Worldwide initiatives to advance brain research

The Association between cognitive impairment and anthropometric indices among the elderly: birjand longitudinal aging study

Background

The population of older adults has been consistently on the rise. We aimed to assess the possible relationship between cognitive decline and anthropometric indices in older adults, using data from the Birjand longitudinal aging study (BLAS).

Methods

In this cross-sectional research, the association between cognitive impairment as determined by two tests (Six Item Cognitive Impairment Test (6-CIT)) and (Mini-Mental State Examination (MMSE)) and anthropometric indices including waist circumference (WC), body mass index (BMI), waist to height ratio (WHtR), waist to hip ratio (WHR), body roundness index (BRI), and a body shape index (ABSI) were assessed among 1353 elderly ≥ 60 years old, participating in the BLAS cohort study (September 2018 to April 2019). Ordinal and binary logistic regression were used for analysis.

Results

According to the MMSE test, 58.3% of participants had cognitive impairment, while this frequency was 64.2% based on the 6-CIT test. A significant reverse association was observed between cognitive decline according to the 6-CIT test and BMI, WHR, and WC (P < 0.05). Cognitive impairment, according to MMSE, was inversely associated with WC and directly associated with WHtR and ABSI in the crude model, which disappeared after adjustment for confounders. BRI was not significantly related to any of the cognitive tests. According to BMI and WC, overweight and obesity could reduce the risk of cognitive impairment.

Conclusions

Overall, the result of this study showed that the risk of cognitive decline decreased among the elderly as BMI, WC, and WHR increased.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-024-01404-8.

Association between drinking water sources and cognitive functioning in Chinese older adults residing in rural areas

OBJECTIVES

To explore the association between drinking water sources and cognitive functioning among older adults residing in rural China.

METHODS

Data were extracted from the 2008-2018 Chinese Longitudinal Healthy Longevity Survey. Drinking water sources were categorized according to whether purification measures were employed. The Chinese version of the Mini-Mental State Examination was used for cognitive functioning assessment, and the score of <24 was considered as having cognitive dysfunction. Cox regression analyses were conducted to derive hazard ratios (HRs) and 95% confidence intervals (CIs) for the effects of various drinking water sources, changes in such sources, and its interaction with exercise on cognition dysfunction.

RESULTS

We included 2304 respondents aged 79.67 ± 10.02 years; of them, 1084 (44.49%) were men. Our adjusted model revealed that respondents consistently drinking tap water were 21% less likely to experience cognitive dysfunction compared with those drinking untreated water (HR = 0.79, 95% CI: 0.70-0.90). Respondents transitioning from natural to tap water showed were 33% less likely to experience cognitive dysfunction (HR = 0.67, 95% CI: 0.58-0.78). Moreover, the HR (95% CI) for the interaction between drinking tap water and exercising was 0.86 (0.75-1.00) when compared with that between drinking untreated water and not exercising. All results adjusted for age, occupation, exercise, and body mass index.

CONCLUSIONS

Prolonged tap water consumption and switching from untreated water to tap water were associated with a decreased risk of cognitive dysfunction in older individuals. Additionally, exercising and drinking tap water was synergistically associated with the low incidence of cognitive dysfunction. These findings demonstrate the importance of prioritizing drinking water health in rural areas, indicating that purified tap water can enhance cognitive function among older adults.

Post-trial access to implantable neural devices: an exploratory international survey

Objectives

Clinical trials of innovative neural implants are rapidly increasing and diversifying, but little is known about participants' post-trial access to the device and ongoing clinical care. This exploratory study examines common practices in the planning and coordination of post-trial access to neurosurgical devices. We also explore the perspectives of trial investigators on the barriers to post-trial access and ongoing care, as well as ethical questions related to the responsibilities of key stakeholder groups.

Design setting and participants

Trial investigators (n=66) completed a survey on post-trial access in the most recent investigational trial of a surgically implanted neural device they had conducted. Survey respondents predominantly specialized in neurosurgery, neurology and psychiatry, with a mean of 14.8 years of experience working with implantable neural devices.

Main outcome measures

Outcomes of interest included rates of device explantation during or at the conclusion of the trial (pre-follow-up) and whether plans for post-trial access were described in the study protocol. Outcomes also included investigators' greatest 'barrier' and 'facilitator' to providing research participants with post-trial access to functional implants and perspectives on current arrangements for the sharing of post-trial responsibilities among key stakeholders.

Results

Trial investigators reported either 'all' (64%) or 'most' (33%) trial participants had remained implanted after the end of the trial, with 'infection' and 'non-response' the most common reasons for explantation. When asked to describe the main barriers to facilitating post-trial access, investigators described limited funding, scarcity of expertise and specialist clinical infrastructure and difficulties maintaining stakeholder relationships. Notwithstanding these barriers, investigators overwhelmingly (95%) agreed there is an ethical obligation to provide post-trial access when participants individually benefit during the trial.

Conclusions

On occasions when devices were explanted during or at the end of the trial, this was done out of concern for the safety and well-being of participants. Further research into common practices in the post-trial phase is needed and essential to ethical and pragmatic discussions regarding stakeholder responsibilities.

Structurally divergent and recurrently mutated regions of primate genomes

We sequenced and assembled using multiple long-read sequencing technologies the genomes of chimpanzee, bonobo, gorilla, orangutan, gibbon, macaque, owl monkey, and marmoset. We identified 1,338,997 lineage-specific fixed structural variants (SVs) disrupting 1,561 protein-coding genes and 136,932 regulatory elements, including the most complete set of human-specific fixed differences. We estimate that 819.47 Mbp or ∼27% of the genome has been affected by SVs across primate evolution. We identify 1,607 structurally divergent regions wherein recurrent structural variation contributes to creating SV hotspots where genes are recurrently lost (e.g., CARD, C4, and OLAH gene families) and additional lineage-specific genes are generated (e.g., CKAP2, VPS36, ACBD7, and NEK5 paralogs), becoming targets of rapid chromosomal diversification and positive selection (e.g., RGPD gene family). High-fidelity long-read sequencing has made these dynamic regions of the genome accessible for sequence-level analyses within and between primate species.

