Against "silent" retractions in neuroscience.

When an academic paper is published in a journal that assigns a digital object identifier (DOI) to papers, this is a de facto fait accompli. Corrections or retractions are supposed to follow a specific protocol, especially in journals that claim to follow the Committee on Publication Ethics (COPE) guidelines. In this paper, we highlight a case of a new, fully open access neuroscience journal that claims to be COPE-compliant, yet has silently retracted two papers since all records, bibliometrics, and PDF files related to their existence have been deleted from the journal's website. Although this phenomenon does not seem to be common in the neurosciences, we consider that any opaque corrective measures in journals whose papers could be cited may negatively impact the wider neuroscience literature and community. Instead, we encourage transparency in retraction to promote truthfulness and trustworthiness.

Toward an Anti-Racist Approach to Biomedical and Neuroscience Research.

Racism is a threat to public health. Race is a sociopolitical construct that has been used for generations to create disparities in educational access, housing conditions, exposure to environmental contaminants, and access to health care. Collectively, these disparities have a negative impact on the health of non-white Americans. The National Institutes of Health (NIH) funds biomedical research, including basic neuroscience research, aimed at understanding the mechanisms and consequences of health and disease in Americans. NIH has recently acknowledged its own structural racism, the disadvantage this perpetuates in the biomedical research enterprise, and has announced its commitment to eliminating these disparities. Here, we discuss different rates of disease in U.S. citizens from different racial backgrounds. We next describe ways in which the biomedical research enterprise (1) has contributed to health disparities and (2) can contribute to the solving this problem. Based on our own scientific expertise, we use neuroscience in general and mental health/addiction disorders more specifically as examples of a broader issue. The NIH, including its neuroscience-focused Institutes, and NIH-funded scientists, including neuroscientists, should prioritize research topics that reflect the health conditions that affect all Americans, not just white Americans.

Dignity neuroscience: universal rights are rooted in human brain science.

Universal human rights are defined by international agreements, law, foreign policy, and the concept of inherent human dignity. However, rights defined on this basis can be readily subverted by overt and covert disagreements and can be treated as distant geopolitical events rather than bearing on individuals' everyday lives. A robust case for universal human rights is urgently needed and must meet several disparate requirements: (1) a framework that resolves tautological definitions reached solely by mutual, revocable agreement; (2) a rationale that transcends differences in beliefs, creed, and culture; and (3) a personalization that empowers both individuals and governments to further human rights protections. We propose that human rights in existing agreements comprise five elemental types: (1) agency, autonomy, and self-determination; (2) freedom from want; (3) freedom from fear; (4) uniqueness; and (5) unconditionality, including protections for vulnerable populations. We further propose these rights and protections are rooted in fundamental properties of the human brain. We provide a robust, empirical foundation for universal rights based on emerging work in human brain science that we term dignity neuroscience. Dignity neuroscience provides an empirical foundation to support and foster human dignity, universal rights, and their active furtherance by individuals, nations, and international law.

Development of culturally sensitive pain neuroscience education materials for Hausa-speaking patients with chronic spinal pain: A modified Delphi study.

This study aimed to develop culturally sensitive pain neuroscience education (PNE) materials for Hausa speaking patients with chronic spinal pain (CSP). PNE is a program of teaching patients about pain that has gained considerable attention in research and is increasingly used during physical therapy for patients with chronic pain. It helps in decreasing pain, disability, fear-avoidance, pain catastrophization, movement restriction, and health care utilization among patients with chronic pain. However, existing PNE materials and their application are limited to few languages and cultural inclinations. Due to the variations in pain perceptions, beliefs, and related outcomes among different population groups, culture-sensitive PNE materials addressing these outcomes are warranted. A focus-group discussion comprising 4 experts was used to adapt and develop preliminary PNE materials. Thereafter, an internet-based 3-round modified Delphi-study involving 22 experts ensued. Experts' consensus/recommendations concerning the content were used in modifying the PNE materials. Consensus was predefined as ≥75% level of (dis)agreement. Eighteen experts completed the Delphi rounds. Nineteen, 18 and 18 experts participated in rounds 1, 2 and 3 respectively, representing 86%, 94% and 100% participation rate respectively. Consensus agreement was reached in every round and content of the materials, including drawings, examples, figures and metaphors were adapted following the experts' suggestions. We therefore concluded that, culture-sensitive PNE materials for Hausa speaking patients with CSP were successfully produced. The present study also provides a direction for further research whereby the effects of culturally-sensitive PNE materials can be piloted among Hausa speaking patients with CSP.

