Sex bias and omission in neuroscience research is influenced by research model and journal, but not reported NIH funding.

Neuroscience research has historically demonstrated sex bias that favors male over female research subjects, as well as sex omission, which is the lack of reporting sex. Here we analyzed the status of sex bias and omission in neuroscience research published across six different journals in 2017. Regarding sex omission, 16% of articles did not report sex. Regarding sex bias, 52% of neuroscience articles reported using both males and females, albeit only 15% of articles using both males and females reported assessing sex as an experimental variable. Overrepresentation of the sole use of males compared to females persisted (26% versus 5%, respectively). Sex bias and omission differed across research models, but not by reported NIH funding status. Sex omission differed across journals. These findings represent the latest information regarding the complex status of sex in neuroscience research and illustrate the continued need for thoughtful and informed action to enhance scientific discovery.

Scientific productivity in neurosciences in Latin America: a scientometrics perspective.

Background: Neurosciences research has increased significantly in recent years around the world. It has led to the development of interdisciplinary work, moving from activities from isolated fields (such as biology, psychology or neurology) to research that involves different scientific perspectives. In developing regions, such as Latin America, it has additional challenges, related to available funding and infrastructure.Aim: To analyze key factors in scientific productivity in neurosciences in Latin America.Methods: A bibliometric analysis of the scientific productivity in neurosciences in main five Latin American countries (Argentina, Brazil, Chile, Colombia and Mexico) was carried out.Results: Brazil was the largest producer of scientific articles, and receptor of citations, in neurosciences in 1998-2017, followed by Mexico. We identified highly cited papers, top institutions, networks of authors, main journals and key areas in neurosciences for this period in the 5 countries.Conclusions: Scientific productivity in neurosciences in Latin America would benefit from the consolidation of more regional, interdisciplinary and international research networks. In this work, we discuss key elements for the consolidation of neurosciences research in Latin America.

Specialty Classifications of Physicians Who Provide Neurocritical Care in the United States.

BACKGROUND: We sought to characterize the specialty classification of US physicians who provide critical care for neurological/neurosurgical disease. METHODS: Using inpatient claims between 2009 and 2015 from a nationally representative 5% sample of Medicare beneficiaries, we selected hospitalizations for neurological/neurosurgical diseases with potential to result in life-threatening manifestations requiring critical care. Using Current Procedural Terminology® codes, we determined the medical specialty of providers submitting critical care claims, and, using National Provider Identifier numbers, we merged in data from the United Council for Neurologic Subspecialties (UCNS) to determine whether the provider was a UCNS diplomate in neurocritical care. We defined providers with a clinical neuroscience background as neurologists, neurosurgeons, and/or UCNS diplomates in neurocritical care. We defined neurocritical care service as a critical care claim with a qualifying neurological/neurosurgical diagnosis in patients with a relevant primary hospital discharge diagnosis and ≥ 3 total critical care claims, excluding claims from the first day of hospitalization since these were mostly emergency-department claims. Our findings were reported using descriptive statistics with exact confidence intervals (CI). RESULTS: Among 1,952,305 Medicare beneficiaries, we identified 99,937 hospitalizations with at least one claim for neurocritical care. In our primary analysis, neurologists accounted for 28.0% (95% CI, 27.5-28.5%) of claims, neurosurgeons for 3.7% (95% CI, 3.5-3.9%), UCNS-certified neurointensivists for 25.8% (95% CI, 25.3-26.3%), and providers with any clinical neuroscience background for 42.8% (95% CI, 42.2-43.3%). The likelihood of management by physicians with a clinical neuroscience background increased proportionally with patients' county-level socioeconomic status and such providers were 3 times more likely to be based at an academic medical center than other physicians who billed for critical care in our sample (odds ratio, 2.9; 95% CI, 1.1-8.1). CONCLUSIONS: Physicians with a dedicated clinical neuroscience background accounted for less than half of neurocritical care service in US Medicare beneficiaries.

Power-up: A Reanalysis of 'Power Failure' in Neuroscience Using Mixture Modeling.

