Ordinal methods for a characterization of evolving functional brain networks.

Ordinal time series analysis is based on the idea to map time series to ordinal patterns, i.e., order relations between the values of a time series and not the values themselves, as introduced in 2002 by C. Bandt and B. Pompe. Despite a resulting loss of information, this approach captures meaningful information about the temporal structure of the underlying system dynamics as well as about properties of interactions between coupled systems. This-together with its conceptual simplicity and robustness against measurement noise-makes ordinal time series analysis well suited to improve characterization of the still poorly understood spatiotemporal dynamics of the human brain. This minireview briefly summarizes the state-of-the-art of uni- and bivariate ordinal time-series-analysis techniques together with applications in the neurosciences. It will highlight current limitations to stimulate further developments, which would be necessary to advance characterization of evolving functional brain networks.

An International Study on Violence and Aggression in Neuroscience Nursing.

ABSTRACT: INTRODUCTION: The primary rationale for this study was to evaluate neuroscience registered nurses' (RNs') experience of violence and aggression internationally. The objectives were to determine how prevalent violence and aggression is in neuroscience nursing and the support and education provided. METHODS: Two online surveys were developed for neuroscience RN educators and RNs through SurveyMonkey. The questionnaires were distributed through RN organizations internationally in 2021. RESULTS: Two hundred seventy-two RNs responded to the survey. Most staff felt safe at work but had experienced some type of violence or aggression, with most experiencing verbal aggression. A variety of support existed for staff but lacked consistency, with some of the staff receiving more support from colleagues than their manager. Most RNs had received education in the management of violence and aggression, but some mentioned this was not specific to their neuroscience patient population, and most required further training. Thirty-one RN educators completed the survey, and the results were similar to those of the RN for education provision. Surprisingly, many neuroscience areas did not have a code system or personal alarms to alert staff to a violence or aggression emergency. CONCLUSION: Most RNs felt safe at work despite the high prevalence of violence and aggression experienced. Education was considered beneficial, but they desired more and further research into effective RN education. Effective support post incident needs to be determined. Protection for staff is paramount: few areas had code systems to raise an alarm for agitation, and few places of work had personal alarms that, if implemented, might enable a quicker response to prevent harm.

Evolution of neural activity in circuits bridging sensory and abstract knowledge.

The ability to associate sensory stimuli with abstract classes is critical for survival. How are these associations implemented in brain circuits? And what governs how neural activity evolves during abstract knowledge acquisition? To investigate these questions, we consider a circuit model that learns to map sensory input to abstract classes via gradient-descent synaptic plasticity. We focus on typical neuroscience tasks (simple, and context-dependent, categorization), and study how both synaptic connectivity and neural activity evolve during learning. To make contact with the current generation of experiments, we analyze activity via standard measures such as selectivity, correlations, and tuning symmetry. We find that the model is able to recapitulate experimental observations, including seemingly disparate ones. We determine how, in the model, the behaviour of these measures depends on details of the circuit and the task. These dependencies make experimentally testable predictions about the circuitry supporting abstract knowledge acquisition in the brain.

NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML.

As researchers develop computational models of neural systems with increasing sophistication and scale, it is often the case that fully de novo model development is impractical and inefficient. Thus arises a critical need to quickly find, evaluate, re-use, and build upon models and model components developed by other researchers. We introduce the NeuroML Database (NeuroML-DB.org), which has been developed to address this need and to complement other model sharing resources. NeuroML-DB stores over 1,500 previously published models of ion channels, cells, and networks that have been translated to the modular NeuroML model description language. The database also provides reciprocal links to other neuroscience model databases (ModelDB, Open Source Brain) as well as access to the original model publications (PubMed). These links along with Neuroscience Information Framework (NIF) search functionality provide deep integration with other neuroscience community modeling resources and greatly facilitate the task of finding suitable models for reuse. Serving as an intermediate language, NeuroML and its tooling ecosystem enable efficient translation of models to other popular simulator formats. The modular nature also enables efficient analysis of a large number of models and inspection of their properties. Search capabilities of the database, together with web-based, programmable online interfaces, allow the community of researchers to rapidly assess stored model electrophysiology, morphology, and computational complexity properties. We use these capabilities to perform a database-scale analysis of neuron and ion channel models and describe a novel tetrahedral structure formed by cell model clusters in the space of model properties and features. This analysis provides further information about model similarity to enrich database search.

