of these findings for models of memory.

For Whom the Mind Wanders, and When, Varies Across Laboratory and Daily-Life Settings.

Undergraduates ( N = 274) participated in a weeklong daily-life experience-sampling study of mind wandering after being assessed in the lab for executive-control abilities (working memory capacity; attention-restraint ability; attention-constraint ability; and propensity for task-unrelated thoughts, or TUTs) and personality traits. Eight times a day, electronic devices prompted subjects to report on their current thoughts and context. Working memory capacity and attention abilities predicted subjects' TUT rates in the lab, but predicted the frequency of daily-life mind wandering only as a function of subjects' momentary attempts to concentrate. This pattern replicates prior daily-life findings but conflicts with laboratory findings. Results for personality factors also revealed different associations in the lab and daily life: Only neuroticism predicted TUT rate in the lab, but only openness predicted mind-wandering rate in daily life (both predicted the content of daily-life mind wandering). Cognitive and personality factors also predicted dimensions of everyday thought other than mind wandering, such as subjective judgments of controllability of thought. Mind wandering in people's daily environments and TUTs during controlled and artificial laboratory tasks have different correlates (and perhaps causes). Thus, mind-wandering theories based solely on lab phenomena may be incomplete.

C-terminal truncations in human 3'-5' DNA exonuclease TREX1 cause autosomal dominant retinal vasculopathy with cerebral leukodystrophy.

Autosomal dominant retinal vasculopathy with cerebral leukodystrophy is a microvascular endotheliopathy with middle-age onset. In nine families, we identified heterozygous C-terminal frameshift mutations in TREX1, which encodes a 3'-5' exonuclease. These truncated proteins retain exonuclease activity but lose normal perinuclear localization. These data have implications for the maintenance of vascular integrity in the degenerative cerebral microangiopathies leading to stroke and dementias.

Modeling risk of occupational zoonotic influenza infection in swine workers.

Zoonotic transmission of influenza A virus (IAV) between swine and workers in swine production facilities may play a role in the emergence of novel influenza strains with pandemic potential. Guidelines to prevent transmission of influenza to swine workers have been developed but there is a need for evidence-based decision-making about protective measures such as respiratory protection. A mathematical model was applied to estimate the risk of occupational IAV exposure to swine workers by contact and airborne transmission, and to evaluate the use of respirators to reduce transmission. The Markov model was used to simulate the transport and exposure of workers to IAV in a swine facility. A dose-response function was used to estimate the risk of infection. This approach is similar to methods previously used to estimate the risk of infection in human health care settings. This study uses concentration of virus in air from field measurements collected during outbreaks of influenza in commercial swine facilities, and analyzed by polymerase chain reaction. It was found that spending 25 min working in a barn during an influenza outbreak in a swine herd could be sufficient to cause zoonotic infection in a worker. However, this risk estimate was sensitive to estimates of viral infectivity to humans. Wearing an excellent fitting N95 respirator reduced this risk, but with high aerosol levels the predicted risk of infection remained high under certain assumptions. The results of this analysis indicate that under the conditions studied, swine workers are at risk of zoonotic influenza infection. The use of an N95 respirator could reduce such risk. These findings have implications for risk assessment and preventive programs targeting swine workers. The exact level of risk remains uncertain, since our model may have overestimated the viability or infectivity of IAV. Additionally, the potential for partial immunity in swine workers associated with repeated low-dose exposures or from previous infection with other influenza strains was not considered. Further studies should explore these uncertainties.

Is playing video games related to cognitive abilities?

The relations between video-game experience and cognitive abilities were examined in the current study. In two experiments, subjects performed a number of working memory, fluid intelligence, and attention-control measures and filled out a questionnaire about their video-game experience. In Experiment 1, an extreme-groups analysis indicated that experienced video-game players outperformed nonplayers on several cognitive-ability measures. However, in Experiments 1 and 2, when analyses examined the full range of subjects at both the task level and the latent-construct level, nearly all of the relations between video-game experience and cognitive abilities were near zero. These results cast doubt on recent claims that playing video games leads to enhanced cognitive abilities. Statistical and methodological issues with prior studies of video-game experience are discussed along with recommendations for future studies.

Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks.

BACKGROUND: Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power. RESULTS: We applied ARIMA and Random Forest time series models to incidence data of outbreaks of highly pathogenic avian influenza (H5N1) in Egypt, available through the online EMPRES-I system. We found that the Random Forest model outperformed the ARIMA model in predictive ability. Furthermore, we found that the Random Forest model is effective for predicting outbreaks of H5N1 in Egypt. CONCLUSIONS: Random Forest time series modeling provides enhanced predictive ability over existing time series models for the prediction of infectious disease outbreaks. This result, along with those showing the concordance between bird and human outbreaks (Rabinowitz et al. 2012), provides a new approach to predicting these dangerous outbreaks in bird populations based on existing, freely available data. Our analysis uncovers the time-series structure of outbreak severity for highly pathogenic avain influenza (H5N1) in Egypt.

Animal models of sports-related head injury: bridging the gap between pre-clinical research and clinical reality.

Sports-related head impact and injury has become a very highly contentious public health and medico-legal issue. Near-daily news accounts describe the travails of concussed athletes as they struggle with depression, sleep disorders, mood swings, and cognitive problems. Some of these individuals have developed chronic traumatic encephalopathy, a progressive and debilitating neurodegenerative disorder. Animal models have always been an integral part of the study of traumatic brain injury in humans but, historically, they have concentrated on acute, severe brain injuries. This review will describe a small number of new and emerging animal models of sports-related head injury that have the potential to increase our understanding of how multiple mild head impacts, starting in adolescence, can have serious psychiatric, cognitive and histopathological outcomes much later in life. Sports-related head injury (SRHI) has emerged as a significant public health issue as athletes can develop psychiatric and neurodegenerative disorders later in life. Animal models have always been an integral part of the study of human TBI but few existing methods are valid for studying SRHI. In this review, we propose criteria for effective animal models of SRHI. Movement of the head upon impact is judged to be of primary importance in leading to concussion and persistent CNS dysfunction.

A combined experimental-correlational approach to the construct validity of performance-based and self-report-based measures of sustained attention.

The ability to sustain attention is often measured with either objective performance indicators, like within-person RT variability, or subjective self-reports, like mind wandering propensity. A more construct valid approach, however, may be to assess the covariation in these performance and self-report measures, given that each of these is influenced by different sources of measurement error. If the correlation between performance-variability and self-report measures reflects the sustained attention construct, then task manipulations aimed at reducing the sustained attention demands of tasks should reduce the correlation between them (in addition to reducing mean levels of variability and mind wandering). The current study investigated this claim with a combined experimental-correlation approach. In two experiments (Ns ~ 1,500 each), participants completed tasks that either maximized or minimized the demand for sustained attention. Our demand manipulations successfully reduced the mean levels of sustained attention failures in both the objective and subjective measures, in both experiments. In neither experiment, however, did the covariation between these measures change as a function of the sustained attention demands of the tasks. We can therefore claim only minimal support for the construct validity of our measurement approach to sustained attention.

Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC.

The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficient drug development. This article applies recent advances in machine learning (ML) for survival analysis to a retrospective study of NSCLC patients who received definitive surgical resection and immune pathology following surgery. ML methods are compared for their effectiveness in identifying prognostic subtypes. Six survival models, including Cox regression and five survival machine learning methods, were calibrated and applied to predict survival for NSCLC patients based on PD-L1 expression, CD3 expression, and ten baseline patient characteristics. Prognostic subregions of the biomarker space are delineated for each method using synthetic patient data augmentation and compared between models for overall survival concordance. A total of 423 NSCLC patients (46% female; median age [inter quantile range]: 67 [60-73]) treated with definite surgical resection were included in the study. And 219 (52%) patients experienced events during the observation period consisting of a maximum follow-up of 10 years and median follow up 78 months. The random survival forest (RSF) achieved the highest predictive accuracy, with a C-index of 0.84. The resultant biomarker subtypes demonstrate that patients with high PD-L1 expression combined with low CD3 counts experience higher risk of death within five-years of surgical resection.