Whole-brain Optical Imaging: A Powerful Tool for Precise Brain Mapping at the Mesoscopic Level

The mammalian brain is a highly complex network that consists of millions to billions of densely-interconnected neurons. Precise dissection of neural circuits at the mesoscopic level can provide important structural information for understanding the brain. Optical approaches can achieve submicron lateral resolution and achieve "optical sectioning" by a variety of means, which has the natural advantage of allowing the observation of neural circuits at the mesoscopic level. Automated whole-brain optical imaging methods based on tissue clearing or histological sectioning surpass the limitation of optical imaging depth in biological tissues and can provide delicate structural information in a large volume of tissues. Combined with various fluorescent labeling techniques, whole-brain optical imaging methods have shown great potential in the brain-wide quantitative profiling of cells, circuits, and blood vessels. In this review, we summarize the principles and implementations of various whole-brain optical imaging methods and provide some concepts regarding their future development.

Trends in Brain Research: A Bibliometric Analysis

BACKGROUND

Bibliometrics methods have allowed researchers to assess the popularity of brain research through the ever-growing number of brain-related research papers. While many topics of brain research have been covered by previous studies, there is no comprehensive overview of the evolution of brain research and its various specialties and funding practices over a long period of time.

OBJECTIVE

This paper aims to (1) determine how brain research has evolved over time in terms of number of papers, (2) countries' relative and absolute positioning in terms of papers and impact, and (3) how those various trends vary by area.

METHODS

Using a list of validated keywords, we extracted brain-related articles and journals indexed in the Web of Science over the 1991-2020 period, for a total of 2,467,708 papers. We used three indicators to perform: number of papers, specialization, and research impact.

RESULTS

Our results show that over the past 30 years, the number of brain-related papers has grown at a faster pace than science in general, with China being at the forefront of this growth. Different patterns of specialization among countries and funders were also underlined. Finally, the NIH, the European Commission, the National Natural Science Foundation of China, the UK Medical Research Council, and the German Research Foundation were found to be among the top funders.

CONCLUSION

Despite data-related limitations, our findings provide a large-scope snapshot of the evolution of brain research and its funding, which may be used as a baseline for future studies on these topics.

Human voltage-gated Na+ and K+ channel properties underlie sustained fast AP signaling

Human cortical pyramidal neurons are large, have extensive dendritic trees, and yet have unexpectedly fast input-output properties: Rapid subthreshold synaptic membrane potential changes are reliably encoded in timing of action potentials (APs). Here, we tested whether biophysical properties of voltage-gated sodium (Na+) and potassium (K+) currents in human pyramidal neurons can explain their fast input-output properties. Human Na+ and K+ currents exhibited more depolarized voltage dependence, slower inactivation, and faster recovery from inactivation compared with their mouse counterparts. Computational modeling showed that despite lower Na+ channel densities in human neurons, the biophysical properties of Na+ channels resulted in higher channel availability and contributed to fast AP kinetics stability. Last, human Na+ channel properties also resulted in a larger dynamic range for encoding of subthreshold membrane potential changes. Thus, biophysical adaptations of voltage-gated Na+ and K+ channels enable fast input-output properties of large human pyramidal neurons.

Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities

Brain-Computer Interfaces (BCI) is an exciting and emerging research area for researchers and scientists. It is a suitable combination of software and hardware to operate any device mentally. This review emphasizes the significant stages in the BCI domain, current problems, and state-of-the-art findings. This article also covers how current results can contribute to new knowledge about BCI, an overview of BCI from its early developments to recent advancements, BCI applications, challenges, and future directions. The authors pointed to unresolved issues and expressed how BCI is valuable for analyzing the human brain. Humans' dependence on machines has led humankind into a new future where BCI can play an essential role in improving this modern world.

Analysis of Network Models with Neuron-Astrocyte Interactions

Neural networks, composed of many neurons and governed by complex interactions between them, are a widely accepted formalism for modeling and exploring global dynamics and emergent properties in brain systems. In the past decades, experimental evidence of computationally relevant neuron-astrocyte interactions, as well as the astrocytic modulation of global neural dynamics, have accumulated. These findings motivated advances in computational glioscience and inspired several models integrating mechanisms of neuron-astrocyte interactions into the standard neural network formalism. These models were developed to study, for example, synchronization, information transfer, synaptic plasticity, and hyperexcitability, as well as classification tasks and hardware implementations. We here focus on network models of at least two neurons interacting bidirectionally with at least two astrocytes that include explicitly modeled astrocytic calcium dynamics. In this study, we analyze the evolution of these models and the biophysical, biochemical, cellular, and network mechanisms used to construct them. Based on our analysis, we propose how to systematically describe and categorize interaction schemes between cells in neuron-astrocyte networks. We additionally study the models in view of the existing experimental data and present future perspectives. Our analysis is an important first step towards understanding astrocytic contribution to brain functions. However, more advances are needed to collect comprehensive data about astrocyte morphology and physiology in vivo and to better integrate them in data-driven computational models. Broadening the discussion about theoretical approaches and expanding the computational tools is necessary to better understand astrocytes' roles in brain functions.

