Collaborating Authors


Cerebellar nuclei evolved by repeatedly duplicating a conserved cell-type set


Cerebellar nuclei, substructures of the cerebellum, transfer information from the cerebellum to other parts of the brain. Using single-cell transcriptomics, Kebschull et al. have now identified a conserved pattern of cerebellar nuclei structure that has been repeated through evolution (see the Perspective by Hatten). Ranging from mice to chickens to humans, cerebellar nuclei are made up of region-specific excitatory neurons and region-invariant inhibitory neurons. In humans, a facet connecting the cerebellum to the frontal cortex is enhanced. Science , this issue p. [eabd5059][1]; see also p. [1411][2] ### INTRODUCTION The brains of extant animals have evolved over hundreds of millions of years from simple circuits. Cell types diversified, connections elaborated, and new brain regions emerged. Models for brain region evolution range from duplication of existing regions to splitting of previously multifunctional regions and de novo assembly from existing cell types. These models, however, have not been demonstrated in vertebrate brains at cell-type resolution. ### RATIONALE We investigated brain region evolution using the cerebellar nuclei as a model system. The cerebellum is a major hindbrain structure in jawed vertebrates, comprising the cerebellar cortex and cerebellar nuclei. It is thought to act as a feedforward model for motor control and cognitive processes. The cerebellar cortex receives and processes inputs and sends outputs to the cerebellar nuclei, which route the results of cerebellar computations to the rest of the brain. Whereas the cerebellar cortex is well conserved across vertebrates, the cerebellar nuclei vary in number, with none in jawless vertebrates, one pair in cartilaginous fishes and amphibians, two pairs in reptiles and birds, and three pairs in mammals. This pattern suggests that extant cerebellar nuclei evolved from a single ancestral nucleus. Cerebellar nuclei thus provide a good model to interrogate brain region evolution. ### RESULTS We characterized the cerebellar nuclei in mice, chickens, and humans using whole-brain and spinal cord projection mapping in cleared samples, single-nucleus RNA sequencing (snRNAseq), and spatially resolved transcript amplicon readout mapping (STARmap) analysis. We first compared the projection patterns of the three cerebellar nuclei of mice. Our data reveal broad projections of all nuclei, which in common target regions are shifted relative to each other. To understand the transcriptomic differences that underlie these shifting projections, we produced a cell-type atlas of the mouse cerebellar nuclei using snRNAseq. We discovered three region-invariant inhibitory cell classes and 15 region-specific excitatory cell types. Excitatory cell types fall into two classes with distinct gene expression and electrophysiological properties. Members of each class are present in every nucleus and are putative sister cell types. STARmap analysis in mice revealed that the organizational unit of the cerebellar nuclei is cytoarchitectonically distinguishable subnuclei, each of which contains the three inhibitory and two excitatory classes. To test whether this archetypal subnucleus is also the evolutionary unit of the cerebellar nuclei, we performed snRNAseq and STARmap on the chicken cerebellar nuclei. We identified four subnuclei, three of which had direct orthologs in mice. Each chicken subnucleus contained the same cell-type set of three inhibitory and two excitatory classes already identified in mice, confirming our hypothesis. Cerebellar nuclei vary in size across vertebrates. In particular, the human lateral nucleus is markedly expanded. To understand this expansion, we performed snRNAseq in humans. We found that the medial and interposed nuclei maintained the archetypal cerebellar nuclei composition. However, the lateral nucleus expanded one excitatory cell class at the expense of the other. Conditional tracing in the mouse lateral nucleus revealed that the cell class expanded in humans preferentially accesses lateral frontal cortices via specific intermediate thalamic nuclei. ### CONCLUSION We identified a conserved cell-type set that forms an archetypal cerebellar nucleus as the unit of cerebellar nuclei organization and evolution. We propose that this archetypal nucleus was repeatedly duplicated during evolution, accompanied primarily by transcriptomic divergence of excitatory neurons and shifts in their projection patterns. Our data support a model of duplication-and-divergence of entire cell-type sets for brain region evolution. ![Figure][3] Evolution of the cerebellar nuclei. Comparative single-cell transcriptomics in mice, chickens, and humans (top left; neurons are color-coded by type), spatial transcriptomic analyses in mice and chickens (top right; neurons are color-coded by type in raw and processed data), and central nervous system (CNS)–wide projection mapping in mice (bottom left; axons in red in a three-dimensional mouse brain) revealed the unit of cerebellar nuclei organization and evolution. This unit (red box) comprises three inhibitory and two excitatory neuron classes (each colored circle indicates a neuron class). Extant cerebellar nuclei likely derived from the duplication and divergence of this unit, with more dynamic gene expression in excitatory neurons (changed color hues), along with projection target shifts. How have complex brains evolved from simple circuits? Here we investigated brain region evolution at cell-type resolution in the cerebellar nuclei, the output structures of the cerebellum. Using single-nucleus RNA sequencing in mice, chickens, and humans, as well as STARmap spatial transcriptomic analysis and whole–central nervous system projection tracing, we identified a conserved cell-type set containing two region-specific excitatory neuron classes and three region-invariant inhibitory neuron classes. This set constitutes an archetypal cerebellar nucleus that was repeatedly duplicated to form new regions. The excitatory cell class that preferentially funnels information to lateral frontal cortices in mice becomes predominant in the massively expanded human lateral nucleus. Our data suggest a model of brain region evolution by duplication and divergence of entire cell-type sets. [1]: /lookup/doi/10.1126/science.abd5059 [2]: /lookup/doi/10.1126/science.abf4483 [3]: pending:yes

