Hindbrain modules differentially transform activity of single collicular neurons to coordinate movements.

Seemingly simple behaviors such as swatting a mosquito or glancing at a signpost involve the precise coordination of multiple body parts. Neural control of coordinated movements is widely thought to entail transforming a desired overall displacement into displacements for each body part. Here we reveal a different logic implemented in the mouse gaze system. Stimulating superior colliculus (SC) elicits head movements with stereotyped displacements but eye movements with stereotyped endpoints. This is achieved by individual SC neurons whose branched axons innervate modules in medulla and pons that drive head movements with stereotyped displacements and eye movements with stereotyped endpoints, respectively. Thus, single neurons specify a mixture of endpoints and displacements for different body parts, not overall displacement, with displacements for different body parts computed at distinct anatomical stages. Our study establishes an approach for unraveling motor hierarchies and identifies a logic for coordinating movements and the resulting pose.

Natural sensory context drives diverse brain-wide activity during C. elegans mating.

Natural goal-directed behaviors often involve complex sequences of many stimulus-triggered components. Understanding how brain circuits organize such behaviors requires mapping the interactions between an animal, its environment, and its nervous system. Here, we use brain-wide neuronal imaging to study the full performance of mating by the C. elegans male. We show that as mating unfolds in a sequence of component behaviors, the brain operates similarly between instances of each component but distinctly between different components. When the full sensory and behavioral context is taken into account, unique roles emerge for each neuron. Functional correlations between neurons are not fixed but change with behavioral dynamics. From individual neurons to circuits, our study shows how diverse brain-wide dynamics emerge from the integration of sensory perception and motor actions in their natural context.

Differential encoding in prefrontal cortex projection neuron classes across cognitive tasks.

Single-cell transcriptomics has been widely applied to classify neurons in the mammalian brain, while systems neuroscience has historically analyzed the encoding properties of cortical neurons without considering cell types. Here we examine how specific transcriptomic types of mouse prefrontal cortex (PFC) projection neurons relate to axonal projections and encoding properties across multiple cognitive tasks. We found that most types projected to multiple targets, and most targets received projections from multiple types, except PFC→PAG (periaqueductal gray). By comparing Ca2+ activity of the molecularly homogeneous PFC→PAG type against two heterogeneous classes in several two-alternative choice tasks in freely moving mice, we found that all task-related signals assayed were qualitatively present in all examined classes. However, PAG-projecting neurons most potently encoded choice in cued tasks, whereas contralateral PFC-projecting neurons most potently encoded reward context in an uncued task. Thus, task signals are organized redundantly, but with clear quantitative biases across cells of specific molecular-anatomical characteristics.

Volumetric Ca2+ Imaging in the Mouse Brain Using Hybrid Multiplexed Sculpted Light Microscopy.

Calcium imaging using two-photon scanning microscopy has become an essential tool in neuroscience. However, in its typical implementation, the tradeoffs between fields of view, acquisition speeds, and depth restrictions in scattering brain tissue pose severe limitations. Here, using an integrated systems-wide optimization approach combined with multiple technical innovations, we introduce a new design paradigm for optical microscopy based on maximizing biological information while maintaining the fidelity of obtained neuron signals. Our modular design utilizes hybrid multi-photon acquisition and allows volumetric recording of neuroactivity at single-cell resolution within up to 1 × 1 × 1.22 mm volumes at up to 17 Hz in awake behaving mice. We establish the capabilities and potential of the different configurations of our imaging system at depth and across brain regions by applying it to in vivo recording of up to 12,000 neurons in mouse auditory cortex, posterior parietal cortex, and hippocampus.

A dynamical model of C. elegans thermal preference reveals independent excitatory and inhibitory learning pathways.