Structurally divergent and recurrently mutated regions of primate genomes

To better understand the pattern of primate genome structural variation, we sequenced and assembled using multiple long-read sequencing technologies the genomes of eight nonhuman primate species, including New World monkeys (owl monkey and marmoset), Old World monkey (macaque), Asian apes (orangutan and gibbon), and African ape lineages (gorilla, bonobo, and chimpanzee). Compared to the human genome, we identified 1,338,997 lineage-specific fixed structural variants (SVs) disrupting 1,561 protein-coding genes and 136,932 regulatory elements, including the most complete set of human-specific fixed differences. Across 50 million years of primate evolution, we estimate that 819.47 Mbp or ~27% of the genome has been affected by SVs based on analysis of these primate lineages. We identify 1,607 structurally divergent regions (SDRs) wherein recurrent structural variation contributes to creating SV hotspots where genes are recurrently lost (CARDs, ABCD7, OLAH) and new lineage-specific genes are generated (e.g., CKAP2, NEK5) and have become targets of rapid chromosomal diversification and positive selection (e.g., RGPDs). High-fidelity long-read sequencing has made these dynamic regions of the genome accessible for sequence-level analyses within and between primate species for the first time.

Collaborating neuroscience online: The case of the Human Brain Project forum

This paper analyzes user interactions on the public-access online forum of the Human Brain Project (HBP), a major European Union-funded neuroscience research initiative, to understand the utility of the Forum for collaborative problem solving. We construct novel data using discussion forum posts and detailed user profiles on the HBP Forum. We find that HBP Forum utilization is comparable to that of a leading general-interest coding platform, and that online usage metrics quickly recovered after an initial Covid-19-related dip. Regression results show that user interactions on the Forum are more active for questions on programming and in HBP core areas. Further, Cox proportional hazard analyses show that such problems are solved faster. Forum posts with users from different countries tend to be discussed more actively but solved slower. Higher shares of administrator support tend to solve problems faster. There are no clear patterns regarding gender and seniority. Our results suggest that building novel collaborative forums can support researchers working on complex topics in challenging times.

Design and simulation of a neural interface based on a microfluidic flexible interconnection cable for chemical delivery

Neural interfaces are fundamental tools for transmitting information from the nervous system. Research on the immune response of an invasive neural interface is a field that requires continuous effort. Various efforts have been made to overcome or minimize limitations through modifying the designs and materials of neural interfaces, modifying surface characteristics, and adding functions to them. In this study, we demonstrate microfluidic channels with crater-shaped structures fabricated using parylene-C membranes for fluid delivery from the perspective of theory, design, and simulation. The simulation results indicated that the fluid flow depended on the size of the outlet and the alignment of microstructures inside the fluidic channel. All the results can be used to support the design of microfluidic channels made by membranes for drug delivery.

Nomogram to predict postoperative cognitive dysfunction in elderly patients undergoing gastrointestinal tumor resection

OBJECTIVE

To establish a nomogram model for the prediction of postoperative cognitive dysfunction (POCD) in elderly patients undergoing gastrointestinal tumor resection.

METHODS

A total of 369 elderly patients scheduled for elective gastrointestinal tumor resection under general anesthesia were included. The cognitive function of each participant was assessed by the Mini-Mental State Examination (MMSE) 1 day before surgery and 7 days after surgery for the diagnosis of POCD. According to the results, patients were divided into a POCD group and a non-POCD group. The differences in hospitalization data and examination results between the two groups were compared. A logistic regression model was used to explore the risk factors for POCD in elderly patients undergoing gastrointestinal tumor resection, and a nomogram was then constructed based on these factors. The diagnostic performance of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUROC) and a calibration plot. The clinical usefulness of the nomogram was estimated using decision curve analysis (DCA).

RESULTS

Among the 369 patients undergoing gastrointestinal tumor resection, 79 patients had POCD, with a positive rate of 21.4%. The nomogram model comprised the following variables: age, body mass index (BMI), history of cerebrovascular disease, preoperative white blood cell (WBC) count, preoperative hemoglobin (Hb) level, intra-operative blood loss, and operation time. The model showed good discrimination, with an area under the curve (AUC) of 0.710 (95% CI = 0.645-0.775), and good calibration (Hosmer-Lemeshow test, χ2 = 5.133, p = 0.274). Internal validation also maintained ideal discrimination and calibration. Decision curves indicated that when the threshold probability was above 0.1, the nomogram achieved more benefit than both the treat-all and treat-none policies.

CONCLUSION

This scoring system is the first nomogram model developed for the prediction of POCD in elderly patients undergoing gastrointestinal tumor resection. It has good efficacy in the prediction of POCD risk and could provide an important reference for the prevention, management, and treatment of POCD.

Brain signal predictions from multi-scale networks using a linearized framework

Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (≲ 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models (≳ 10⁶ neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials (‘spikes’) or spike rates. Corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin.

Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations, and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay, and volume conductor model allow for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals from conductance-based multicompartment neuron networks. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for.

The work also constitutes a major advance in computational efficiency of accurate, biophysics-based signal predictions from large-scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool LFPykernels serves as a reference implementation of the framework.

Understanding the brain’s function and activity in healthy and pathological states across spatial scales and times spanning entire lives is one of humanity’s great undertakings. In experimental and clinical work probing the brain’s activity, a variety of electric and magnetic measurement techniques are routinely applied. However interpreting the extracellularly measured signals remains arduous due to multiple factors, mainly the large number of neurons contributing to the signals and complex interactions occurring in recurrently connected neuronal circuits. To understand how neurons give rise to such signals, mechanistic modeling combined with forward models derived using volume conductor theory has proven to be successful, but this approach currently does not scale to the systems level (encompassing millions of neurons or more) where simplified or abstract neuron representations typically are used. Motivated by experimental findings implying approximately linear relationships between times of neuronal action potentials and extracellular population signals, we provide a biophysics-based method for computing causal filters relating spikes and extracellular signals that can be applied with spike times or rates of large-scale neuronal network models for predictions of population signals without relying on ad hoc approximations.