NextMind's brain-computer interface is ready for developers


If science fiction has taught us anything, it's that we have an innate fantasy to control things with our minds. Whether it's duping Stormtroopers about which droids they are looking for or Professor X's mutant powers, we clearly hope that magical powers lie dormant within. The truth is, the power is definitely there, but you might just need something like NextMind's headset to use it (for now). NextMind is the latest in a long line of companies trying to harness the brain as a means of controlling our digital world. At first, its take on things may seem familiar: Don a headset which places a sensor on the back of your head, and it'll detect your brainwaves which can then be translated into digital actions.

Improve Glaucoma Assessment with Brain-Computer Interface and Machine Learning


Humans are good at picking up sensory cues that cause a drastic change or pain. But what about gradual changes, like deteriorating eyesight, when the changes are so slight and so slow that people would not be able to notice it. The basis for healthcare in many countries is for patients to look for medical care when they are symptomatic. With this approach, individuals would need to know what their symptoms are. That people would not be able to notice until a later stage, where their quality of life is significantly affected due to vision loss.

The Neural Coding Framework for Learning Generative Models Artificial Intelligence

One way to understand how the brain adapts to its environment is to view it as a type of generative pattern-creation model [20], one that is engaged in a never-ending process of self-correction, often without external teaching signals (or labels) [53]. Under this perspective, the brain is continuously making predictions about elements of its environment, a process that allows it to infer useful representations of the sensory data it receives [56] as well as to synthesize novel patterns, which could serve as the potential basis for long-term planning and imagination itself [12]. From the theoretical viewpoint of predictive processing, the brain could be likened to a hierarchical model whose levels are implemented by neurons (or clusters of neurons). If levels are likened to regions of the brain, the neurons at one level (region) attempt to predict the state of neurons at another level (region) and adjust/correct their local model synaptic parameters based on how different their predictions were from the observed signal. Furthermore, these neurons utilize various mechanisms to laterally stimulate/suppress each other [40] to facilitate contextual processing (such as grouping/segmenting visual components of objects in a scene).