Caenorhabditis elegans is capable of learning and remembering behaviorally relevant cues such as smells, tastes, and temperature. This is an example of associative learning, a process in which behavior is modified by making associations between various stimuli. Since the mathematical theory of conditioning does not account for some of its salient aspects, such as spontaneous recovery of extinguished associations, accurate modeling of behavior of real animals during conditioning has turned out difficult. Here, we do this in the context of the dynamics of the thermal preference of C. elegans. We quantify C. elegans thermotaxis in response to various conditioning temperatures, starvation durations, and genetic perturbations using a high-resolution microfluidic droplet assay. We model these data comprehensively, within a biologically interpretable, multi-modal framework. We find that the strength of the thermal preference is composed of two independent, genetically separable contributions and requires a model with at least four dynamical variables. One pathway positively associates the experienced temperature independently of food and the other negatively associates with the temperature when food is absent. The multidimensional structure of the association strength provides an explanation for the apparent classical temperature-food association of C. elegans thermal preference and a number of longstanding questions in animal learning, including spontaneous recovery, asymmetric response to appetitive vs. aversive cues, latent inhibition, and generalization among similar cues.

Neuroscience Needs Network Science.

The brain is a complex system comprising a myriad of interacting neurons, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such interconnected systems, offering a framework for integrating multiscale data and complexity. To date, network methods have significantly advanced functional imaging studies of the human brain and have facilitated the development of control theory-based applications for directing brain activity. Here, we discuss emerging frontiers for network neuroscience in the brain atlas era, addressing the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease. We underscore the importance of fostering interdisciplinary opportunities through workshops, conferences, and funding initiatives, such as supporting students and postdoctoral fellows with interests in both disciplines. By bringing together the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way toward a deeper understanding of the brain and its functions, as well as offering new challenges for network science.

Pathways from the superior colliculus and the nucleus of the optic tract to the posterior parietal cortex in macaque monkeys: Functional frameworks for representation updating and online movement guidance.

The posterior parietal cortex (PPC) integrates multisensory and motor-related information for generating and updating body representations and movement plans. We used retrograde transneuronal transfer of rabies virus combined with a conventional tracer in macaque monkeys to identify direct and disynaptic pathways to the arm-related rostral medial intraparietal area (MIP), the ventral lateral intraparietal area (LIPv), belonging to the parietal eye field, and the pursuit-related lateral subdivision of the medial superior temporal area (MSTl). We found that these areas receive major disynaptic pathways via the thalamus from the nucleus of the optic tract (NOT) and the superior colliculus (SC), mainly ipsilaterally. NOT pathways, targeting MSTl most prominently, serve to process the sensory consequences of slow eye movements for which the NOT is the key sensorimotor interface. They potentially contribute to the directional asymmetry of the pursuit and optokinetic systems. MSTl and LIPv receive feedforward inputs from SC visual layers, which are potential correlates for fast detection of motion, perceptual saccadic suppression and visual spatial attention. MSTl is the target of efference copy pathways from saccade- and head-related compartments of SC motor layers and head-related reticulospinal neurons. They are potential sources of extraretinal signals related to eye and head movement in MSTl visual-tracking neurons. LIPv and rostral MIP receive efference copy pathways from all SC motor layers, providing online estimates of eye, head and arm movements. Our findings have important implications for understanding the role of the PPC in representation updating, internal models for online movement guidance, eye-hand coordination and optic ataxia.

A neural circuit perspective on brain aromatase.

This review explores the role of aromatase in the brain as illuminated by a set of conserved network-level connections identified in several vertebrate taxa. Aromatase-expressing neurons are neurochemically heterogeneous but the brain regions in which they are found are highly-conserved across the vertebrate lineage. During development, aromatase neurons have a prominent role in sexual differentiation of the brain and resultant sex differences in behavior and human brain diseases. Drawing on literature primarily from birds and rodents, we delineate brain regions that express aromatase and that are strongly interconnected, and suggest that, in many species, aromatase expression essentially defines the Social Behavior Network. Moreover, in several cases the inputs to and outputs from this core Social Behavior Network also express aromatase. Recent advances in molecular and genetic tools for neuroscience now enable in-depth and taxonomically diverse studies of the function of aromatase at the neural circuit level.

