In Situ Hybridization of Brain Slices.

In situ hybridization (ISH) is an important technique for identifying gene expression at the cellular level in various organs, including brain slices. This approach hybridizes nucleic acid probes to cellular mRNA, allowing the detection of transcriptional products. Recent advances have enabled RNA preservation in formalin-fixed paraffin-embedded (FFPE) samples, making ISH applicable to brain tumor diagnosis and research. Here, we provide a concise overview of the standard application of chromogenic ISH in neuroscience research and neuropathology practice using FFPE blocks of brain slice sections.

Next Directions in the Neuroscience of Cancers Arising outside the CNS.

SUMMARY: The field of cancer neuroscience has begun to define the contributions of nerves to cancer initiation and progression; here, we highlight the future directions of basic and translational cancer neuroscience for malignancies arising outside of the central nervous system.

Neuroscience: Memory modification without catastrophe.

Adaptive behaviour is supported by changes in neuronal networks. Insight into maintaining these memories - preventing their catastrophic loss - despite further network changes occurring due to novel learning is provided in a new study.

Shortcut citations in the methods section: Frequency, problems, and strategies for responsible reuse.

Methods sections are often missing essential details. Methodological shortcut citations, in which authors cite previous papers instead of describing the method in detail, may contribute to this problem. This meta-research study used 3 approaches to examine shortcut citation use in neuroscience, biology, and psychiatry. First, we assessed current practices in more than 750 papers. More than 90% of papers used shortcut citations. Other common reasons for using citations in the methods included giving credit or specifying what was used (who or what citation) and providing context or a justification (why citation). Next, we reviewed 15 papers to determine what can happen when readers follow shortcut citations to find methodological details. While shortcut citations can be used effectively, they can also deprive readers of essential methodological details. Problems encountered included difficulty identifying or accessing the cited materials, missing or insufficient descriptions of the cited method, and shortcut citation chains. Third, we examined journal policies. Fewer than one quarter of journals had policies describing how authors should report previously described methods. We propose that methodological shortcut citations should meet 3 criteria; cited resources should provide (1) a detailed description of (2) the method used by the citing authors', and (3) be open access. Resources that do not meet these criteria should be cited to give credit, but not as shortcut citations. We outline actions that authors and journals can take to use shortcut citations responsibly, while fostering a culture of open and reproducible methods reporting.

BRAND: a platform for closed-loop experiments with deep network models.

Objective.Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g. C and C++).Approach.To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termednodes, which communicate with each other in agraphvia streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.Main results.In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1 ms chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 ms of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial (ClinicalTrials.gov Identifier: NCT00912041) performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems.Significance.By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments.

Towards building a trustworthy pipeline integrating Neuroscience Gateway and Open Science Chain.

When the scientific dataset evolves or is reused in workflows creating derived datasets, the integrity of the dataset with its metadata information, including provenance, needs to be securely preserved while providing assurances that they are not accidentally or maliciously altered during the process. Providing a secure method to efficiently share and verify the data as well as metadata is essential for the reuse of the scientific data. The National Science Foundation (NSF) funded Open Science Chain (OSC) utilizes consortium blockchain to provide a cyberinfrastructure solution to maintain integrity of the provenance metadata for published datasets and provides a way to perform independent verification of the dataset while promoting reuse and reproducibility. The NSF- and National Institutes of Health (NIH)-funded Neuroscience Gateway (NSG) provides a freely available web portal that allows neuroscience researchers to execute computational data analysis pipeline on high performance computing resources. Combined, the OSC and NSG platforms form an efficient, integrated framework to automatically and securely preserve and verify the integrity of the artifacts used in research workflows while using the NSG platform. This paper presents the results of the first study that integrates OSC-NSG frameworks to track the provenance of neurophysiological signal data analysis to study brain network dynamics using the Neuro-Integrative Connectivity tool, which is deployed in the NSG platform. Database URL: https://www.opensciencechain.org.

The neuroscience of lucid dreaming: Past, present, future.

Lucid dreaming allows conscious awareness and control of vivid dream states; however, its rarity and instability make neuroscientific experimentation challenging. Recent advances in wearable neurotechnology, large-scale collaborations, citizen neuroscience, and artificial intelligence increasingly facilitate the decoding of this intriguing phenomenon.

Aptamer Technologies in Neuroscience, Neuro-Diagnostics and Neuro-Medicine Development.