Recently, evidence for endemically low statistical power has cast neuroscience findings into doubt. If low statistical power plagues neuroscience, then this reduces confidence in the reported effects. However, if statistical power is not uniformly low, then such blanket mistrust might not be warranted. Here, we provide a different perspective on this issue, analyzing data from an influential study reporting a median power of 21% across 49 meta-analyses (Button et al., 2013). We demonstrate, using Gaussian mixture modeling, that the sample of 730 studies included in that analysis comprises several subcomponents so the use of a single summary statistic is insufficient to characterize the nature of the distribution. We find that statistical power is extremely low for studies included in meta-analyses that reported a null result and that it varies substantially across subfields of neuroscience, with particularly low power in candidate gene association studies. Therefore, whereas power in neuroscience remains a critical issue, the notion that studies are systematically underpowered is not the full story: low power is far from a universal problem.SIGNIFICANCE STATEMENT Recently, researchers across the biomedical and psychological sciences have become concerned with the reliability of results. One marker for reliability is statistical power: the probability of finding a statistically significant result given that the effect exists. Previous evidence suggests that statistical power is low across the field of neuroscience. Our results present a more comprehensive picture of statistical power in neuroscience: on average, studies are indeed underpowered-some very seriously so-but many studies show acceptable or even exemplary statistical power. We show that this heterogeneity in statistical power is common across most subfields in neuroscience. This new, more nuanced picture of statistical power in neuroscience could affect not only scientific understanding, but potentially policy and funding decisions for neuroscience research.

Time, frequency, and time-varying Granger-causality measures in neuroscience.

This article proposes a systematic methodological review and an objective criticism of existing methods enabling the derivation of time, frequency, and time-varying Granger-causality statistics in neuroscience. The capacity to describe the causal links between signals recorded at different brain locations during a neuroscience experiment is indeed of primary interest for neuroscientists, who often have very precise prior hypotheses about the relationships between recorded brain signals. The increasing interest and the huge number of publications related to this topic calls for this systematic review, which describes the very complex methodological aspects underlying the derivation of these statistics. In this article, we first present a general framework that allows us to review and compare Granger-causality statistics in the time domain, and the link with transfer entropy. Then, the spectral and the time-varying extensions are exposed and discussed together with their estimation and distributional properties. Although not the focus of this article, partial and conditional Granger causality, dynamical causal modelling, directed transfer function, directed coherence, partial directed coherence, and their variant are also mentioned.

The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample.

This paper describes the data repository for the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) initial study cohort. The Cam-CAN Stage 2 repository contains multi-modal (MRI, MEG, and cognitive-behavioural) data from a large (approximately N=700), cross-sectional adult lifespan (18-87years old) population-based sample. The study is designed to characterise age-related changes in cognition and brain structure and function, and to uncover the neurocognitive mechanisms that support healthy cognitive ageing. The database contains raw and preprocessed structural MRI, functional MRI (active tasks and resting state), and MEG data (active tasks and resting state), as well as derived scores from cognitive behavioural experiments spanning five broad domains (attention, emotion, action, language, and memory), and demographic and neuropsychological data. The dataset thus provides a depth of neurocognitive phenotyping that is currently unparalleled, enabling integrative analyses of age-related changes in brain structure, brain function, and cognition, and providing a testbed for novel analyses of multi-modal neuroimaging data.

Undergraduate Neuroscience Majors: A Missed Opportunity for Psychiatry Workforce Development.