Habituation as a neural algorithm for online odor discrimination.

Habituation is a form of simple memory that suppresses neural activity in response to repeated, neutral stimuli. This process is critical in helping organisms guide attention toward the most salient and novel features in the environment. Here, we follow known circuit mechanisms in the fruit fly olfactory system to derive a simple algorithm for habituation. We show, both empirically and analytically, that this algorithm is able to filter out redundant information, enhance discrimination between odors that share a similar background, and improve detection of novel components in odor mixtures. Overall, we propose an algorithmic perspective on the biological mechanism of habituation and use this perspective to understand how sensory physiology can affect odor perception. Our framework may also help toward understanding the effects of habituation in other more sophisticated neural systems.

Systems Neuroscience of Natural Behaviors in Rodents.

Animals evolved in complex environments, producing a wide range of behaviors, including navigation, foraging, prey capture, and conspecific interactions, which vary over timescales ranging from milliseconds to days. Historically, these behaviors have been the focus of study for ecology and ethology, while systems neuroscience has largely focused on short timescale behaviors that can be repeated thousands of times and occur in highly artificial environments. Thanks to recent advances in machine learning, miniaturization, and computation, it is newly possible to study freely moving animals in more natural conditions while applying systems techniques: performing temporally specific perturbations, modeling behavioral strategies, and recording from large numbers of neurons while animals are freely moving. The authors of this review are a group of scientists with deep appreciation for the common aims of systems neuroscience, ecology, and ethology. We believe it is an extremely exciting time to be a neuroscientist, as we have an opportunity to grow as a field, to embrace interdisciplinary, open, collaborative research to provide new insights and allow researchers to link knowledge across disciplines, species, and scales. Here we discuss the origins of ethology, ecology, and systems neuroscience in the context of our own work and highlight how combining approaches across these fields has provided fresh insights into our research. We hope this review facilitates some of these interactions and alliances and helps us all do even better science, together.

Laminar specific fMRI reveals directed interactions in distributed networks during language processing.

Interactions between top-down and bottom-up information streams are integral to brain function but challenging to measure noninvasively. Laminar resolution, functional MRI (lfMRI) is sensitive to depth-dependent properties of the blood oxygen level-dependent (BOLD) response, which can be potentially related to top-down and bottom-up signal contributions. In this work, we used lfMRI to dissociate the top-down and bottom-up signal contributions to the left occipitotemporal sulcus (LOTS) during word reading. We further demonstrate that laminar resolution measurements could be used to identify condition-specific distributed networks on the basis of whole-brain connectivity patterns specific to the depth-dependent BOLD signal. The networks corresponded to top-down and bottom-up signal pathways targeting the LOTS during word reading. We show that reading increased the top-down BOLD signal observed in the deep layers of the LOTS and that this signal uniquely related to the BOLD response in other language-critical regions. These results demonstrate that lfMRI can reveal important patterns of activation that are obscured at standard resolution. In addition to differences in activation strength as a function of depth, we also show meaningful differences in the interaction between signals originating from different depths both within a region and with the rest of the brain. We thus show that lfMRI allows the noninvasive measurement of directed interaction between brain regions and is capable of resolving different connectivity patterns at submillimeter resolution, something previously considered to be exclusively in the domain of invasive recordings.

Functional ultrasound imaging: A useful tool for functional connectomics?