Common functional localizers to enhance NHP & cross-species neuroscience imaging research.

Functional localizers are invaluable as they can help define regions of interest, provide cross-study comparisons, and most importantly, allow for the aggregation and meta-analyses of data across studies and laboratories. To achieve these goals within the non-human primate (NHP) imaging community, there is a pressing need for the use of standardized and validated localizers that can be readily implemented across different groups. The goal of this paper is to provide an overview of the value of localizer protocols to imaging research and we describe a number of commonly used or novel localizers within NHPs, and keys to implement them across studies. As has been shown with the aggregation of resting-state imaging data in the original PRIME-DE submissions, we believe that the field is ready to apply the same initiative for task-based functional localizers in NHP imaging. By coming together to collect large datasets across research group, implementing the same functional localizers, and sharing the localizers and data via PRIME-DE, it is now possible to fully test their robustness, selectivity and specificity. To do this, we reviewed a number of common localizers and we created a repository of well-established localizer that are easily accessible and implemented through the PRIME-RE platform.

Standardized and reproducible measurement of decision-making in mice.

Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here, we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We adopted a task for head-fixed mice that assays perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path toward achieving reproducibility in neuroscience through collaborative open-science approaches.

In science, it is of vital importance that multiple studies corroborate the same result. Researchers therefore need to know all the details of previous experiments in order to implement the procedures as exactly as possible. However, this is becoming a major problem in neuroscience, as animal studies of behavior have proven to be hard to reproduce, and most experiments are never replicated by other laboratories. Mice are increasingly being used to study the neural mechanisms of decision making, taking advantage of the genetic, imaging and physiological tools that are available for mouse brains. Yet, the lack of standardized behavioral assays is leading to inconsistent results between laboratories. This makes it challenging to carry out large-scale collaborations which have led to massive breakthroughs in other fields such as physics and genetics. To help make these studies more reproducible, the International Brain Laboratory (a collaborative research group) et al. developed a standardized approach for investigating decision making in mice that incorporates every step of the process; from the training protocol to the software used to analyze the data. In the experiment, mice were shown images with different contrast and had to indicate, using a steering wheel, whether it appeared on their right or left. The mice then received a drop of sugar water for every correction decision. When the image contrast was high, mice could rely on their vision. However, when the image contrast was very low or zero, they needed to consider the information of previous trials and choose the side that had recently appeared more frequently. This method was used to train 140 mice in seven laboratories from three different countries. The results showed that learning speed was different across mice and laboratories, but once training was complete the mice behaved consistently, relying on visual stimuli or experiences to guide their choices in a similar way. These results show that complex behaviors in mice can be reproduced across multiple laboratories, providing an unprecedented dataset and open-access tools for studying decision making. This work could serve as a foundation for other groups, paving the way to a more collaborative approach in the field of neuroscience that could help to tackle complex research challenges.

Revisiting the global workspace orchestrating the hierarchical organization of the human brain.

A central challenge in neuroscience is how the brain organizes the information necessary to orchestrate behaviour. Arguably, this whole-brain orchestration is carried out by a core subset of integrative brain regions, a 'global workspace', but its constitutive regions remain unclear. We quantified the global workspace as the common regions across seven tasks as well as rest, in a common 'functional rich club'. To identify this functional rich club, we determined the information flow between brain regions by means of a normalized directed transfer entropy framework applied to multimodal neuroimaging data from 1,003 healthy participants and validated in participants with retest data. This revealed a set of regions orchestrating information from perceptual, long-term memory, evaluative and attentional systems. We confirmed the causal significance and robustness of our results by systematically lesioning a generative whole-brain model. Overall, this framework describes a complex choreography of the functional hierarchical organization of the human brain.

Interactive rhythms in the wild, in the brain, and in silico.