[Neurophilic herpesvirus: a powerful tool for neuroscience research].

Viruses are powerful tools for the study of modern neurosciences. Most of the research on the connection and function of neurons were done by using recombinant viruses, among which neurotropic herpesvirus is one of the most important tools. With the continuous development of genetic engineering and molecular biology techniques, several recombinant neurophilic herpesviruses have been engineered into different viral tools for neuroscience research. This review describes and discusses several common and widely used neurophilic herpesviruses as nerve conduction tracers, viral vectors for neurological diseases, and lytic viruses for neuro-oncology applications, which provides a reference for further exploring the function of neurophilic herpesviruses.

Research Data Management and Data Sharing for Reproducible Research-Results of a Community Survey of the German National Research Data Infrastructure Initiative Neuroscience.

Science is changing: the volume and complexity of data are increasing, the number of studies is growing and the goal of achieving reproducible results requires new solutions for scientific data management. In the field of neuroscience, the German National Research Data Infrastructure (NFDI-Neuro) initiative aims to develop sustainable solutions for research data management (RDM). To obtain an understanding of the present RDM situation in the neuroscience community, NFDI-Neuro conducted a comprehensive survey among the neuroscience community. Here, we report and analyze the results of the survey. We focused the survey and our analysis on current needs, challenges, and opinions about RDM. The German neuroscience community perceives barriers with respect to RDM and data sharing mainly linked to (1) lack of data and metadata standards, (2) lack of community adopted provenance tracking methods, (3) lack of secure and privacy preserving research infrastructure for sensitive data, (4) lack of RDM literacy, and (5) lack of resources (time, personnel, money) for proper RDM. However, an overwhelming majority of community members (91%) indicated that they would be willing to share their data with other researchers and are interested to increase their RDM skills. Taking advantage of this willingness and overcoming the existing barriers requires the systematic development of standards, tools, and infrastructure, the provision of training, education, and support, as well as additional resources for RDM to the research community and a constant dialogue with relevant stakeholders including policy makers to leverage of a culture change through adapted incentivization and regulation.

Introducing JOSPT Open: An Inclusive Journal to Support the Work of a Diverse Musculoskeletal Rehabilitation Community.

SYNOPSIS: This editorial introduces JOSPT Open, a new gold open-access journal in the JOSPT stable. In 2023, JOSPT Open will publish its inaugural issue. The journal aims to cover all aspects of musculoskeletal rehabilitation and associated areas related to musculoskeletal health, including clinical biomechanics, diagnostic imaging, epidemiology, physical activity, sports science, and pain neuroscience. JOSPT Open welcomes diverse approaches to studying interesting and important clinical questions, and innovative approaches to translating knowledge to practice. J Orthop Sports Phys Ther 2023;53(2):52-54. doi:10.2519/jospt.2023.11769.

Multi-area recordings and optogenetics in the awake, behaving marmoset.

The common marmoset has emerged as a key model in neuroscience. Marmosets are small in size, show great potential for genetic modification and exhibit complex behaviors. Thus, it is necessary to develop technology that enables monitoring and manipulation of the underlying neural circuits. Here, we describe a novel approach to record and optogenetically manipulate neural activity in awake, behaving marmosets. Our design utilizes a light-weight, 3D printed titanium chamber that can house several high-density silicon probes for semi-chronic recordings, while enabling simultaneous optogenetic stimulation. We demonstrate the application of our method in male marmosets by recording multi- and single-unit data from areas V1 and V6 with 192 channels simultaneously, and show that optogenetic activation of excitatory neurons in area V6 can influence behavior in a detection task. This method may enable future studies to investigate the neural basis of perception and behavior in the marmoset.