Decoding psychosis: from national genome project to national brain project

The mind has puzzled humans for centuries, and its disorders, such as psychoses, have caused tremendous difficulties. However, relatively recent biotechnological breakthroughs, such as DNA technology and neuroimaging, have empowered scientists to explore the more fundamental aspects of psychosis. From searching for psychosis-causing genes to imaging the depths of the brain, scientists worldwide seek novel methods to understand the mind and the causes of its disorders. This article will briefly review the history of understanding and managing psychosis and the main findings of modern genetic research and then attempt to stimulate thought for decoding the biological mechanisms of psychosis in the present era of brain science.

Association of Body Mass Index and Plant-Based Diet with Cognitive Impairment among Older Chinese Adults: A Prospective, Nationwide Cohort Study

To examine the association of body mass index (BMI) and a plant-based diet (PBD) with cognitive impairment in older adults, this cohort study used data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a national, community-based, longitudinal, prospective study in China. Cognitive function was evaluated via the Mini-Mental State Examination (MMSE). Diet was assessed using a simplified food frequency questionnaire (FFQ), and PBD patterns were estimated using the overall plant-based diet index (PDI), the healthful plant-based diet index (hPDI), and the unhealthful plant-based diet index (uPDI). BMI was measured objectively during the physical examination. Cox proportional hazard models and restricted cubic spline analyses were used. A total of 4792 participants with normal cognition at baseline were included, and 1077 participants were identified as having developed cognitive impairment during the 24,156 person-years of follow-up. A reverse J-shaped association was observed between BMI and cognitive impairment (p = 0.005 for nonlinearity). Participants who were overweight (HR = 0.79; 95% CI 0.66–0.95) and obese (HR = 0.72; 95% CI 0.54–0.96) had a decreased risk of cognitive impairment, while those who were underweight (HR = 1.42; 95% CI 1.21–1.66) had an increased risk. Lower PDI, lower hPDI, and higher uPDI were associated with an increased risk of cognitive impairment (HR = 1.32; 95% CI 1.16–1.50 for PDI; HR = 1.46; 95% CI 1.29–1.66 for hPDI; HR = 1.21; 95% CI 1.06–1.38 for uPDI). The protective effect of being overweight on cognitive impairment was more pronounced among participants with a higher PDI (HR = 0.74; 95% CI 0.57–0.95) than those with a lower PDI (HR = 0.87; 95% CI 0.67–1.12), among participants with a higher hPDI (HR = 0.73; 95% CI 0.57–0.94) than those with a lower hPDI (HR = 0.93; 95% CI 0.72–1.10), and among participants with a lower uPDI (HR = 0.61; 95% CI 0.46–0.80) than those with a higher uPDI (HR = 1.01; 95% CI 0.80–1.27). Our results support the positive associations of overweight status, obesity, an overall PBD, and a healthful PBD with cognitive function in older adults. A lower adherence to an overall PBD, a healthful PBD, and a higher adherence to an unhealthful PBD may attenuate the protective effect of being overweight on cognitive function.

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data - such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles - also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.

Long-term Weight Change and its Temporal Relation to Later-life Dementia in the Health and Retirement Study

CONTEXT

Weight loss among middle-aged and older adults has been associated with a higher risk of subsequent dementia. However, most studies have limited follow-up durations or suboptimal control for the potential influence of physical frailty (PF).

OBJECTIVE

Our study aimed to investigate the long-term and temporal relations of weight change to risk of dementia among middle-aged and older adults in the United States.

METHODS

A total of 5985 participants aged 65 years and older were included from the Health and Retirement Study. History of long-term weight change was calculated using 9 repeated body mass index measurements during 1992-2008. We then followed participants' dementia status from 2008 to 2018. Multivariable Cox proportional hazard models were used.

RESULTS

During the study follow-up period (mean = 7.54 years), a total of 682 (11.40%) dementia cases were documented. After adjustment for basic demographic and lifestyle factors, participants with weight loss (median: -0.23 kg/m2 per year) were at a significantly higher risk of dementia (HR = 1.60; 95% CI, 1.33, 1.92), compared with the stable weight group (median: 0.11 kg/m2 per year). This association was attenuated but remained strong and significant after further adjustment for PF (HR = 1.57; 95% CI, 1.30, 1.89). Significant association was observed for weight loss assessed approximately 14 to 18 years preceding dementia diagnosis (HR = 1.30; 95% CI, 1.07, 1.58), and was consistent closer to diagnosis.

CONCLUSION

Both recent and remote weight loss were associated with a higher risk of later-life dementia among middle-aged and older adults independent of PF status.

Towards a Governance Framework for Brain Data

The increasing availability of brain data within and outside the biomedical field, combined with the application of artificial intelligence (AI) to brain data analysis, poses a challenge for ethics and governance. We identify distinctive ethical implications of brain data acquisition and processing, and outline a multi-level governance framework. This framework is aimed at maximizing the benefits of facilitated brain data collection and further processing for science and medicine whilst minimizing risks and preventing harmful use. The framework consists of four primary areas of regulatory intervention: binding regulation, ethics and soft law, responsible innovation, and human rights.

Current Topics in Research, Care, and Welfare of Common Marmosets

Although the common marmoset (Callithrix jacchus) has been maintained in captivity in biomedical research settings for decades, interest and use of the species as an animal model for a diverse array of purposes has increased in the 21st century. Unfortunately, the development of validated animal care standards such as nutrition, husbandry, and clinical care has not expanded with the same rapidity as the use of the species in research. The goal of this themed issue of the ILAR Journal is to review current literature relevant to topics that impact marmoset health, welfare, and use in research. As the population of captive marmosets increases worldwide, the editors urge scientists, veterinary clinicians, and colony managers to continue conducting and publishing robust studies to develop evidence-based standards related to marmoset care and use. The editors also encourage IACUCs and other institutional review bodies to seek training on topics relevant to marmoset welfare and develop related policies prior to acquiring animals as a novel species.