Iterative VAE as a predictive brain model for out-of-distribution generalization Artificial Intelligence

Our ability to generalize beyond training data to novel, out-of-distribution, image degradations is a hallmark of primate vision. The predictive brain, exemplified by predictive coding networks (PCNs), has become a prominent neuroscience theory of neural computation. Motivated by the recent successes of variational autoencoders (VAEs) in machine learning, we rigorously derive a correspondence between PCNs and VAEs. This motivates us to consider iterative extensions of VAEs (iVAEs) as plausible variational extensions of the PCNs. We further demonstrate that iVAEs generalize to distributional shifts significantly better than both PCNs and VAEs. In addition, we propose a novel measure of recognizability for individual samples which can be tested against human psychophysical data. Overall, we hope this work will spur interest in iVAEs as a promising new direction for modeling in neuroscience.

How a brain controls a computer


Neuroscience Most brain-computer interfaces (BCIs) that use neuron recordings have analyzed the activity of those neurons that contribute directly to the decoded BCI output. Liu and Schieber found that although only a few primary motor cortex (M1) units controlled a closed-loop BCI, substantial numbers of non-BCI units were likewise modulated in relation to the task, not only in frontal motor areas (area M1 and the dorsal and ventral premotor cortex) but also in parietal areas (somatosensory cortex and the anterior intraparietal area). All of these cortical areas thus participated both in natural control of voluntary limb movement and in a more general system for closed-loop control of an effector being moved to a visual target. Harnessing the activity of units from multiple cortical areas might help in the development of next-generation BCIs. eNeuro 7 , ENEURO.0376-20.2020 (2020).

In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes


CRISPR targeting in vivo, especially in mammals, can be difficult and time consuming when attempting to determine the effects of a single gene. However, such studies may be required to identify pathological gene variants with effects in specific cells along a developmental trajectory. To study the function of genes implicated in autism spectrum disorders (ASDs), Jin et al. applied a gene-editing and single-cell–sequencing system, Perturb-Seq, to knock out 35 ASD candidate genes in multiple mice embryos (see the Perspective by Treutlein and Camp). This method identified networks of gene expression in neuronal and glial cells that suggest new functions in ASD-related genes. Science , this issue p. [eaaz6063][1]; see also p. [1038][2] ### INTRODUCTION Human genetic studies have revealed long lists of genes and loci associated with risk for many diseases and disorders, but to systematically evaluate their phenotypic effects remains challenging. Without any a priori knowledge, these risk genes could affect any cellular processes in any cell type or tissue, which creates an enormous search space for identifying possible downstream effects. New high-throughput approaches are needed to functionally dissect these large gene sets across a spectrum of cell types in vivo. ### RATIONALE Analysis of trio-based whole-exome sequencing has implicated a large number of de novo loss-of-function variants that contribute to autism spectrum disorder and developmental delay (ASD/ND) risk. Such de novo variants often have large effect sizes, thus providing a key entry point for mechanistic studies. We have developed in vivo Perturb-Seq to allow simultaneous assessment of the individual phenotypes of a panel of such risk genes in the context of the developing mouse brain. ### RESULTS Using CRISPR-Cas9, we introduced frameshift mutations in 35 ASD/ND risk genes in pools, within the developing mouse neocortex in utero, followed by single-cell transcriptomic analysis of perturbed cells from the early postnatal brain. We analyzed five broad cell classes—cortical projection neurons, cortical inhibitory neurons, astrocytes, oligodendrocytes, and microglia—and selected cells that had received only single perturbations. Using weighted gene correlation network analysis, we identified 14 covarying gene modules that represent transcriptional programs expressed in different classes of cortical cells. These modules included both those affecting common biological processes across multiple cell subsets and others representing cell type–specific features restricted to certain subsets. We estimated the effect size of each perturbation on each of the 14 gene modules by fitting a joint linear regression model, estimating how module gene expression in cells from each perturbation group deviated from their expression level in internal control cells. Perturbations in nine ASD/ND genes had significant effects across five modules across four cell classes, including cortical projection neurons, cortical inhibitory neurons, astrocytes, and oligodendrocytes. Some of these results were validated by using a single-perturbation model as well as a germline-modified mutant mouse model. To establish whether the perturbation-associated gene modules identified in the mouse cerebral cortex are relevant to human biology and ASD/ND pathology, we performed co-analyses of data from ASD and control human brains and human cerebral organoids. Both gene expression and gene covariation (“modularity”) of several of the gene modules identified in the mouse Perturb-Seq analysis are conserved in human brain tissue. Comparison with single-cell data from ASD patients showed overlap in both affected cell types and transcriptomic phenotypes. ### CONCLUSION In vivo Perturb-Seq can serve as a scalable tool for systems genetic studies of large gene panels to reveal their cell-intrinsic functions at single-cell resolution in complex tissues. In this work, we demonstrated the application of in vivo Perturb-Seq to ASD/ND risk genes in the developing brain. This method can be applied across diverse diseases and tissues in the intact organism. ![Figure][3] In vivo Perturb-Seq identified neuron and glia-associated effects by perturbations of risk genes implicated in ASD/ND. De novo risk genes in this study were chosen from Satterstrom et al. (2018), and co-analysis with ASD patient data at bottom right is from Velmeshev et al. (2019); full citations for both are included in the full article online. The number of disease risk genes and loci identified through human genetic studies far outstrips the capacity to systematically study their functions. We applied a scalable genetic screening approach, in vivo Perturb-Seq, to functionally evaluate 35 autism spectrum disorder/neurodevelopmental delay (ASD/ND) de novo loss-of-function risk genes. Using CRISPR-Cas9, we introduced frameshift mutations in these risk genes in pools, within the developing mouse brain in utero, followed by single-cell RNA-sequencing of perturbed cells in the postnatal brain. We identified cell type–specific and evolutionarily conserved gene modules from both neuronal and glial cell classes. Recurrent gene modules and cell types are affected across this cohort of perturbations, representing key cellular effects across sets of ASD/ND risk genes. In vivo Perturb-Seq allows us to investigate how diverse mutations affect cell types and states in the developing organism. [1]: /lookup/doi/10.1126/science.aaz6063 [2]: /lookup/doi/10.1126/science.abf3661 [3]: pending:yes