OBJECTIVE: This study sought to determine whether and to what extent medical students with an undergraduate college major in neuroscience, relative to other college majors, pursue psychiatry relative to other brain-based specialties (neurology and neurosurgery) and internal medicine. METHODS: The authors analyzed data from AAMC matriculation and graduation surveys for all students who graduated from US medical schools in 2013 and 2014 (n = 29,714). Students who majored in neuroscience, psychology, and biology were compared to all other students in terms of their specialty choice at both time points. For each major, the authors determined rates of specialty choice of psychiatry, neurology, neurosurgery, and, for comparison, internal medicine. This study employed Chi-square statistic to compare odds of various specialty choices among different majors. RESULTS: Among medical students with an undergraduate neuroscience major (3.5% of all medical students), only 2.3% preferred psychiatry at matriculation, compared to 21.5% who chose neurology, 13.1% neurosurgery, and 11% internal medicine. By graduation, psychiatry specialty choice increased to 5.1% among neuroscience majors while choice of neurology and neurosurgery declined. Psychology majors (OR = 3.16, 95% CI 2.60-4.47) but not neuroscience majors (OR 1.28, 0.92-1.77) were more likely than their peers to choose psychiatry. CONCLUSIONS: Psychiatry struggles to attract neuroscience majors to the specialty. This missed opportunity is an obstacle to developing the neuroscience literacy of the workforce and jeopardizes the neuroscientific future of our field. Several potential strategies to address the recruitment challenges exist.

Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: The importance of constructing transfer function autoregressive models.

Many articles on perception, performance, psychophysiology, and neuroscience seek to relate pairs of time series through assessments of their cross-correlations. Most such series are individually autocorrelated: they do not comprise independent values. Given this situation, an unfounded reliance is often placed on cross-correlation as an indicator of relationships (e.g., referent vs. response, leading vs. following). Such cross-correlations can indicate spurious relationships, because of autocorrelation. Given these dangers, we here simulated how and why such spurious conclusions can arise, to provide an approach to resolving them. We show that when multiple pairs of series are aggregated in several different ways for a cross-correlation analysis, problems remain. Finally, even a genuine cross-correlation function does not answer key motivating questions, such as whether there are likely causal relationships between the series. Thus, we illustrate how to obtain a transfer function describing such relationships, informed by any genuine cross-correlations. We illustrate the confounds and the meaningful transfer functions by two concrete examples, one each in perception and performance, together with key elements of the R software code needed. The approach involves autocorrelation functions, the establishment of stationarity, prewhitening, the determination of cross-correlation functions, the assessment of Granger causality, and autoregressive model development. Autocorrelation also limits the interpretability of other measures of possible relationships between pairs of time series, such as mutual information. We emphasize that further complexity may be required as the appropriate analysis is pursued fully, and that causal intervention experiments will likely also be needed.

[Magnetic resonance imaging. Density equalizing mapping analysis of global research architecture]. / Magnetresonanztomographie. Eine Density-equalizing-mapping-Analyse der globalen Forschungsarchitektur.

BACKGROUND: Despite the great medical importance, there is still no comprehensive scientometric analysis regarding the results of magnetic resonance imaging (MRI) and the development of the importance for the healthcare system. AIMS: This paper evaluated and analyzed the entire research publication results on the topic of MRI for the period 1981-2007 based on scientometric methods and parameters. MATERIAL AND METHODS: A scientometric analysis (database: ISI Web of Science 1981-2007, search terms MRI and magnetic resonance imaging) was performed. The following parameters were analyzed: number of publications, countries of publication, number of citations, citation rate and collaborations, using various analytical and display techniques, including density equalizing map projections. RESULTS: Most of the 49,122 publications on MRI could be attributed to the USA (32.5 %), which also has the most cooperative collaborations. Within Europe, Germany (10.3 %) is the country with the highest number of publications followed by the UK (9.3 %). The western industrialized nations dominate over the rest of the world in terms of scientific developments of MRI. The thematic focus of the publications lies in the fields of radiology and neuroscience. In addition to the journal Neurology most scientific articles were published in Magnetic Resonance in Medicine and Circulation. DISCUSSION: The results show that the current trend is continuing and the scientific interest in MRI is continuously increasing.

Iranians' contribution to world literature on neuroscience.