Neurophotonic tools for microscopic measurements and manipulation: status report

Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions.

Identifying knowledge important to teach about the nervous system in the context of secondary biology and science education-A Delphi study

Teaching about the nervous system has become a challenging task in secondary biology and science education because of the fast development in the field of neuroscience. A major challenge is to determine what content to teach. Curricula goals are often too general to guide instruction, and information about the nervous system has become overwhelming and diverse with ubiquitous relevance in society. In addition, several misconceptions and myths are circulating in educational communities causing world-wide confusion as to what content is correct. To help teachers, textbook authors, and curricula developers in this challenging landscape of knowledge, the aim of the present study is to identify the expert view on what knowledge is important for understanding the nervous system in the context of secondary biology and science education. To accomplish this, we have conducted a thematic content analysis of textbooks followed by a Delphi study of 15 experts in diverse but relevant fields. The results demonstrate six curriculum themes including gross anatomy and function, cell types and functional units, the nerve signal, connections between neurons, when nerve signals travel through networks of neurons, and plasticity in the nervous system, as well as 26 content principles organized in a coherent curriculum progression from general content to more specific content. Whereas some of the principles clarify and elaborate on traditional school biology knowledge, others add new knowledge to the curriculum. Importantly, the new framework for teaching about the nervous system presented here, meets the needs of society, as expressed by recent international policy frameworks of OECD and WHO, and it addresses common misconceptions about the brain. The study suggests an update of the biology and science curriculum.

Neuroscience20 (BRAIN20, SPINE20, and MENTAL20) Health Initiative: A Global Consortium Addressing the Human and Economic Burden of Brain, Spine, and Mental Disorders Through Neurotech Innovations and Policies

Neurological disorders significantly impact the world's economy due to their often chronic and life-threatening nature afflicting individuals which, in turn, creates a global disease burden. The Group of Twenty (G20) member nations, which represent the largest economies globally, should come together to formulate a plan on how to overcome this burden. The Neuroscience-20 (N20) initiative of the Society for Brain Mapping and Therapeutics (SBMT) is at the vanguard of this global collaboration to comprehensively raise awareness about brain, spine, and mental disorders worldwide. This paper aims to provide a comprehensive review of the various brain initiatives worldwide and highlight the need for cooperation and recommend ways to bring down costs associated with the discovery and treatment of neurological disorders. Our systematic search revealed that the cost of neurological and psychiatric disorders to the world economy by 2030 is roughly $16T. The cost to the economy of the United States is $1.5T annually and growing given the impact of COVID-19. We also discovered there is a shortfall of effective collaboration between nations and a lack of resources in developing countries. Current statistical analyses on the cost of neurological disorders to the world economy strongly suggest that there is a great need for investment in neurotechnology and innovation or fast-tracking therapeutics and diagnostics to curb these costs. During the current COVID-19 pandemic, SBMT, through this paper, intends to showcase the importance of worldwide collaborations to reduce the population's economic and health burden, specifically regarding neurological/brain, spine, and mental disorders.

Association between body mass index, its change and cognitive impairment among Chinese older adults: a community-based, 9-year prospective cohort study

To examine the association of baseline body mass index (BMI) and BMI change with cognitive impairment among older adults in China. The study included data from the Chinese Longitudinal Healthy Longevity Study, a national community-based prospective cohort study from 2002 to 2018. Baseline BMI and BMI change were available for 12,027 adults aged older than 65 years. Cognitive impairment was defined as Chinese version of the Mini Mental State Examination score lower than 18. Multivariable Cox proportional hazard model was used. Among 12,027 participants (mean age was 81.23 years old and 47.48% were male), the proportion of underweight, normal, overweight and obese at baseline was 33.87%, 51.39%, 11.39% and 3.34%, respectively. During an average of 5.9 years' follow-up, 3086 participants (4.35 per 100 person-years) with incident cognitive impairment were identified. Compared with normal weight group, adjusted hazard ratio (AHR) for cognitive impairment was 0.86 (95% CI 0.75-0.99) among overweight group, whereas corresponding AHR was 1.02 (95% CI 0.94-1.10) in underweight and 1.01 (95% CI 0.80-1.28) in obese participants. Large weight loss (< -10%) was significantly associated with an increased risk of cognitive impairment (AHR, 1.42, 95% CI 1.29-1.56), compared to stable weight status group (-5% ~ 5%). In the restricted cubic spline models, BMI change showed a reverse J-shaped association with cognitive impairment. BMI-defined overweight, but not obesity, was associated with a lower risk of cognitive impairment among elderly Chinese adults, while large weight loss was associated with an increased risk. These findings are consistent with weight loss in the prodromal phase of dementia.

Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda

Simulation of large-scale networks of neurons is an important approach to understanding and interpreting experimental data from healthy and diseased brains. Owing to the rapid development of simulation software and the accumulation of quantitative data of different neuronal types, it is possible to predict both computational and dynamical properties of local microcircuits in a ‘bottom-up’ manner. Simulated data from these models can be compared with experiments and ‘top-down’ modelling approaches, successively bridging the scales. Here we describe an open source pipeline, using the software Snudda, for predicting microcircuit connectivity and for setting up simulations using the NEURON simulation environment in a reproducible way. We also illustrate how to further ‘curate’ data on single neuron morphologies acquired from public databases. This model building pipeline was used to set up a first version of a full-scale cellular level model of mouse dorsal striatum. Model components from that work are here used to illustrate the different steps that are needed when modelling subcortical nuclei, such as the basal ganglia.