Heard About Brain Computer Interfaces?


In simple terms, it is a communication interface between the brain and an external device, your brain speaks, and the machine responds. It can also be a bidirectional communication pathway where the machine can pass on the information to the brain known as active BCI. What information are we talking about here? It is the information on the electrical activity of the brain from the surface of the scalp. Electrodes are placed on the scalp to pick up the electric potentials generated by the brain, and this information is sent to the machine, this is Passive BCI.

Sensorimotor representation learning for an "active self" in robots: A model survey Artificial Intelligence

For example, sensorimotor birth, infants spend their first months of life undergoing experiences are used to learn a forward model, and a many developmental milestones to incrementally develop forward model can be the basis for learning high-level the representation of their body. This body schema is cognitive conceptual representations. In agreement with related mainly to touch, proprioception, and vision (see Schillaci et al. (2016), we aim to go deeper into the role of Table 1) as these sensory modalities continue to develop multisensory information collected through exploration from the fetal stage (see Hoffmann, 2017; Adolph in the formation of an agent's body and peripersonal and Joh, 2007 for reviews). Later on, the representation space representation, and how these sensorimotor representations of the surrounding space of the body--the PPS--is affect the agent's sense of the active self, aggregated from the proprioceptive and exteroceptive including the sense of agency and the sense of body modalities (see Table 1). In addition, infants develop ownership. Thus, motor explorations will be mentioned the capability to generate motor actions corresponding but not exhaustively discussed in this surveyed work.