There are some historical divisions in methods, rationales, and purposes between studies on comparative cognition and behavioural ecology. In turn, the interaction between these two branches and studies from mathematics, computation, and neuroscience is not usual. In this short piece, we attempt to build bridges among these disciplines. We present a series of interconnected vignettes meant to illustrate what a more interdisciplinary approach looks like when successful, and its advantages. Concretely, we focus on a recent topic, namely animal rhythms in interaction, studied under different approaches. We showcase 5 research efforts, which we believe successfully link 5 particular scientific areas of rhythm research conceptualised as the following: social neuroscience, detailed rhythmic quantification, ontogeny, computational approaches, and spontaneous interactions. Our suggestions will hopefully spur a "comparative rhythms in interaction" field, which can integrate and capitalize on knowledge from zoology, comparative psychology, neuroscience, and computation. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Reserved Bed Program Reduces Neurosciences Intensive Care Unit Capacity Strain: An Implementation Study.

BACKGROUND: Neurosciences intensive care units (NICUs) provide institutional centers for specialized care. Despite a demonstrable reduction in morbidity and mortality, NICUs may experience significant capacity strain with resulting supraoptimal utilization and diseconomies of scale. We present an implementation study in the recognition and management of capacity strain within a large NICU in the United States. Excessive resource demand in an NICU creates significant operational issues. OBJECTIVE: To evaluate the efficacy of a Reserved Bed Pilot Program (RBPP), implemented to maximize economies of scale, to reduce transfer declines due to lack of capacity, and to increase transfer volume for the neurosciences service-line. METHODS: Key performance indicators (KPIs) were created to evaluate RBPP efficacy with respect to primary (strategic) objectives. Operational KPIs were established to evaluate changes in operational throughput for the neurosciences and other service-lines. For each KPI, pilot-period data were compared to the previous fiscal year. RESULTS: RBPP implementation resulted in a significant increase in accepted transfer volume to the neurosciences service-line (P = .02). Transfer declines due to capacity decreased significantly (P = .01). Unit utilization significantly improved across service-line units relative to theoretical optima (P < .03). Care regionalization was achieved through a significant reduction in "off-service" patient placement (P = .01). Negative externalities were minimized, with no significant negative impact in the operational KPIs of other evaluated service-lines (P = .11). CONCLUSION: Capacity strain is a significant issue for hospital units. Reducing capacity strain can increase unit efficiency, improve resource utilization, and augment service-line throughput. RBPP implementation resulted in a significant improvement in service-line operations, regional access to care, and resource efficiency, with minimal externalities at the institutional level.

Doing the locomotion: Insights and potential pitfalls associated with using locomotor activity as a readout of the circadian rhythm in larval zebrafish.

BACKGROUND: Zebrafish have been used as a model to study circadian rhythms (CRs) for over 20 years by analyzing various endpoints including locomotor activity. Such studies often utilize high-throughput analysis monitoring activity of larvae placed in well plates numbering >48 wells per plate. Although the CR can be influenced by numerous factors, it is not clear if such effects are permanent. Here, we investigated the variability of CRs of larvae analyzed in different types of well plates and determined the permanency of experimentally-induced aberrations in CRs. NEW METHOD: Utilized the tracking software Ethovision XT to investigate how different well plate sizes influence the CR. Re-tested subjects for recovery from long-term CR disruptions and evaluated CR patterns at the individual level. RESULTS: CR tracking using locomotion as a readout is best in 24 well plates. CR consistency is not maintained in larvae tracked in 48 or 96 well plates. A perturbed CR due to constant light recovered after just 3 days of a normal light/dark cycle. COMPARISON WITH EXISTING METHODS: Unlike other CR locomotor-based assays, our approach allowed for a medium-throughput analysis of individual CRs, minimized variability and allowed for the re-evaluation of larval CRs 4-5 days later. CONCLUSIONS: This medium-throughput locomotor CR analysis allows for a standardized, less variable approach whereby larvae can be re-tested to identify potential long-term changes after experimental manipulations. Long-term behavioral experiments in 48 or 96 well plates may impart stress on the larvae due to space constraints which could impact nervous system function and/or behavior.

Description and validation of the LocoWhisk system: Quantifying rodent exploratory, sensory and motor behaviours.

