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Wild populations are increasingly threatened by human-mediated climate change and land use changes. As populations decline, the probability of inbreeding increases, along with the potential for negative effects on individual fitness. Detecting and characterizing runs of homozygosity (ROHs) is a popular strategy for assessing the extent of individual inbreeding present in a population and can also shed light on the genetic mechanisms contributing to inbreeding depression. Here, we analyze simulated and empirical datasets to demonstrate the downstream effects of program selection and long-term demographic history on ROH inference, leading to context-dependent biases in the results. Through a sensitivity analysis we evaluate how various parameter values impact ROH-calling results, highlighting its utility as a tool for parameter exploration. Our results indicate that ROH inferences are sensitive to factors such as sequencing depth and ROH length distribution, with bias direction and magnitude varying with demographic history and the programs used. Estimation biases are particularly pronounced at lower sequencing depths, potentially leading to either underestimation or overestimation of inbreeding. These results are particularly important for the management of endangered species, as underestimating inbreeding signals in the genome can substantially undermine conservation initiatives. We also found that small true ROHs can be incorrectly lumped together and called as longer ROHs, leading to erroneous inference of recent inbreeding. To address these challenges, we suggest using a combination of ROH detection tools and ROH length-specific inferences, along with sensitivity analysis, to generate robust and context-appropriate population inferences regarding inbreeding history. We outline these recommendations for ROH estimation at multiple levels of sequencing effort, which are typical of conservation genomics studies.

A confluence of technological capabilities is creating an opportunity for machine learning and artificial intelligence (AI) to enable \"smart\" nanoengineered brain machine interfaces (BMI). This new generation of technologies will be able to communicate with the brain in ways that support contextual learning and adaptation to changing functional requirements. This applies to both invasive technologies aimed at restoring neurological function, as in the case of neural prosthesis, as well as non-invasive technologies enabled by signals such as electroencephalograph (EEG). Advances in computation, hardware, and algorithms that learn and adapt in a contextually dependent way will be able to leverage the capabilities that nanoengineering offers the design and functionality of BMI. We explore the enabling capabilities that these devices may exhibit, why they matter, and the state of the technologies necessary to build them. We also discuss a number of open technical challenges and problems that will need to be solved in order to achieve this.

Identification of the cellular players and molecular messengers that communicate neuronal activity to the vasculature driving cerebral hemodynamics is important for (1) the basic understanding of cerebrovascular regulation and (2) interpretation of functional Magnetic Resonance Imaging (fMRI) signals. Using a combination of optogenetic stimulation and 2-photon imaging in mice, we demonstrate that selective activation of cortical excitation and inhibition elicits distinct vascular responses and identify the vasoconstrictive mechanism as Neuropeptide Y (NPY) acting on Y1 receptors. The latter implies that task-related negative Blood Oxygenation Level Dependent (BOLD) fMRI signals in the cerebral cortex under normal physiological conditions may be mainly driven by the NPY-positive inhibitory neurons. Further, the NPY-Y1 pathway may offer a potential therapeutic target in cerebrovascular disease. Unlike other cells in the body, brain cells contain almost no energy reserves and rely on blood vessels for continuous supply of oxygen. A change in the brain’s activity can cause these blood vessels to either dilate or constrict, which alters the supply to match the change in demand. However, it is not known which signals cause these changes in the blood vessels. Previous studies have shown that individual blood vessels in an intact brain tend to dilate when the brain’s activity increases, and constrict when brain activity is inhibited. However, these studies were based on correlations, and there was no direct evidence that the inhibitory cells cause blood vessels to constrict. Uhlirova, Kılıç et al. now provide such evidence. The experiments made use of mice that had been genetically modified such that the excitatory or inhibitory nerve cells in their brains could be selectively activated by shining a blue light on the brain’s surface. The vessels in the outer millimeter of the gray matter of each mouse’s brain were imaged in detail, both before and after the blue light was used to activate the nerve cells. The experiments reveal that both excitatory and inhibitory nerve cells can cause blood vessels in the brain to dilate. However, blood vessels in the brain will only constrict in response to inhibitory nerve cells. Uhlirova, Kılıç et al. went on to identify a molecule called Neuropeptide Y (or NPY short) as a signal that triggers the constriction of the blood vessels. This signaling molecule is released by a specific sub-type of inhibitory nerve cell and it binds to a receptor protein on the brain’s blood vessels to make them constrict. These findings suggest that NPY and its receptor on blood vessels may offer promising targets for drugs to treat diseases of the brain’s blood vessels. Further studies are now needed to identify the signals responsible for the dilation of blood vessels in the brain.

