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Sharing human brain data can yield scientific benefits, but because of various disincentives, only a fraction of these data is currently shared. We profile three successful data-sharing experiences from the NIH BRAIN Initiative Research Opportunities in Humans (ROH) Consortium and demonstrate benefits to data producers and to users.

In this paper, we provide an overview and analysis of the BRAIN Initiative data-sharing ecosystem. First, we compare and contrast the characteristics of the seven BRAIN Initiative data archives germane to data sharing and reuse, namely data submission and access procedures and aspects of interoperability. Second, we discuss challenges, benefits, and future opportunities, focusing on issues largely specific to sharing human data and drawing on N = 34 interviews with diverse stakeholders. The BRAIN Initiative-funded archive ecosystem faces interoperability and data stewardship challenges, such as achieving and maintaining interoperability of data and archives and harmonizing research participants’ informed consents for tiers of access for human data across multiple archives. Yet, a benefit of this distributed archive ecosystem is the ability of more specialized archives to adapt to the needs of particular research communities. Finally, the multiple archives offer ample raw material for network evolution in response to the needs of neuroscientists over time. Our first objective in this paper is to provide a guide to the BRAIN Initiative data-sharing ecosystem for readers interested in sharing and reusing neuroscience data. Second, our analysis supports the development of empirically informed policy and practice aimed at making neuroscience data more findable, accessible, interoperable, and reusable.

A comprehensive characterization of neuronal cell types, their distributions, and patterns of connectivity is critical for understanding the properties of neural circuits and how they generate behaviors. Here we review the experiences of the BRAIN Initiative Cell Census Consortium, ten pilot projects funded by the U.S. BRAIN Initiative, in developing, validating, and scaling up emerging genomic and anatomical mapping technologies for creating a complete inventory of neuronal cell types and their connections in multiple species and during development. These projects lay the foundation for a larger and longer-term effort to generate whole-brain cell atlases in species including mice and humans.

Data sharing is essential to further advance the field of neuropsychiatry. However, it raises significant ethical issues in the domains of privacy, consent, and diversity. We begin by considering the sensitive nature of much neuropsychiatric data. Next, we review relevant policies of the National Institutes of Mental Health (NIMH), a prominent funder in this field. Because data sharing in neuropsychiatry is in its infancy and rapidly evolving, the NIMH policies serve as a helpful starting point for examining ethical considerations related to the collection and distribution of neuropsychiatric data. However, we find gaps in their guidance in each of the three key ethical domains. Finally, we illustrate how examination of lessons and strategies from other contexts where sustained attention has already been given to these ethical issues may add value by suggesting specific opportunities for improvement. In particular, we highlight approaches including a three-tiered data access scheme, use of technology to enhance the data sharing component of the informed consent process, and evidence-based, targeted recruitment of underrepresented populations to support diverse data resources. Assessment of current policy and potentially helpful innovations in other fields is a necessary step in moving the field forward in an ethically responsible manner. [ABSTRACT FROM AUTHOR]

The evolution of the field of neuroscience has been propelled by the advent of novel technological capabilities, and the pace at which these capabilities are being developed has accelerated dramatically in the past decade. Capitalizing on this momentum, the United States launched the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative to develop and apply new tools and technologies for revolutionizing our understanding of the brain. In this article, we review the scientific vision for this initiative set forth by the National Institutes of Health and discuss its implications for the future of neuroscience research. Particular emphasis is given to its potential impact on the mapping and study of neural circuits, and how this knowledge will transform our understanding of the complexity of the human brain and its diverse array of behaviours, perceptions, thoughts and emotions.

O objetivo deste artigo é discutir os desafios que se colocam para a ética da neurociência na terceira década do século XXI. Discutimos aqui o projeto americano BRAIN initiative criado em 2013 e fazemos alguns questionamentos a este, especialmente no que se refere a participação e a pesquisa que está sendo feita pela agencia de pesquisa do Departamento de Defesa dos Estados Unidos da América (DARPA). Questionamos também a pesquisa sobre o cérebro e desenvolvimento de neurotecnologias que é feita pelo setor privado como as Tech giants e outras empresas bilionárias. Propomos que é necessário que haja uma grande reformulação na área da ética da neurociência com o objetivo de contribuir para colocar a neurociência e a neurotecnologia cada vez mais disponíveis para todos e a serviço da humanidade.