Functional visualization and manipulation in the marmoset brain using viral vectors

The common marmoset, a New World monkey, has a primate-specific cortex with approximately 40 Brodmann areas. Genetically encoded calcium indicator (GECI) techniques have been applied to study the functional organization of the marmoset cortex. The success of GCaMP (a green fluorescent of GECI) imaging and other advances, including optogenetic approaches, provide an interesting and exciting opportunity to study the primate brain at the molecular and cellular levels, leading to an understanding of primate neural circuits. These approaches will help advance our knowledge on cognition in primates, including humans, and therapy for human neurological and psychiatric disorders.

A marathon, not a sprint - neuroimaging, Open Science and ethics

Open Science is calling for a radical re-thinking of existing scientific practices. Within the neuroimaging community, Open Science practices are taking the form of open data repositories and open lab notebooks. The broad sharing of data that accompanies Open Science, however, raises some difficult ethical and legal issues. With neuroethics as a focusing lens, we explore eight central concerns posed by open data with regard to human brain imaging studies: respect for individuals and communities, concern for marginalized communities, consent, privacy protections, participatory research designs, contextual integrity, fusions of clinical and research goals, and incidental findings. Each consideration assists in bringing nuance to the potential benefits for open data sharing against associated challenges. We combine current understandings with forward-looking solutions to key issues. We conclude by underscoring the need for new policy tools to enhance the potential for responsible open data.

How Blue is the Sky?

The recent trend toward an industrialization of brain exploration and the technological prowess of artificial intelligence algorithms and high-performance computing has caught the imagination of the public. These impressive advances are fueling an uncontrolled societal hype, the more amplified, the more "Blue Sky" the claim is. Will we ever be able to simulate a brain in silico? Will "it" (the digital avatar) be conscious? The Blue Brain Project (BBP) and the European flagship the Human Brain Project (HBP) have surfed on this wave for the past 10 years. Their already significant lifetimes now offer new case studies for neuroscience sociology and epistemology, as the projects mature. Their distinctive "Blue Sky" flavor has been a key feature in securing unprecedented funding (more than one billion Euros) mostly through supranational institutions. The longitudinal analysis of these ventures provides clues to how the neuromyth they propagate sells science, in a scientific world based on an economy of promises.

Progress in Brain Computer Interface: Challenges and Opportunities

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

Current Status of and Perspectives on the Application of Marmosets in Neurobiology

The common marmoset (Callithrix jacchus), a small New World primate, is receiving substantial attention in the neuroscience and biomedical science fields because its anatomical features, functional and behavioral characteristics, and reproductive features and its amenability to available genetic modification technologies make it an attractive experimental subject. In this review, I outline the progress of marmoset neuroscience research and summarize both the current status (opportunities and limitations) of and the future perspectives on the application of marmosets in neuroscience and disease modeling.

Dietary carotenoids related to risk of incident Alzheimer dementia (AD) and brain AD neuropathology: a community-based cohort of older adults

BACKGROUND

Studies have reported a protective relation to cognitive decline with long-term intake of total and individual dietary carotenoids. However, the underlying mechanisms have not yet been clearly established in humans.

OBJECTIVES

To evaluate the prospective association between intakes of total and individual carotenoids and risk of incident Alzheimer dementia (AD) and explore the underlying neuropathological basis.

METHODS

Among 927 participants from the Rush Memory and Aging Project who were free from AD at baseline and were followed up for a mean of 7 y, we estimated HRs for AD using Cox proportional hazards models by intakes of energy-adjusted carotenoids. Brain AD neuropathology was assessed in postmortem brain autopsies among 508 deceased participants. We used linear regression to assess the association of carotenoid intake with AD-related neuropathology.

RESULTS

Higher intake of total carotenoids was associated with substantially lower hazard of AD after controlling for age, sex, education, ApoE-ε4, participation in cognitively stimulating activities, and physical activity level. Comparing the top and bottom quintiles (median intake: 24.8 compared with 6.7 mg/d) of total carotenoids, the multivariate HR (95% CI) was 0.52 (0.33, 0.81), P-trend < 0.01. A similar association was observed for lutein-zeaxanthin, a weaker linear inverse association was observed for β-carotene, and a marginally significant linear inverse association was found for β-cryptoxanthin. Among the deceased participants, consumers of higher total carotenoids (top compared with bottom tertile, 18.2 compared with 8.2 mg/d) had less global AD pathology (b: -0.10; SE = 0.04; P-trend = 0.01). For individual carotenoids, lutein-zeaxanthin and lycopene were inversely associated with brain global pathology, whereas lutein-zeaxanthin showed additional inverse associations with AD diagnostic score, neuritic plaque severity, and neurofibrillary tangle density and severity.

CONCLUSIONS

Our findings support a beneficial role of total carotenoid consumption, in particular lutein/zeaxanthin, on AD incidence that may be related to the inhibition of brain β-amyloid deposition and fibril formation.

Artificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges

Clinical neuroscience is increasingly relying on the collection of large volumes of differently structured data and the use of intelligent algorithms for data analytics. In parallel, the ubiquitous collection of unconventional data sources (e.g. mobile health, digital phenotyping, consumer neurotechnology) is increasing the variety of data points. Big data analytics and approaches to Artificial Intelligence (AI) such as advanced machine learning are showing great potential to make sense of these larger and heterogeneous data flows. AI provides great opportunities for making new discoveries about the brain, improving current preventative and diagnostic models in both neurology and psychiatry and developing more effective assistive neurotechnologies. Concurrently, it raises many new methodological and ethical challenges. Given their transformative nature, it is still largely unclear how AI-driven approaches to the study of the human brain will meet adequate standards of scientific validity and affect normative instruments in neuroethics and research ethics. This manuscript provides an overview of current AI-driven approaches to clinical neuroscience and an assessment of the associated key methodological and ethical challenges. In particular, it will discuss what ethical principles are primarily affected by AI approaches to human neuroscience, and what normative safeguards should be enforced in this domain.