BACKGROUND: Previous studies have demonstrated that analysing whisker movements and locomotion allows us to quantify the behavioural consequences of sensory, motor and cognitive deficits in rodents. Independent whisker and feet trackers exist but there is no fully-automated, open-source software and hardware solution, that measures both whisker movements and gait. NEW METHOD: We present the LocoWhisk arena and new accompanying software (ARTv2) that allows the automatic detection and measurement of both whisker and gait information from high-speed video footage. RESULTS: We demonstrate the new whisker and foot detector algorithms on high-speed video footage of freely moving small mammals, and show that whisker movement and gait measurements collected in the LocoWhisk arena are similar to previously reported values in the literature. COMPARISON WITH EXISTING METHOD(S): We demonstrate that the whisker and foot detector algorithms, are comparable in accuracy, and in some cases significantly better, than readily available software and manual trackers. CONCLUSION: The LocoWhisk system enables the collection of quantitative data from whisker movements and locomotion in freely behaving rodents. The software automatically records both whisker and gait information and provides added statistical tools to analyse the data. We hope the LocoWhisk system and software will serve as a solid foundation from which to support future research in whisker and gait analysis.

Does data cleaning improve brain state classification?

BACKGROUND: Neuroscientists routinely seek to identify and remove noisy or artifactual observations from their data. They do so with the belief that removing such data improves power to detect relations between neural activity and behavior, which are often subtle and can be overwhelmed by noise. Whereas standard methods can exclude certain well-defined noise sources (e.g., 50/60 Hz electrical noise), in many situations there is not a clear difference between noise and signals so it is not obvious how to separate the two. Here we ask whether methods routinely used to "clean" human electrophysiological recordings lead to greater power to detect brain-behavior relations. NEW METHOD: This, to the authors' knowledge, is the first large-scale simultaneous evaluation of multiple commonly used methods for removing noise from intracranial EEG recordings. RESULTS: We find that several commonly used data cleaning methods (automated methods based on statistical signal properties and manual methods based on expert review) do not increase the power to detect univariate and multivariate electrophysiological biomarkers of successful episodic memory encoding, a well-characterized broadband pattern of neural activity observed across the brain. COMPARISON WITH EXISTING METHODS: Researchers may be more likely to increase statistical power to detect physiological phenomena of interest by allocating resources away from cleaning noisy data and toward collecting more within-patient observations. CONCLUSIONS: These findings highlight the challenge of partitioning signal and noise in the analysis of brain-behavior relations, and suggest increasing sample size and numbers of observations, rather than data cleaning, as the best approach to improving statistical power.

Utilizing supervised machine learning to identify microglia and astrocytes in situ: implications for large-scale image analysis and quantification.

BACKGROUND: The evaluation of histological tissue samples plays a crucial role in deciphering preclinical disease and injury mechanisms. High-resolution images can be obtained quickly however data acquisition are often bottlenecked by manual analysis methodologies. NEW METHOD: We describe and validate a pipeline for a novel machine learning-based analytical method, using the Opera High-Content Screening system and Harmony software, allowing for detailed image analysis of cellular markers in histological samples. RESULTS: To validate the machine learning pipeline, analyses of single proteins in mouse brain sections were utilized. To demonstrate adaptability of the pipeline for multiple cell types and epitopes, the percent brain coverage of microglial cells, identified by ionized calcium binding adaptors molecule 1 (Iba1), and of astrocytes, by glial fibrillary acidic protein (GFAP) demonstrated no significant differences between automated and manual analyses protocols. Further to examine the robustness of this protocol for multiple proteins simultaneously labeling of rat brain sections were utilized; co-localization of astrocytic endfeet on blood vessels, using aquaporin-4 and tomato lectin respectively, were efficiently identified and quantified by the novel pipeline and were not significantly different between the two analyses protocols. Comparison with Existing Methods: The automated platform maintained the sensitivity and accuracy of manual analysis, while accomplishing the analyses in 1/200th of the time. CONCLUSIONS: We demonstrate the benefits and potential of adapting an automated high-throughput machine-learning analytical approach for the analysis ofin situ tissue samples, show effectiveness across different animal models, while reducing analysis time and increasing productivity.