Aptamers developed using in vitro Systematic Evolution of Ligands by Exponential Enrichment (SELEX) technology are single-stranded nucleic acids 10-100 nucleotides in length. Their targets, often with specificity and high affinity, range from ions and small molecules to proteins and other biological molecules as well as larger systems, including cells, tissues, and animals. Aptamers often rival conventional antibodies with improved performance, due to aptamers' unique biophysical and biochemical properties, including small size, synthetic accessibility, facile modification, low production cost, and low immunogenicity. Therefore, there is sustained interest in engineering and adapting aptamers for many applications, including diagnostics and therapeutics. Recently, aptamers have shown promise as early diagnostic biomarkers and in precision medicine for neurodegenerative and neurological diseases. Here, we critically review neuro-targeting aptamers and their potential applications in neuroscience research, neuro-diagnostics, and neuro-medicine. We also discuss challenges that must be overcome, including delivery across the blood-brain barrier, increased affinity, and improved in vivo stability and in vivo pharmacokinetic properties.

Dynamic threat-reward neural processing under semi-naturalistic ecologically relevant scenarios.

Studies of affective neuroscience have typically employed highly controlled, static experimental paradigms to investigate the neural underpinnings of threat and reward processing in the brain. Yet our knowledge of affective processing in more naturalistic settings remains limited. Specifically, affective studies generally examine threat and reward features separately and under brief time periods, despite the fact that in nature organisms are often exposed to the simultaneous presence of threat and reward features for extended periods. To study the neural mechanisms of threat and reward processing under distinct temporal profiles, we created a modified version of the PACMAN game that included these environmental features. We also conducted two automated meta-analyses to compare the findings from our semi-naturalistic paradigm to those from more constrained experiments. Overall, our results revealed a distributed system of regions sensitive to threat imminence and a less distributed system related to reward imminence, both of which exhibited overlap yet neither of which involved the amygdala. Additionally, these systems broadly overlapped with corresponding meta-analyses, with the notable absence of the amygdala in our findings. Together, these findings suggest a shared system for salience processing that reveals a heightened sensitivity toward environmental threats compared to rewards when both are simultaneously present in an environment. The broad correspondence of our findings to meta-analyses, consisting of more tightly controlled paradigms, illustrates how semi-naturalistic studies can corroborate previous findings in the literature while also potentially uncovering novel mechanisms resulting from the nuances and contexts that manifest in such dynamic environments.

Applying the IEEE BRAIN neuroethics framework to intra-cortical brain-computer interfaces.

Objective. Brain-computer interfaces (BCIs) are neuroprosthetic devices that allow for direct interaction between brains and machines. These types of neurotechnologies have recently experienced a strong drive in research and development, given, in part, that they promise to restore motor and communication abilities in individuals experiencing severe paralysis. While a rich literature analyzes the ethical, legal, and sociocultural implications (ELSCI) of these novel neurotechnologies, engineers, clinicians and BCI practitioners often do not have enough exposure to these topics.Approach. Here, we present the IEEE Neuroethics Framework, an international, multiyear, iterative initiative aimed at developing a robust, accessible set of considerations for diverse stakeholders.Main results. Using the framework, we provide practical examples of ELSCI considerations for BCI neurotechnologies. We focus on invasive technologies, and in particular, devices that are implanted intra-cortically for medical research applications.Significance. We demonstrate the utility of our framework in exposing a wide range of implications across different intra-cortical BCI technology modalities and conclude with recommendations on how to utilize this knowledge in the development and application of ethical guidelines for BCI neurotechnologies.

Benchtop mesoSPIM: a next-generation open-source light-sheet microscope for cleared samples.

In 2015, we launched the mesoSPIM initiative, an open-source project for making light-sheet microscopy of large cleared tissues more accessible. Meanwhile, the demand for imaging larger samples at higher speed and resolution has increased, requiring major improvements in the capabilities of such microscopes. Here, we introduce the next-generation mesoSPIM ("Benchtop") with a significantly increased field of view, improved resolution, higher throughput, more affordable cost, and simpler assembly compared to the original version. We develop an optical method for testing detection objectives that enables us to select objectives optimal for light-sheet imaging with large-sensor cameras. The improved mesoSPIM achieves high spatial resolution (1.5 µm laterally, 3.3 µm axially) across the entire field of view, magnification up to 20×, and supports sample sizes ranging from sub-mm up to several centimeters while being compatible with multiple clearing techniques. The microscope serves a broad range of applications in neuroscience, developmental biology, pathology, and even physics.