The NanoZoomer artificial intelligence connectomics pipeline for tracer injection studies of the marmoset brain

We describe our connectomics pipeline for processing anterograde tracer injection data for the brain of the common marmoset (Callithrix jacchus). Brain sections were imaged using a batch slide scanner (NanoZoomer 2.0-HT) and we used artificial intelligence to precisely segment the tracer signal from the background in the fluorescence images. The shape of each brain was reconstructed by reference to a block-face and all data were mapped into a common 3D brain space with atlas and 2D cortical flat map. To overcome the effect of using a single template atlas to specify cortical boundaries, brains were cyto- and myelo-architectonically annotated to create individual 3D atlases. Registration between the individual and common brain cortical boundaries in the flat map space was done to absorb the variation of each brain and precisely map all tracer injection data into one cortical brain space. We describe the methodology of our pipeline and analyze the accuracy of our tracer segmentation and brain registration approaches. Results show our pipeline can successfully process and normalize tracer injection experiments into a common space, making it suitable for large-scale connectomics studies with a focus on the cerebral cortex.

International Brain Initiative: An Innovative Framework for Coordinated Global Brain Research Efforts

The International Brain Initiative (IBI) has been established to coordinate efforts across existing and emerging national and regional brain initiatives. This NeuroView describes how to be involved and the new opportunities for global collaboration that are emerging between scientists, scientific societies, funders, industry, government, and society.

Greedy low-rank algorithm for spatial connectome regression

Recovering brain connectivity from tract tracing data is an important computational problem in the neurosciences. Mesoscopic connectome reconstruction was previously formulated as a structured matrix regression problem (Harris et al. in Neural Information Processing Systems, 2016), but existing techniques do not scale to the whole-brain setting. The corresponding matrix equation is challenging to solve due to large scale, ill-conditioning, and a general form that lacks a convergent splitting. We propose a greedy low-rank algorithm for the connectome reconstruction problem in very high dimensions. The algorithm approximates the solution by a sequence of rank-one updates which exploit the sparse and positive definite problem structure. This algorithm was described previously (Kressner and Sirković in Numer Lin Alg Appl 22(3):564-583, 2015) but never implemented for this connectome problem, leading to a number of challenges. We have had to design judicious stopping criteria and employ efficient solvers for the three main sub-problems of the algorithm, including an efficient GPU implementation that alleviates the main bottleneck for large datasets. The performance of the method is evaluated on three examples: an artificial "toy" dataset and two whole-cortex instances using data from the Allen Mouse Brain Connectivity Atlas. We find that the method is significantly faster than previous methods and that moderate ranks offer a good approximation. This speedup allows for the estimation of increasingly large-scale connectomes across taxa as these data become available from tracing experiments. The data and code are available online.

Classification of GABAergic interneurons by leading neuroscientists

There is currently no unique catalog of cortical GABAergic interneuron types. In 2013, we asked 48 leading neuroscientists to classify 320 interneurons by inspecting images of their morphology. That study was the first to quantify the degree of agreement among neuroscientists in morphology-based interneuron classification, showing high agreement for the chandelier and Martinotti types, yet low agreement for most of the remaining types considered. Here we present the dataset containing the classification choices by the neuroscientists according to interneuron type as well as to five prominent morphological features. These data can be used as crisp or soft training labels for learning supervised machine learning interneuron classifiers, while further analyses can try to pinpoint anatomical characteristics that make an interneuron especially difficult or especially easy to classify.

Gotta Trace 'em All: A Mini-Review on Tools and Procedures for Segmenting Single Neurons Toward Deciphering the Structural Connectome

Decoding the morphology and physical connections of all the neurons populating a brain is necessary for predicting and studying the relationships between its form and function, as well as for documenting structural abnormalities in neuropathies. Digitizing a complete and high-fidelity map of the mammalian brain at the micro-scale will allow neuroscientists to understand disease, consciousness, and ultimately what it is that makes us humans. The critical obstacle for reaching this goal is the lack of robust and accurate tools able to deal with 3D datasets representing dense-packed cells in their native arrangement within the brain. This obliges neuroscientist to manually identify the neurons populating an acquired digital image stack, a notably time-consuming procedure prone to human bias. Here we review the automatic and semi-automatic algorithms and software for neuron segmentation available in the literature, as well as the metrics purposely designed for their validation, highlighting their strengths and limitations. In this direction, we also briefly introduce the recent advances in tissue clarification that enable significant improvements in both optical access of neural tissue and image stack quality, and which could enable more efficient segmentation approaches. Finally, we discuss new methods and tools for processing tissues and acquiring images at sub-cellular scales, which will require new robust algorithms for identifying neurons and their sub-structures (e.g., spines, thin neurites). This will lead to a more detailed structural map of the brain, taking twenty-first century cellular neuroscience to the next level, i.e., the Structural Connectome.

Functional Neuroimaging in the New Era of Big Data

The field of functional neuroimaging has substantially advanced as a big data science in the past decade, thanks to international collaborative projects and community efforts. Here we conducted a literature review on functional neuroimaging, with focus on three general challenges in big data tasks: data collection and sharing, data infrastructure construction, and data analysis methods. The review covers a wide range of literature types including perspectives, database descriptions, methodology developments, and technical details. We show how each of the challenges was proposed and addressed, and how these solutions formed the three core foundations for the functional neuroimaging as a big data science and helped to build the current data-rich and data-driven community. Furthermore, based on our review of recent literature on the upcoming challenges and opportunities toward future scientific discoveries, we envisioned that the functional neuroimaging community needs to advance from the current foundations to better data integration infrastructure, methodology development toward improved learning capability, and multi-discipline translational research framework for this new era of big data.