Neural progenitor cells were encapsulated in vitro within a three-dimensional network of nanofibers formed by self-assembly of peptide amphiphile molecules. The self-assembly is triggered by mixing cell suspensions in media with dilute aqueous solutions of the molecules, and cells survive the growth of the nanofibers around them. These nanofibers were designed to present to cells the neurite-promoting laminin epitope IKVAV at nearly van der Waals density. Relative to laminin or soluble peptide, the artificial nanofiber scaffold induced very rapid differentiation of cells into neurons, while discouraging the development of astrocytes. This rapid selective differentiation is linked to the amplification of bioactive epitope presentation to cells by the nanofibers.

The category of directed graphs is isomorphic to a particular category whose objects are labeled undirected bipartite graphs and whose morphisms are undirected graph morphisms that respect the labeling. Based on this isomorphism, we begin by showing that the class of all directed graphs is a Graph Isomorphism Complete class. Afterwards, by extending this categorical framework to weighted prime graphs, we prove that the categories of multidirected graphs with and without self-loops are each isomorphic to a particular category of weighted prime graphs. Consequently, we prove that these classes of multidirected graphs are also Graph Isomorphism Complete.

Despite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective—direct and causal—connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect, and apparent links. Our method can be generally applied to the functional characterization of any in vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in vitro hiPSC-derived neuronal cultures.

The computational properties of the human brain arise from an intricate interplay between billions of neurons connected in complex networks. However, our ability to study these networks in healthy human brain is limited by the necessity to use non-invasive technologies. This is in contrast to animal models where a rich, detailed view of cellular-level brain function with cell-type-specific molecular identity has become available due to recent advances in microscopic optical imaging and genetics. Thus, a central challenge facing neuroscience today is leveraging these mechanistic insights from animal studies to accurately draw physiological inferences from non-invasive signals in humans. On the essential path towards this goal is the development of a detailed ‘bottom-up’ forward model bridging neuronal activity at the level of cell-type-specific populations to non-invasive imaging signals. The general idea is that specific neuronal cell types have identifiable signatures in the way they drive changes in cerebral blood flow, cerebral metabolic rate of O2 (measurable with quantitative functional Magnetic Resonance Imaging), and electrical currents/potentials (measurable with magneto/electroencephalography). This forward model would then provide the ‘ground truth’ for the development of new tools for tackling the inverse problem—estimation of neuronal activity from multimodal non-invasive imaging data. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

Voltage imaging and “all-optical electrophysiology” in human induced pluripotent stem cell (hiPSC)-derived neurons have opened unprecedented opportunities for high-throughput phenotyping of activity in neurons possessing unique genetic backgrounds of individual patients. While prior all-optical electrophysiology studies relied on genetically encoded voltage indicators, here, we demonstrate an alternative protocol using a synthetic voltage sensor and genetically encoded optogenetic actuator that generate robust and reproducible results. We demonstrate the functionality of this method by measuring spontaneous and evoked activity in three independent hiPSC-derived neuronal cell lines with distinct genetic backgrounds.