OBJECTIVE: The purpose of this study is to analyse Iranian scientific publications in the neuroscience subfields by librarians and neuroscientists, using Science Citation Index Expanded (SCIE) via Web of Science data over the period, 2002-2008. METHODS: Data were retrieved from the SCIE. Data were collected from the 'subject area' of the database and classified by neuroscience experts into 14 subfields. To identify the citation patterns, we applied the 'impact factor' and the 'number of publication'. Data were also analysed using HISTCITE, Excel 2007 and SPSS. RESULTS: Seven hundred and thirty-four papers have been published by Iranian between 2002 and 2008. Findings showed a growing trend of neuroscience papers in the last 3 years with most papers (264) classified in the neuropharmacology subfield. There were fewer papers in neurohistory, psychopharmacology and artificial intelligence. International contributions of authors were mostly in the neurology subfield, and 'Collaboration Coefficient' for the neuroscience subfields in Iran was 0.686 which is acceptable. Most international collaboration between Iranians and developed countries was from USA. Eighty-seven percent of the published papers were in journals with the impact factor between 0 and 4; 25% of papers were published by the researchers affiliated to Tehran University of Medical Sciences. CONCLUSION: Progress of neuroscience in Iran is mostly seen in the neuropharmacology and the neurology subfields. Other subfields should also be considered as a research priority by health policymakers. As this study was carried out by the collaboration of librarians and neuroscientists, it has been proved valuable for both librarians and policymakers. This study may be encouraging for librarians from other developing countries.

An R package for state-trace analysis.

State-trace analysis (Bamber, Journal of Mathematical Psychology, 19, 137-181, 1979) is a graphical analysis that can determine whether one or more than one latent variable mediates an apparent dissociation between the effects of two experimental manipulations. State-trace analysis makes only ordinal assumptions and so, is not confounded by range effects that plague alternative methods, especially when performance is measured on a bounded scale (such as accuracy). We describe and illustrate the application of a freely available GUI driven package, StateTrace, for the R language. StateTrace automates many aspects of a state-trace analysis of accuracy and other binary response data, including customizable graphics and the efficient management of computationally intensive Bayesian methods for quantifying evidence about the outcomes of a state-trace experiment, developed by Prince, Brown, and Heathcote (Psychological Methods, 17, 78-99, 2012).

Wiener-Granger causality: a well established methodology.

For decades, the main ways to study the effect of one part of the nervous system upon another have been either to stimulate or lesion the first part and investigate the outcome in the second. This article describes a fundamentally different approach to identifying causal connectivity in neuroscience: a focus on the predictability of ongoing activity in one part from that in another. This approach was made possible by a new method that comes from the pioneering work of Wiener (1956) and Granger (1969). The Wiener-Granger method, unlike stimulation and ablation, does not require direct intervention in the nervous system. Rather, it relies on the estimation of causal statistical influences between simultaneously recorded neural time series data, either in the absence of identifiable behavioral events or in the context of task performance. Causality in the Wiener-Granger sense is based on the statistical predictability of one time series that derives from knowledge of one or more others. This article defines Wiener-Granger Causality, discusses its merits and limitations in neuroscience, and outlines recent developments in its implementation.

Bayesian inference of population prevalence.

Within neuroscience, psychology, and neuroimaging, the most frequently used statistical approach is null hypothesis significance testing (NHST) of the population mean. An alternative approach is to perform NHST within individual participants and then infer, from the proportion of participants showing an effect, the prevalence of that effect in the population. We propose a novel Bayesian method to estimate such population prevalence that offers several advantages over population mean NHST. This method provides a population-level inference that is currently missing from study designs with small participant numbers, such as in traditional psychophysics and in precision imaging. Bayesian prevalence delivers a quantitative population estimate with associated uncertainty instead of reducing an experiment to a binary inference. Bayesian prevalence is widely applicable to a broad range of studies in neuroscience, psychology, and neuroimaging. Its emphasis on detecting effects within individual participants can also help address replicability issues in these fields.