As the Brain Research through Advancing Neurotechnologyies, better known as the BRAIN Initiative moves forward at a rapid pace increasing attention has focused on neuroethics. The National Institutes of Health recently mandated a review of the progress of the BRAIN Initiative progress with the goal of fine-tuning its future directions. The BRAIN Working Group 2 focused its discussion on science while the BRAIN Neuroethics Subgroup focused on neuroethics. The Brain Neuroethics Subgroup deliberated for over a year collecting information and recommendations through in person and video meetings, a public one-day neuroethics symposium and soliciting input from neuroscientists and neuroethicist's. The resulting report entitled "The BRAIN Initiative and Neuroethics: Enabling and Enhancing Neuroscience Advances for Society" was accepted by Director of NIH in October of 2019. The recommendations span many BRAIN research neuroethics concerns including privacy considerations, the use of nonhuman primate model systems, neural modulation and enhancement, subject participation in BRAIN research and equity in neuroscience research. Further the group recommended a transformative project whose goal of detailing the scientific mechanisms and ethical underpinnings of consciousness is one of the most daunting issues that impact our perceptions of ourselves. It is anticipated that the report?s recommendations will provide a foundation or "roadmap" for ensuring that neuroethics and BRAIN research move forward as an integrated effort thereby insuring that BRAIN research is of the highest quality. [ABSTRACT FROM AUTHOR]

This paper presents a fully-integrated current-controlled stimulator that is powered directly from on-chip coil antenna and achieves adiabatic energy-replenishing operation without any bulky external components. Adiabatic supply voltages, which can reach a differential range of up to 7.2 V, are directly generated from an on-chip 190-MHz resonant LC tank via a self-cascading/folding rectifier network, bypassing the losses that would otherwise be introduced by the 0.8 V system supply-generating rectifier and regulator. The stimulator occupies 0.22 mm $^2$ in a 180 nm silicon-on-insulator process and produces differential currents up to 145  $\mu$ A. Using a charge replenishing scheme, the stimulator redirects the charges accumulated across the electrodes to the system power supplies for 63.1% of stimulation energy recycling. To benchmark the efficiency of stimulation, a figure of merit termed the stimulator efficiency factor (SEF) is introduced. The adiabatic power rails and energy replenishment scheme enabled our stimulator to achieve an SEF of 6.0. [ABSTRACT FROM AUTHOR]

Microscale neural technologies interface with the nervous system to record and stimulate brain tissue with high spatial and temporal resolution. These devices are being developed to understand the mechanisms that govern brain function, plasticity and cognitive learning, treat neurological diseases, or monitor and restore functions over the lifetime of the patient. Despite decades of use in basic research over days to months, and the growing prevalence of neuromodulation therapies, in many cases the lack of knowledge regarding the fundamental mechanisms driving activation has dramatically limited our ability to interpret data or fine-tune design parameters to improve long-term performance. While advances in materials, microfabrication techniques, packaging, and understanding of the nervous system has enabled tremendous innovation in the field of neural engineering, many challenges and opportunities remain at the frontiers of the neural interface in terms of both neurobiology and engineering. In this short-communication, we explore critical needs in the neural engineering field to overcome these challenges. Disentangling the complexities involved in the chronic neural interface problem requires simultaneous proficiency in multiple scientific and engineering disciplines. The critical component of advancing neural interface knowledge is to prepare the next wave of investigators who have simultaneous multi-disciplinary proficiencies with a diverse set of perspectives necessary to solve the chronic neural interface challenge. [ABSTRACT FROM AUTHOR]

Direct electrical stimulation of the human brain can elicit sensory and motor perceptions as well as recall of memories. Stimulating higher order association areas of the lateral temporal cortex in particular was reported to activate visual and auditory memory representations of past experiences (Penfield and Perot, 1963). We hypothesized that this effect could be used to modulate memory processing. Recent attempts at memory enhancement in the human brain have been focused on the hippocampus and other mesial temporal lobe structures, with a few reports of memory improvement in small studies of individual brain regions. Here, we investigated the effect of stimulation in four brain regions known to support declarative memory: hippocampus, parahippocampal neocortex, prefrontal cortex and temporal cortex. Intracranial electrode recordings with stimulation were used to assess verbal memory performance in a group of 22 patients (nine males). We show enhanced performance with electrical stimulation in the lateral temporal cortex (paired t-test, P = 0.0067), but not in the other brain regions tested. This selective enhancement was observed both on the group level, and for two of the four individual subjects stimulated in the temporal cortex. This study shows that electrical stimulation in specific brain areas can enhance verbal memory performance in humans.awx373media15704855796001. [ABSTRACT FROM AUTHOR]