Charting the social neuroscience of human attachment (SoNeAt).

This introduction aims to set out the potential as well as some of the pitfalls of the newly emerging area of the Social Neuroscience of Human Attachment (SoNeAt). To organize and interconnect the burgeoning empirical studies in this line of research, including those in this special issue, we outline a programmatic framework including an extension of our conceptual proposals NAMA and NAMDA to guide future research. We hope that this special issue will act as a stimulus for redoubling our efforts advancing the newly emerging SoNeAt area bridging attachment theory and social neuroscience.

On the Role of Theory and Modeling in Neuroscience.

In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena that cross a wide range of scales and often require consideration at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative models and theories each play a distinct role in defining and bridging levels of abstraction, will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment.

Neuroscience: Visual restoration with optogenetics.

Treating photoreceptor degenerative diseases is an exciting application of optogenetic technologies. However, there are significant challenges, such as producing normal visual signaling as the retina rewires in response to photoreceptor death. However, a new study shows remarkable functional stability in retinal circuits that can be engaged by optogenetics following photoreceptor loss.

Concise Overview of Glypromate Neuropeptide Research: From Chemistry to Pharmacological Applications in Neurosciences.

Neurodegenerative diseases of the central nervous system (CNS) pose a serious health concern worldwide, with a particular incidence in developed countries as a result of life expectancy increase and the absence of restorative treatments. Presently, treatments for these neurological conditions are focused on managing the symptoms and/or slowing down their progression. As so, the research on novel neuroprotective drugs is of high interest. Glypromate (glycyl-l-prolyl-l-glutamic acid, also known as GPE), an endogenous small peptide widespread in the brain, holds great promise to tackle neurodegenerative diseases such as Parkinson's, Alzheimer's, and Huntington's, s well as other CNS-related disorders like Rett and Down's syndromes. However, the limited pharmacokinetic properties of Glypromate hinder its clinical application. As such, intense research has been devoted to leveraging the pharmacokinetic profile of this neuropeptide. This review aims to offer an updated perspective on Glypromate research by exploring the vast array of chemical derivatizations of more than 100 analogs described in the literature over the past two decades. The collection and discussion of the most relevant structure-activity relationships will hopefully guide the discovery of new Glypromate-based neuroprotective drugs.

Use of mental health services in the aftermath of COVID-19 waves: a retrospective study conducted in a French Psychiatric and Neurosciences University Hospital.

OBJECTIVE: The COVID-19 pandemic and the restrictive measures taken to prevent its propagation had profound effects on mental health and well-being, especially in children and young adults (<25 years old). This study aimed to analyse the medium and long-term impact of the COVID-19 pandemic on the use of the mental health services, by age groups and gender. DESIGN: We conducted a retrospective study using the medical and administrative information system databases of patients, between 2019 and 2021. SETTING: This study was conducted in the Groupe Hospitalier Universitaire Paris Psychiatry and Neurosciences. OUTCOME MEASURES: We reported three indicators: the number of new patients attending outpatient clinics, the number of emergency department (ED) visits and the number of hospital admissions. METHODS: We considered the weekly number of each indicator, by age groups and by gender. We also collected the reasons of ED visits and hospital admissions. The 2020 and 2021 data were compared with the same period in 2019. The evolution of the indicators over the 3 years was analysed with interrupted time-series analysis. RESULTS: All three indicators showed a dramatic decrease during the first lockdown period (March 2020) especially for the youngest. In 2021, the activity resumed but without reaching its prepandemic level. Moreover, mental healthcare seeking was significantly lower since the beginning of the pandemic compared with the prepandemic period for all age groups, except for young women (<25 years old). Among them, there was a higher level of mental health services use in 2021, compared with 2019: +20% of new patients at the outpatient clinics, +39% of ED visits and+17% of hospital admissions. CONCLUSIONS: The COVID-19 pandemic has had severe consequences on populations' mental health, especially among young women, which seem to persist months after the end of restrictive measures.

Identifying Thematics in a Brain-Computer Interface Research.

This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020-2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application.