Functional ultrasound (fUS) is a hemodynamic-based functional neuroimaging technique, primarily used in animal models, that combines a high spatiotemporal resolution, a large field of view, and compatibility with behavior. These assets make fUS especially suited to interrogating brain activity at the systems level. In this review, we describe the technical capabilities offered by fUS and discuss how this technique can contribute to the field of functional connectomics. First, fUS can be used to study intrinsic functional connectivity, namely patterns of correlated activity between brain regions. In this area, fUS has made the most impact by following connectivity changes in disease models, across behavioral states, or dynamically. Second, fUS can also be used to map brain-wide pathways associated with an external event. For example, fUS has helped obtain finer descriptions of several sensory systems, and uncover new pathways implicated in specific behaviors. Additionally, combining fUS with direct circuit manipulations such as optogenetics is an attractive way to map the brain-wide connections of defined neuronal populations. Finally, technological improvements and the application of new analytical tools promise to boost fUS capabilities. As brain coverage and the range of behavioral contexts that can be addressed with fUS keep on increasing, we believe that fUS-guided connectomics will only expand in the future. In this regard, we consider the incorporation of fUS into multimodal studies combining diverse techniques and behavioral tasks to be the most promising research avenue.

Relationships between lognormal distributions of neural properties, activity, criticality, and connectivity.

Relationships between convergence of inputs onto neurons, divergence of outputs from them, synaptic strengths, nonlinear firing response properties, and randomness of axonal ranges are systematically explored by interrelating means and variances of synaptic strengths, firing rates, and soma voltages. When self-consistency is imposed, it is found that broad distributions of synaptic strength are a necessary concomitant of the known massive convergence of inputs to individual neurons, and observed widths of lognormal distributions of synaptic strength and firing rate are explained provided the brain is in a near-critical state, consistent with independent observations. The strongest individual synapses are shown to have an effect on soma voltage comparable to the effect of all others combined, which supports suggestions that they may have a key role in neural communication. Remarkably, inclusion of moderate randomness in characteristic axonal ranges is shown to account for the observed [Formula: see text]-fold variability in two-point connectivity at a given separation and [Formula: see text]-fold overall when the known mean exponential fall-off is included, consistent with observed near-lognormal distributions. Inferred axonal deviations from straight-line paths are also consistent with independent estimates.

Subspecialization within default mode nodes characterized in 10,000 UK Biobank participants.

The human default mode network (DMN) is implicated in several unique mental capacities. In this study, we tested whether brain-wide interregional communication in the DMN can be derived from population variability in intrinsic activity fluctuations, gray-matter morphology, and fiber tract anatomy. In a sample of 10,000 UK Biobank participants, pattern-learning algorithms revealed functional coupling states in the DMN that are linked to connectivity profiles between other macroscopical brain networks. In addition, DMN gray matter volume was covaried with white matter microstructure of the fornix. Collectively, functional and structural patterns unmasked a possible division of labor within major DMN nodes: Subregions most critical for cortical network interplay were adjacent to subregions most predictive of fornix fibers from the hippocampus that processes memories and places.

Separation of hemodynamic signals from GCaMP fluorescence measured with wide-field imaging.

Wide-field calcium imaging is often used to measure brain dynamics in behaving mice. With a large field of view and a high sampling rate, wide-field imaging can monitor activity from several distant cortical areas simultaneously, revealing cortical interactions. Interpretation of wide-field images is complicated, however, by the absorption of light by hemoglobin, which can substantially affect the measured fluorescence. One approach to separating hemodynamics and calcium signals is to use multiwavelength backscatter recordings to measure light absorption by hemoglobin. Following this approach, we develop a spatially detailed regression-based method to estimate hemodynamics. This Spatial Model is based on a linear form of the Beer-Lambert relationship but is fit at every pixel in the image and does not rely on the estimation of physical parameters. In awake mice of three transgenic lines, the Spatial Model offers improved separation of hemodynamics and changes in GCaMP fluorescence. The improvement is pronounced near blood vessels and, in contrast with the Beer-Lambert equations, can remove vascular artifacts along the sagittal midline and in general permits more accurate fluorescence-based determination of neuronal activity across the cortex.NEW & NOTEWORTHY This paper addresses a well-known and strong source of contamination in wide-field calcium-imaging data: hemodynamics. To guide researchers toward the best method to separate calcium signals from hemodynamics, we compare the performance of several methods in three commonly used mouse lines and present a novel regression model that outperforms the other techniques we consider.