Microscale neural technologies interface with the nervous system to record and stimulate brain tissue with high spatial and temporal resolution. These devices are being developed to understand the mechanisms that govern brain function, plasticity and cognitive learning, treat neurological diseases, or monitor and restore functions over the lifetime of the patient. Despite decades of use in basic research over days to months, and the growing prevalence of neuromodulation therapies, in many cases the lack of knowledge regarding the fundamental mechanisms driving activation has dramatically limited our ability to interpret data or fine-tune design parameters to improve long-term performance. While advances in materials, microfabrication techniques, packaging, and understanding of the nervous system has enabled tremendous innovation in the field of neural engineering, many challenges and opportunities remain at the frontiers of the neural interface in terms of both neurobiology and engineering. In this short-communication, we explore critical needs in the neural engineering field to overcome these challenges. Disentangling the complexities involved in the chronic neural interface problem requires simultaneous proficiency in multiple scientific and engineering disciplines. The critical component of advancing neural interface knowledge is to prepare the next wave of investigators who have simultaneous multi-disciplinary proficiencies with a diverse set of perspectives necessary to solve the chronic neural interface challenge.

The nervous systems of cnidarians, pre-bilaterian animals that diverged close to the base of the metazoan radiation, are structurally simple and thus have great potential to reveal fundamental principles of neural circuits. Unfortunately, cnidarians have thus far been relatively intractable to electrophysiological and genetic techniques and consequently have been largely passed over by neurobiologists. However, recent advances in molecular and imaging methods are fueling a renaissance of interest in and research into cnidarians nervous systems. Here, we review current knowledge on the nervous systems of cnidarian species and propose that researchers should seize this opportunity and undertake the study of members of this phylum as strategic experimental systems with great basic and translational relevance for neuroscience. [ABSTRACT FROM AUTHOR]

Recent demand and initiatives in brain research have driven significant interest toward developing chronically implantable neural interface systems with high spatiotemporal resolution and spatial coverage extending to the whole brain. Electroencephalography-based systems are noninvasive and cost efficient in monitoring neural activity across the brain, but suffer from fundamental limitations in spatiotemporal resolution. On the other hand, neural spike and local field potential (LFP) monitoring with penetrating electrodes offer higher resolution, but are highly invasive and inadequate for long-term use in humans due to unreliability in long-term data recording and risk for infection and inflammation. Alternatively, electrocorticography (ECoG) promises a minimally invasive, chronically implantable neural interface with resolution and spatial coverage capabilities that, with future technology scaling, may meet the needs of recently proposed brain initiatives. In this paper, we discuss the challenges and state-of-the-art technologies that are enabling next-generation fully implantable high-density ECoG interfaces, including details on electrodes, data acquisition front-ends, stimulation drivers, and circuits and antennas for wireless communications and power delivery. Along with state-of-the-art implantable ECoG interface systems, we introduce a modular ECoG system concept based on a fully encapsulated neural interfacing acquisition chip (ENIAC). Multiple ENIACs can be placed across the cortical surface, enabling dense coverage over wide area with high spatiotemporal resolution. The circuit and system level details of ENIAC are presented, along with measurement results. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive overview of modelling, simulation and implementation of neural networks, taking into account that two aims have emerged in this area: the improvement of our understanding of the behaviour of the nervous system and the need to find inspiration from it to build systems with the advantages provided by nature to perform certain relevant tasks. The development and evolution of different topics related to neural networks is described (simulators, implementations, and real-world applications) showing that the field has acquired maturity and consolidation, proven by its competitiveness in solving real-world problems. The paper also shows how, over time, artificial neural networks have contributed to fundamental concepts at the birth and development of other disciplines such as Computational Neuroscience, Neuro-engineering, Computational Intelligence and Machine Learning. A better understanding of the human brain is considered one of the challenges of this century, and to achieve it, as this paper goes on to describe, several important national and multinational projects and initiatives are marking the way to follow in neural-network research. [ABSTRACT FROM AUTHOR]

A proposal for an Australian Brain Initiative (ABI) is under development by members of the Australian Brain Alliance. Here we discuss the goals of the ABI, its areas of research focus, its context in the Australian research setting, and its necessity for ensuring continued success for Australian brain research. [ABSTRACT FROM AUTHOR]