Scientists use statistical tools to evaluate observations or measurements from carefully designed experiments. In psychology and neuroscience, these experiments involve studying a randomly selected group of people, looking for patterns in their behaviour or brain activity, to infer things about the population at large. The usual method for evaluating the results of these experiments is to carry out null hypothesis statistical testing (NHST) on the population mean ­ that is, the average effect in the population that the study participants were selected from. The test asks whether the observed results in the group studied differ from what might be expected if the average effect in the population was zero. However, in psychology and neuroscience studies, people's brain activity and performance on cognitive tasks can differ a lot. This means important effects in individuals can be lost in the overall population average. Ince et al. propose that this shortcoming of NHST can be overcome by shifting the statistical analysis away from the population mean, and instead focusing on effects in individual participants. This led them to create a new statistical approach named Bayesian prevalence. The method looks at effects within each individual in the study and asks how likely it would be to see the same result if the experiment was repeated with a new person chosen from the wider population at random. Using this approach, it is possible to quantify how typical or uncommon an observed effect is in the population, and the uncertainty around this estimate. This differs from NHST which only provides a binary 'yes or no' answer to the question, 'does this experiment provide sufficient evidence that the average effect in the population is not zero?' Another benefit of Bayesian prevalence is that it can be applied to studies with small numbers of participants which cannot be analysed using other statistical methods. Ince et al. show that the Bayesian prevalence can be applied to a range of psychology and neuroimaging experiments, from brain imaging to electrophysiology studies. Using this alternative statistical method could help address issues of replication in these fields where NHST results are sometimes not the same when studies are repeated.

Alzheimer's disease research: scientific productivity and impact of the top 100 investigators in the field.

The online availability of scientific-literature databases and natural-language-processing (NLP) algorithms has enabled large-scale bibliometric studies within the field of scientometrics. Using NLP techniques and Thomson ISI reports, an initial analysis of the role of Alzheimer's disease (AD) within the neurosciences as well as a summary of the various research foci within the AD scientific community are presented. Citation analyses and productivity filters are applied to post-1984, AD-specific subsets of the PubMed and Thomson ISI Web-of-Science literature bases to algorithmically identify a pool of the top AD researchers. From the initial pool of AD investigators, top-100 rankings are compiled to assess productivity and impact. One of the impact and productivity metrics employed is an AD-specific H-index. Within the AD-specific H-index ranking, there are many cases of multiple AD investigators with similar or identical H-indices. In order to facilitate differentiation among investigators with equal or near-equal H indices, two derivatives of the H-index are proposed: the Second-Tier H-index and the Scientific Following H-index. Winners of two prestigious AD-research awards are highlighted, membership to the Institute of Medicine of the US National Academy of Sciences is acknowledged, and an analysis of highly-productive, high-impact, AD-research collaborations is presented.

A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback.

Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how behaviorally relevant adaptive changes in complex networks of spiking neurons could be achieved in a self-organizing manner through local synaptic plasticity. However, the capabilities and limitations of this learning rule could so far only be tested through computer simulations. This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. Our model for this experiment relies on a combination of reward-modulated STDP with variable spontaneous firing activity. Hence it also provides a possible functional explanation for trial-to-trial variability, which is characteristic for cortical networks of neurons but has no analogue in currently existing artificial computing systems. In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics.

The international dimensions of neuroethics.

Neuroethics, in its modern form, investigates the impact of brain science in four basic dimensions: the self, social policy, practice and discourse. In this study, we analyzed a set of 461 peer-reviewed articles with neuroethics content, published by authors from 32 countries. We analyzed the data for: (1) trends in the development of international neuroethics over time, and (2) how challenges at the intersection of ethics and neuroscience are viewed in countries that are considered developed by International Monetary Fund (IMF) standards, and in those that are developing. Our results demonstrate a steady increase in global participation in neuroethics from 1989 to 2005, characterized by an increase in numbers of articles published specifically on neuroethics, journals publishing these articles, and countries contributing to the literature. The focus from all countries was on the practice of brain science and the amelioration of neurological disease. Indicators of technology creation and diffusion in developing countries were specifically correlated with increases in publications concerning policy implications of brain science. Neuroethics is an international endeavor and, as such, should be sensitive to the impact that context has on acceptance and use of technological innovation.