Neuroethics at 15: The Current and Future Environment for Neuroethics

Neuroethics research and scholarship intersect with dynamic academic disciplines in science, engineering, and the humanities. On the occasion of the 15th anniversary of the formation of the International Neuroethics Society, we identify current and future topics for neuroethics and discuss the many social and political challenges that emerge from the converging dynamics of neurotechnologies and artificial intelligence. We also highlight the need for a global, transdisciplinary, and integrated community of researchers to address the challenges that are precipitated by this rapid sociotechnological transformation.

Whither Research Domain Criteria?

In 2010, the National Institute of Mental Health launched the Research Diagnostic Criteria (RDoC) as a research framework aimed at advancing research into the etiology of mental disorders, the development of clinically actionable biomarkers, and the eventual development of precision medications. The foundation of RDoC in that first phase rested in the assumption that mental disorders are brain disorders that originate in aberrant neural circuitry, and that therapeutic advances could flow from alterations in that circuitry. RDoC proposed a matrix of psychological constructs with seven levels of analysis ranging from the cell to self-report, but with neural circuitry at the center. In 2016, another model was proposed in which neural circuitry became equivalent to other units of analyses. With the advent of a new Director of the NIMH, the emphasis returned to neural circuitry as a priority, along with computational psychiatry. Have these shifts undermined the RDoC project?

Dual-use nano-neurotechnology

The chemical and biological nonproliferation regime stands at a watershed moment, when failure seems a real possibility. After the unsuccessful outcome of the 2016 Eighth Review Conference, the future of the Biological and Toxin Weapons Convention is uncertain. As the Chemical Weapons Convention (CWC) approaches its Fourth Review Conference in 2018, it has almost completed removing the huge stocks of chemical weapons, but it now faces the difficult organizational task of moving its focus to preventing the reemergence of chemical weapons at a time when the international security situation appears to be increasingly more difficult and dangerous. In this article, we assess the current and near-term state (5-10 years) and impact of three related areas of science and technology that could be of dual-use concern: targeted delivery of agents to the central nervous system (CNS), particularly by means of nanotechnology; direct impact of nanomaterials on synaptic functions in the CNS; and neuronal circuits in the brain that might be targeted by those with hostile intent. We attempt to assess the implications of our findings, particularly for the consideration of the problem of state-level interest in so-called nonlethal incapacitating chemical agents for law enforcement at the CWC Review Conference in 2018, but also more generally for the longer-term future of the chemical and biological nonproliferation regime.

PAT-Probabilistic Axon Tracking for Densely Labeled Neurons in Large 3-D Micrographs

A major goal of contemporary neuroscience research is to map the structural connectivity of mammalian brain using microscopy imaging data. In this context, the reconstruction of densely labeled axons from two-photon microscopy images is a challenging and important task. The visually overlapping, crossing, and often strongly distorted images of the axons allow many ambiguous interpretations to be made. We address the problem of tracking axons in densely labeled samples of neurons in large image data sets acquired from marmoset brains. Our high-resolution images were acquired using two-photon microscopy and they provided whole brain coverage, occupying terabytes of memory. Both the image distortions and the large data set size frequently make it impractical to apply present-day neuron tracing algorithms to such data due to the optimization of such algorithms to the precise tracing of either single or sparse sets of neurons. Thus, new tracking techniques are needed. We propose a probabilistic axon tracking algorithm (PAT). PAT tackles the tracking of axons in two steps: locally (L-PAT) and globally (G-PAT). L-PAT is a probabilistic tracking algorithm that can tackle distorted, cluttered images of densely labeled axons. L-PAT divides a large micrograph into smaller image stacks. It then processes each image stack independently before mapping the axons in each image to a sparse model of axon trajectories. G-PAT merges the sparse L-PAT models into a single global model of axon trajectories by minimizing a global objective function using a probabilistic optimization method. We demonstrate the superior performance of PAT over standard approaches on synthetic data. Furthermore, we successfully apply PAT to densely labeled axons in large images acquired from marmoset brains.

Neural circuitry and precision medicines for mental disorders: are they compatible?

Given the failure of psychiatry to develop clinically useful biomarkers for psychiatric disorders, and the concomitant failure to develop significant advances in diagnosis and treatment, the National Institute of Mental Health (NIMH) in 2010 launched the Research Domain Criteria (RDoC), a framework for research based on the assumption that mental disorders are disorders of identifiable brain neural circuits, with neural circuitry at the center of units of analysis ranging from genes, molecules, and cells to behavior, self-reports, and paradigms. These were to be integrated with five validated dimensional psychological constructs such as negative and positive valence systems. Four years later, the NIMH stated that the ultimate goal of RDoC is precision medicine for psychiatry, with the assumption that precision medications will normalize dysfunctional neural circuits. How this could be accomplished is not obvious, given that neural circuits are widely distributed, have unclear boundaries, and exhibit a significant degree of neuroplasticity, with multiple circuits present in any given disorder. Moreover, the early focus on neural circuitry has been criticized for its reductionism and neglect of the more recent RDoC emphasis on the integration and equivalence of biological and psychological phenomena. Yet this seems inconsistent with the priorities of the NIMH director, an advocate of the central role of neural circuitry and projects such as the Brain Initiative and the Human Connectome Project. Will such projects, at a cost of at least $10 billion, lead to precision medications for mental disorders, or further diminish funding for clinical care and research?