Neuroscience advances have brought important ethical questions. The recent launch of two large brain projects, the United States BRAIN Initiative and the European Union Human Brain Project, should accelerate progress in understanding the brain. This article examines neuroethics in those two projects, as well as its exploration by other efforts. [ABSTRACT FROM AUTHOR]

Efficient spike acquisition techniques are needed to bridge the divide from creating large multielectrode arrays (MEA) to achieving whole-cortex electrophysiology. In this paper, we introduce generalized analog thresholding (gAT), which achieves millisecond temporal resolution with sampling rates as low as 10 Hz. Consider the torrent of data from a single 1,000-channel MEA, which would generate more than 3 GB/min using standard 30-kHz Nyquist sampling. Recent neural signal processing methods based on compressive sensing still require Nyquist sampling as a first step and use iterative methods to reconstruct spikes. Analog thresholding (AT) remains the best existing alternative, where spike waveforms are passed through an analog comparator and sampled at 1 kHz, with instant spike reconstruction. By generalizing AT, the new method reduces sampling rates another order of magnitude, detects more than one spike per interval, and reconstructs spike width. Unlike compressive sensing, the new method reveals a simple closed-form solution to achieve instant (noniterative) spike reconstruction. The base method is already robust to hardware nonidealities, including realistic quantization error and integration noise. Because it achieves these considerable specifications using hardware-friendly components like integrators and comparators, generalized AT could translate large-scale MEAs into implantable devices for scientific investigation and medical technology. [ABSTRACT FROM AUTHOR]

Previous studies have indicated that the expression of CCN3, a member of the CCN family of proteins, was tightly regulated during central nervous development and was associated with acquisition of cognitive functions in rats (Perbal, Mol Pathol 54(2):57-79, 2001; Su et al. Sheng Li Xue Bao 52(4):290-294, 2000) therefore suggesting that CCN3 might be involved in higher levels of physiological communication in the brain. In spite of the considerable amount of progress made into the understanding of neuronal organization and communication, reducing the knowledge gap between brain cellular biology and behavioral studies remains a huge challenge. Mind-to-mind communication has been the subject of numerous science fiction writings, intense research and emotional debates for many years. Scientists have tried for a long time to achieve transmission of messages between living subjects via non intrusive protocols. Thanks to the great progress made in imagery and neurosciences, another dimension of neuronal function in communication has now been documented. Two recent experimental demonstrations of direct brain to brain communication without physical contact (Grau et al. () Conscious brain-to-brain communication in humans using non-invasive technologies. PLoS One. Aug 19;9(8)- - Rao et al. () A direct brain-to-brain interface in humans. PLoS One. Nov 5;9(11)) pave the road to more sophisticated applications that could profoundly affect communications of humans with other humans, animals and machines. Although the wide use of such applications might seem a long way off, they raise quite a number of ethical, legal and societal issues. [ABSTRACT FROM AUTHOR]

Modern neuroscientific recording technologies are increasingly generating rich, multimodal data that provide unique opportunities to investigate the intricacies of brain function. However, our ability to exploit the dynamic, interactive interplay among neural processes is limited by the lack of appropriate analysis methods. In this paper, some challenging issues in neuroscience data analysis are described, and some general-purpose approaches to address such challenges are proposed. Specifically, we discuss statistical methodologies with a theme of loss functions, and hierarchical Bayesian inference methodologies from the perspective of constructing optimal mappings. These approaches are demonstrated on both simulated and experimentally acquired neural data sets to assess causal influences and track time-varying interactions among neural processes on a fine time scale. [ABSTRACT FROM PUBLISHER]

The BRAIN Initiative aims to break new ground in the scale and speed of data collection in neuroscience, requiring tools to handle data in the magnitude of yottabytes (1024). The scale, investment and organization of it are being compared to the Human Genome Project (HGP), which has exemplified "big science" for biology. In line with the trend towards Big Data in genomic research, the promise of the BRAIN Initiative, as well as the European Human Brain Project, rests on the possibility to amass vast quantities of data to model the complex interactions between the brain and behavior and inform the diagnosis and prevention of neurological disorders and psychiatric disease. Advocates of this "data driven" paradigm in neuroscience argue that harnessing the large quantities of data generated across laboratories worldwide has numerous methodological, ethical and economic advantages, but it requires the neuroscience community to adopt a culture of data sharing and open access to benefit from them. In this article, we examine the rationale for data sharing among advocates and briefly exemplify these in terms of new "open neuroscience" projects. Then, drawing on the frequently invoked model of data sharing in genomics, we go on to demonstrate the complexities of data sharing, shedding light on the sociological and ethical challenges within the realms of institutions, researchers and participants, namely dilemmas around public/private interests in data, (lack of) motivation to share in the academic community, and potential loss of participant anonymity. Our paper serves to highlight some foreseeable tensions around data sharing relevant to the emergent "open neuroscience" movement. [ABSTRACT FROM AUTHOR]