Key Points Nanotechnologies are technologies that use engineered materials or devices with a functional organization on the nanometre scale (that is, one billionth of a metre) in at least one dimension, typically ranging from 1 to ∼100 nanometres. This implies that at least some aspect of the material or device can be manipulated and controlled by physical and/or chemical means at nanometre resolutions, which results in functional properties that are unique to the engineered technology and not shown by its constituent elements. Nanotechnologies are therefore primarily defined by the functional properties that determine how they interact. Although the chemical and/or physical make up of a nanomaterial or device is important in the overall technological process, it is secondary to their engineering and functional properties. Applications of nanotechnology in basic neuroscience include those that investigate molecular, cellular and physiological processes. One example is nanoengineered materials and approaches for promoting neuronal adhesion and growth to help us understand the underlying neurobiology or to support other technologies designed to interact with neurons in vivo (for example, coating of recording or stimulating electrodes). Another is nanoengineered materials and approaches for directly interacting, recording and/or stimulating neurons at a molecular level. A third example is imaging applications using nanotechnology tools, such as chemically functionalized semiconductor quantum dots. Applications of nanotechnology in clinical neuroscience focus on research aimed at limiting and reversing neuropathological disease states. These include nanotechnology approaches designed to support and/or promote the functional regeneration of the nervous system; neuroprotective strategies, in particular those that use fullerene derivatives; and nanotechnology approaches that facilitate the delivery of drugs and small molecules across the blood–brain barrier. The challenges associated with using nanotechnology applications in neuroscience are numerous, but the impact that they can have on understanding how the nervous system works, how it fails in disease and how we can intervene at the molecular level are significant. The capacity to exploit drugs, small molecules, neurotransmitters and neural developmental factors offers the potential to tailor technologies to particular applications. For example, neural developmental factors, such as the cadherins, laminins and bone morphometric protein families, as well as their receptors, can be manipulated in new ways. Nanotechnology offers the ability to take advantage of the functional specificity of these molecules by incorporating them into engineered materials and devices to have highly specific, targeted effects. The main technical challenges that are encountered when using nanotechnology in neuroscience include the need for greater specificity, multiple induced physiological functions and minimal side effects. In vivo there are other unique challenges that must be considered, including the inherent complexity of the CNS and its anatomically restrictive nature. Neuroscientists have a unique role in developing nanotechnologies. Both researchers and clinicians need to identify potential applications of nanotechnology in neuroscience and neurology to maximize their impact. Scientists with other specialties can develop powerful platform technologies and even provide neuroscience-specific examples, but it is only with direct input from and in partnership with neuroscientists that broad neurophysiological and clinical applications can be properly formulated and addressed. Nanotechnology holds great promises in all scientific disciplines. Silva discusses the basic concepts of nanotechnology, its current applications in basic and clinical neuroscience, and the conceptual and technical challenges it faces in tackling the complexities of the nervous system. Nanotechnologies exploit materials and devices with a functional organization that has been engineered at the nanometre scale. The application of nanotechnology in cell biology and physiology enables targeted interactions at a fundamental molecular level. In neuroscience, this entails specific interactions with neurons and glial cells. Examples of current research include technologies that are designed to better interact with neural cells, advanced molecular imaging technologies, materials and hybrid molecules used in neural regeneration, neuroprotection, and targeted delivery of drugs and small molecules across the blood–brain barrier.

Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids, a super-selective algorithm has been proposed for inferring the effective connectivity networks from multi-electrode array data. In this paper, we apply a novel statistical method called spectral mirror estimation to the time series of inferred effective connectivity organoid networks. This method produces a one-dimensional iso-mirror representation of the dynamics of the time series of the networks which exhibits a piecewise linear structure. A classical change point algorithm is then applied to this representation, which successfully detects a change point coinciding with the neuroscientifically significant time inhibitory neurons start appearing and the percentage of astrocytes increases dramatically. This finding demonstrates the potential utility of applying the iso-mirror dynamic structure discovery method to inferred effective connectivity time series of organoid networks.