Brain mapping, from molecules to networks


CATEGORY WINNER: CELL AND MOLECULAR BIOLOGY William E. Allen William E. Allen received his undergraduate degree from Brown University in 2012, M.Phil. in Computational Biology from the University of Cambridge in 2013, and Ph.D. in Neurosciences from Stanford University in 2019. At Stanford, he worked to develop new tools for the large-scale characterization of neural circuit structure and function, which he applied to understand the neural basis of thirst. After completing his Ph.D., William started as an independent Junior Fellow in the Society of Fellows at Harvard University, where he is developing and applying new approaches to map mammalian brain function and dysfunction over an animal's life span. [ ][1] Charting what the pioneering neuroanatomist Santiago Ramón y Cajal called the “impenetrable jungle” of the brain ([ 1 ][2]) presents one of biology's greatest challenges. How do billions of neurons, wired through trillions of connections, work together to produce cognition and behavior? Like an orchestra, wherein many instruments played simultaneously produce a sound greater than the sum of its parts, thought and behavior emerge from communication between ensembles of molecularly distinct neurons distributed throughout vast neural circuits. Although we know much about the properties of individual genes, cells, and circuits (see the figure, panel A), a vast gap lies between the function of each brain component and an animal's behavior. Bridging this gap has proven technically and conceptually difficult. Inspired by the fact that the development of high-throughput DNA sequencing led geneticists to shift focus from individual genes to the entire genome, I wanted to develop approaches that could simultaneously link multiple levels of the brain, from molecules to neurons to brain-wide neural networks. My goal was to capture a global perspective while maintaining the high resolution and specificity necessary to understand the function of individual components at each level. This new viewpoint, I hoped, would reveal how the collective properties of the brain's building blocks give rise to behavior. During my doctoral studies at Stanford University with Karl Deisseroth and Liqun Luo, I developed new methods to map the architecture and activity of mammalian neural circuits. I applied these approaches to understand the neural basis of thirst, a fundamental regulator of behavior ([ 2 ][3]). Need-based motivational drives, such as hunger and thirst, direct animals to satisfy specific physiological imperatives important for survival ([ 3 ][4]). Despite decades of research, at the beginning of my studies it was unclear how the activity of neurons that sense these needs causes an animal to engage in specific motivated behaviors (e.g., eating or drinking) to maintain homeostasis ([ 3 ][4]). Thirst, a relatively simple yet important drive, thus seemed the perfect model system for investigating multiple levels in the brain. I first traced thirst motivational drive from cellular gene expression to a circuit mechanism. Using a new version of targeted recombination in active populations (TRAP2), a tool to genetically label neurons according to their activity, I found that neurons in the median preoptic nucleus (MnPO) of the hypothalamus became activated in thirsty mice ([ 4 ][5]) (see the figure, panel C). Single-cell RNA sequencing revealed that these neurons formed a single molecularly defined cell type. Artificial activation of these neurons caused mice to drink water within seconds, whereas their inhibition prevented mice from drinking, which suggested that these MnPO neurons were master regulators of thirst. Drinking water also gradually reduced the activity of these neurons. Finally, activation of these neurons was aversive. Together, these results suggested a surprising “drive reduction” model of thirst motivation: Genetically hard-wired thirst neurons become active when mice need hydration, which causes mice to drink water. This ability to ascribe specific functional relevance to genetically defined neurons inspired me to develop new techniques to map cells within their native tissue architecture in even greater molecular detail. To this end, I co-developed STARmap, an approach for highly multiplexed in situ RNA sequencing to measure the expression of hundreds of genes simultaneously within a brain section at the level of single mRNA molecules ([ 5 ][6]) (see the figure, panel B ). In combination with genetic markers of activity, this technique powerfully describes the molecular identity of behaviorally activated neurons and their