Collaboration in neuroscience is impeded by the difficulty of sharing primary data, results, and software across labs. Here, we introduce Neuroscience Data Interface (NDI), a platform-independent standard that allows an analyst to use and create software that functions independently from the format of the raw data or the manner in which the data are organized into files. The interface is rooted in a simple vocabulary that describes common apparatus and storage devices used in neuroscience experiments. Results of analyses, and analyses of analyses, are stored as documents in a scalable, queryable database that stores the relationships and history among the experiment elements and documents. The interface allows the development of an application ecosystem where applications can focus on calculation rather than data format or organization. This tool can be used by individual labs to exchange and analyze data, and it can serve to curate neuroscience data for searchable archives., (Copyright © 2022 García Murillo et al.)

Neuroscience research is changing at an incredible pace due to technological innovation and recent national and global initiatives such as the BRAIN initiative. Given the wealth of data supporting the value of course-based undergraduate research experiences (CUREs) for students, we developed and assessed a neurotechnology CURE, Mapping the Brain . The goal of the course is to immerse undergraduate and graduate students in research and to explore technological advances in neuroscience. In the laboratory portion of the course, students pursued a hypothesis-driven, collaborative National Institutes of Health (NIH) research project. Using chemogenetic technology (Designer Receptors Exclusively Activated by Designer Drugs-DREADDs) and a recombinase-based intersectional genetic strategy, students mapped norepinephrine neurons, and their projections and explored the effects of activating these neurons in vivo . In lecture, students compared traditional and cutting-edge neuroscience methodologies, analyzed primary literature, designed hypothesis-based experiments, and discussed technological limitations of studying the brain. Over two consecutive years in the Program at North Carolina State University, we assessed student learning and perceptions of learning based on Society for Neuroscience's (SfN) core concepts and essential principles of neuroscience. Using analysis of student assignments and pre/post content and perception-based course surveys, we also assessed whether the course improved student research article analysis and neurotechnology assessment. Our analyses reveal new insights and pedagogical approaches for engaging students in research and improving their critical analysis of research articles and neurotechnologies. Our data also show that our multifaceted approach increased student confidence and promoted a data focused mentality when tackling research literature. Through the integration of authentic research and a neurotechnology focus, Mapping the Brain provides a unique model as a modern neuroscience laboratory course., (Copyright © 2021 Faculty for Undergraduate Neuroscience.)

The Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative is focused on developing new tools and neurotechnologies to revolutionize our understanding of how the brain functions in health and disease, in large part to address the growing societal impact of neurological, mental health, and substance abuse disorders. Recent advances in neurotechnology are delivering unprecedented ways to interrogate and modulate brain function, and the BRAIN Initiative is focused on translation for human medical uses over the next decade. Since its inception, the NIH component of the BRAIN Initiative has utilized an iterative model of integrating ethics into the scientific trajectory of the Initiative, most recently with the creation of a Neuroethics Division of the NIH BRAIN Initiative Multi-Council Working Group. The Division serves as a resource of expertise, to help the BRAIN Initiative navigate issues involving ethics. Here we discuss the BRAIN Initiative, and its implications and aspirations for neuroethics. We also discuss new opportunities for collaboration and for integrating stakeholder voices.

The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative introduced by the Obama Administration in 2013 presents a context for integrating many STEM competencies into undergraduate neuroscience coursework. The BRAIN Initiative core principles overlap with core STEM competencies identified by the AAAS Vision and Change report and other entities. This neurobiology course utilizes the BRAIN Initiative to serve as the unifying theme that facilitates a primary emphasis on student competencies such as scientific process, scientific communication, and societal relevance while teaching foundational neurobiological content such as brain anatomy, cellular neurophysiology, and activity modulation. Student feedback indicates that the BRAIN Initiative is an engaging and instructional context for this course. Course module organization, suitable BRAIN Initiative commentary literature, sample primary literature, and important assignments are presented.