Dark control: The default mode network as a reinforcement learning agent.

The default mode network (DMN) is believed to subserve the baseline mental activity in humans. Its higher energy consumption compared to other brain networks and its intimate coupling with conscious awareness are both pointing to an unknown overarching function. Many research streams speak in favor of an evolutionarily adaptive role in envisioning experience to anticipate the future. In the present work, we propose a process model that tries to explain how the DMN may implement continuous evaluation and prediction of the environment to guide behavior. The main purpose of DMN activity, we argue, may be described by Markov decision processes that optimize action policies via value estimates through vicarious trial and error. Our formal perspective on DMN function naturally accommodates as special cases previous interpretations based on (a) predictive coding, (b) semantic associations, and (c) a sentinel role. Moreover, this process model for the neural optimization of complex behavior in the DMN offers parsimonious explanations for recent experimental findings in animals and humans.

Ascending vestibular pathways to parietal areas MIP and LIPv and efference copy inputs from the medial reticular formation: Functional frameworks for body representations updating and online movement guidance.

The posterior parietal cortex (PPC) serves as a sensorimotor interface by integrating multisensory signals with motor related information for generating and updating body representations and movement plans. Using retrograde transneuronal transfer of rabies virus combined with a conventional tracer, we identified direct and polysynaptic pathways to two PPC areas, the rostral medial intraparietal area (MIP) and the ventral part of the lateral intraparietal area (LIPv) in macaque monkeys. We found that rostral MIP and LIPv receive ascending vestibular pathways, and putative efference copy inputs disynaptically from the medullary medial reticular formation (MRF) where reticulospinal pathways to neck and arm motoneurons originate. LIPv receives minor disynaptic vestibular inputs, and substantial projections from the head movement-related rostral MRF, consistent with head gain modulation of LIPv activity and a role in planning gaze shifts. Rostral MIP is the target of prominent disynaptic pathways from reaching- and head movement-related MRF domains, and major ascending vestibular pathways trisynaptically from both labyrinths, explaining prominent vestibular responses and discrimination between active and passive movements demonstrated in rostral MIP and in the neighboring ventral intraparietal area, which are heavily interconnected. The findings that rostral MIP (belonging to the 'parietal reach region'), receives vestibular inputs as directly as classical vestibular areas, via a parallel channel, and efference copy signals pathways from MRF reticulospinal domains that belong to reach and head movement networks have important implications for the understanding of the role of the PPC in updating body representations and internal models for online guidance of movement.

Using Blogs as Practice Writing About Original Neuroscience Papers Enhances Students' Confidence in Their Critical Analysis of Research.

Increasing emphasis is being put on providing students with opportunities to read and write about primary scientific literature in undergraduate neuroscience education. Extensive research has indicated that students' attitudes and self-efficacy as well as writing quality improve when they are provided with opportunities for practice and feedback. Here we tested the value of using a blog format to practice writing about scientific research articles. Students were assigned small groups and did work on their own individual schedules to build toward time allotted in class to discuss the articles with their groups. Our goal was to build confidence in the students' ability to read and analyze original research articles. We found that the students in the junior-level Systems Neuroscience course had high confidence in their ability to read and analyze papers at the end of the blogging experience. Surprisingly, however, this did not manifest in a change in quality of final, higher stakes, written reports on original research articles when compared to a control sample from a previous year that did not include the blog assignments. We conclude that blogs provide a useful format for students to discuss research articles collaboratively while building confidence in their ability to analyze and discuss original neuroscience articles. Although the final reports' quality did not change compared to the earlier offering of the course, we believe that the blog experience is a valuable tool for building confidence and creating a positive experience for students in learning to read and analyze original neuroscience research articles.