neighbors at single-cell resolution. ![Figure][7] New large-scale, high-resolution approaches to bridging multiple levels of brain function A new approach to brain function mapping. (A) An illustration of the levels of brain function and how they are interlinked. (B to D) New approaches to bridging levels: (B) STARm ap amplicons barcoding 1020 RNA species simultaneously with single-molecule resolution in the mouse visual cortex. (C) Genetic labeling of neurons according to activity reveals thirst neurons in the median preoptic nucleus of the hypothalamus, used to identify the motivational mechanism of thirst drive. (D) Brain-wide activity map of the response of thousands of neurons across dozens of brain regions to a water-predicting sensory cue, in thirsty or sated mice, reveals widespread broadcasting of thirst state. GRAPHIC: N. DESAI/ SCIENCE FROM W. ALLEN, WANG ET AL . ([ 5 ][6]), ALLEN ET AL . ( 4 ), ALLEN ET AL . ([ 9 ][8]) Despite these insights, a question remained: How do thirst-sensitive neurons deep in the brain coordinate activity in distributed circuits spanning sensory perception, cognition, and motor output to produce motivated behavior? I found that MnPO thirst neurons projected to many brain regions potentially serving different behavioral roles ([ 4 ][5]), but the gap between individual neurons and brain-wide networks was daunting. Earlier in graduate school, I had developed several new microscopy techniques to characterize brain-wide ([ 6 ][9]) or neocortex- wide ([ 7 ][10]) activity, which revealed that global neural activity was present during even simple motivated behaviors. However, because of the mammalian brain's opacity, these approaches were limited in their ability to record fast neural activity throughout the brain at the scale required to understand thirst motivation. Fortunately, however, developments in microelectronics enabled me to construct global maps of neuronal activity with microsecond-level temporal resolution. Using advanced “Neuropixels” probes ([ 8 ][11]), thin silicon needles that can be acutely inserted into the brain to record the electrical signals of hundreds of neurons simultaneously, I developed an experimental approach to record the activity of huge neuronal ensembles across the brain and reconstruct the anatomical location of each recorded cell ([ 9 ][8]). Applying this technique, I mapped the brain-wide flow of activity through ∼24,000 single neurons during thirst-motivated behavior ([ 9 ][8]) (see the figure, panel D). My experiments revealed that this simple behavior produced an unexpectedly global coordination of activity throughout the brain. By observing how activity changed as mice drank water, as well as directly stimulating hypothalamic thirst neurons, I showed that this activity wave was dependent on the animal's motivational state. Surprisingly, the activity of a few hundred thirst neurons instantly modulated the state of the entire brain. Even more surprisingly, I found many neurons, distributed throughout the brain, that directly encoded thirst. These results suggest that even simple behaviors, such as thirst, are emergent properties of the entire brain. I hope these new approaches will at last enable us to comprehend the rules that transform distributed patterns of electrical activity in neural circuits into thoughts, emotions, and perceptions. Understanding how molecules, neurons, and networks interact to shape these rules will have a sweeping impact on our understanding of brain function in health and disease. 1. [↵][12]“Mas, por desgracia, faltábanos el arma poderosa con que descuajar la selva impenetrable de la substancia gris…” ([ 10 ][13]). 2. [↵][14]1. C. A. Zimmerman, 2. D. E. Leib, 3. Z. A. Knight , Nat. Rev. Neurosci. 18, 459 (2017). [OpenUrl][15][CrossRef][16][PubMed][17] 3. [↵][18]1. S. M. Sternson , Neuron 77, 810 (2013). [OpenUrl][19][CrossRef][20][PubMed][21][Web of Science][22] 4. [↵][23]1. W. E. Allen et al ., Science 357, 1149 (2017). [OpenUrl][24][Abstract/FREE Full Text][25] 5. [↵][26]1. X. Wang et al ., Science 361, eaat5691 (2018). [OpenUrl][27][Abstract/FREE Full Text][28] 6. [↵][29]1. L. Ye et al ., Cell 165, 1776 (2016). [OpenUrl][30][CrossRef][31][PubMed][32] 7. [↵][33]1. W. E. Allen et al ., Neuron 94, 891 (2017). [OpenUrl][34][CrossRef][35][PubMed][36] 8. [↵][37]1. J. J. Jun et al ., Nature 551, 232 (2017). [OpenUrl][38][CrossRef][39][PubMed][40] 9. [↵][41]1. W. E. Allen et al ., Science 364, eeav3932 (2019). [OpenUrl][42] 10. [↵][43]1. S. Ramón y Cajal , Recuerdos de mi vida: Historia de mi labor científica (Moya, Madrid, 1917). 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