Mephedrone does not damage dopamine nerve endings of the striatum, but enhances the neurotoxicity of methamphetamine, amphetamine, and MDMA.

Mephedrone (4-methylmethcathinone) is a ß-ketoamphetamine stimulant drug of abuse with close structural and mechanistic similarities to methamphetamine. One of the most powerful actions associated with mephedrone is the ability to stimulate dopamine (DA) release and block its re-uptake through its interaction with the dopamine transporter (DAT). Although mephedrone does not cause toxicity to DA nerve endings, its ability to serve as a DAT blocker could provide protection against methamphetamine-induced neurotoxicity like other DAT inhibitors. To test this possibility, mice were treated with mephedrone (10, 20, or 40 mg/kg) prior to each injection of a neurotoxic regimen of methamphetamine (four injections of 2.5 or 5.0 mg/kg at 2 h intervals). The integrity of DA nerve endings of the striatum was assessed through measures of DA, DAT, and tyrosine hydroxylase levels. The moderate to severe DA toxicity associated with the different doses of methamphetamine was not prevented by any dose of mephedrone but was, in fact, significantly enhanced. The hyperthermia caused by combined treatment with mephedrone and methamphetamine was the same as seen after either drug alone. Mephedrone also enhanced the neurotoxic effects of amphetamine and 3,4-methylenedioxymethamphetamine on DA nerve endings. In contrast, nomifensine protected against methamphetamine-induced neurotoxicity. As mephedrone increases methamphetamine neurotoxicity, the present results suggest that it interacts with the DAT in a manner unlike that of other typical DAT inhibitors. The relatively innocuous effects of mephedrone alone on DA nerve endings mask a potentially dangerous interaction with drugs that are often co-abused with it, leading to heightened neurotoxicity.

A Compressed Language Model Embedding Dataset of ICD 10 CM Descriptions.

This paper presents novel datasets providing numerical representations of ICD-10-CM codes by generating description embeddings using a large language model followed by a dimension reduction via autoencoder. The embeddings serve as informative input features for machine learning models by capturing relationships among categories and preserving inherent context information. The model generating the data was validated in two ways. First, the dimension reduction was validated using an autoencoder, and secondly, a supervised model was created to estimate the ICD-10-CM hierarchical categories. Results show that the dimension of the data can be reduced to as few as 10 dimensions while maintaining the ability to reproduce the original embeddings, with the fidelity decreasing as the reduced-dimension representation decreases. Multiple compression levels are provided, allowing users to choose as per their requirements. The readily available datasets of ICD-10-CM codes are anticipated to be highly valuable for researchers in biomedical informatics, enabling more advanced analyses in the field. This approach has the potential to significantly improve the utility of ICD-10-CM codes in the biomedical domain.

Genetic depletion of brain 5HT reveals a common molecular pathway mediating compulsivity and impulsivity.

Neuropsychiatric disorders characterized by behavioral disinhibition, including disorders of compulsivity (e.g. obsessive-compulsive disorder; OCD) and impulse-control (e.g. impulsive aggression), are severe, highly prevalent and chronically disabling. Treatment options for these diseases are extremely limited. The pathophysiological bases of disorders of behavioral disinhibition are poorly understood but it has been suggested that serotonin dysfunction may play a role. Mice lacking the gene encoding brain tryptophan hydroxylase 2 (Tph2-/-), the initial and rate-limiting enzyme in the synthesis of serotonin, were tested in numerous behavioral assays that are well known for their utility in modeling human neuropsychiatric diseases. Mice lacking Tph2 (and brain 5HT) show intense compulsive and impulsive behaviors to include extreme aggression. The impulsivity is motor in form and not cognitive because Tph2-/- mice show normal acquisition and reversal learning on a spatial learning task. Restoration of 5HT levels by treatment of Tph2-/- mice with its immediate precursor 5-hydroxytryptophan attenuated compulsive and impulsive-aggressive behaviors. Surprisingly, in Tph2-/- mice, the lack of 5HT was not associated with anxiety-like behaviors. The results indicate that 5HT mediates behavioral disinhibition in the mammalian brain independent of anxiogenesis.