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.

Neuromodulatory Control of Long-Term Behavioral Patterns and Individuality across Development.

Animals generate complex patterns of behavior across development that may be shared or unique to individuals. Here, we examine the contributions of developmental programs and individual variation to behavior by monitoring single Caenorhabditis elegans nematodes over their complete developmental trajectories and quantifying their behavior at high spatiotemporal resolution. These measurements reveal reproducible trajectories of spontaneous foraging behaviors that are stereotyped within and between developmental stages. Dopamine, serotonin, the neuropeptide receptor NPR-1, and the TGF-ß peptide DAF-7 each have stage-specific effects on behavioral trajectories, implying the existence of a modular temporal program controlled by neuromodulators. In addition, a fraction of individuals within isogenic populations raised in controlled environments have consistent, non-genetic behavioral biases that persist across development. Several neuromodulatory systems increase or decrease the degree of non-genetic individuality to shape sustained patterns of behavior across the population.

Molecular and Circuit-Dynamical Identification of Top-Down Neural Mechanisms for Restraint of Reward Seeking.

Reward-seeking behavior is fundamental to survival, but suppression of this behavior can be essential as well, even for rewards of high value. In humans and rodents, the medial prefrontal cortex (mPFC) has been implicated in suppressing reward seeking; however, despite vital significance in health and disease, the neural circuitry through which mPFC regulates reward seeking remains incompletely understood. Here, we show that a specific subset of superficial mPFC projections to a subfield of nucleus accumbens (NAc) neurons naturally encodes the decision to initiate or suppress reward seeking when faced with risk of punishment. A highly resolved subpopulation of these top-down projecting neurons, identified by 2-photon Ca2+ imaging and activity-dependent labeling to recruit the relevant neurons, was found capable of suppressing reward seeking. This natural activity-resolved mPFC-to-NAc projection displayed unique molecular-genetic and microcircuit-level features concordant with a conserved role in the regulation of reward-seeking behavior, providing cellular and anatomical identifiers of behavioral and possible therapeutic significance.

Deep Brain Stimulation for Obsessive-Compulsive Disorder and Depression.

The field of stereotactic neurosurgery developed more than 70 years ago to address a therapy gap for patients with severe psychiatric disorders. In the decades since, it has matured tremendously, benefiting from advances in clinical and basic sciences. Deep brain stimulation (DBS) for severe, treatment-resistant psychiatric disorders is currently poised to transition from a stage of empiricism to one increasingly rooted in scientific discovery. Current drivers of this transition are advances in neuroimaging, but rapidly emerging ones are neurophysiological-as we understand more about the neural basis of these disorders, we will more successfully be able to use interventions such as invasive stimulation to restore dysfunctional circuits to health. Paralleling this transition is a steady increase in the consistency and quality of outcome data. Here, we focus on obsessive-compulsive disorder and depression, two topics that have received the most attention in terms of trial volume and scientific effort.

From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response.

Detailed descriptions of brain-scale sensorimotor circuits underlying vertebrate behavior remain elusive. Recent advances in zebrafish neuroscience offer new opportunities to dissect such circuits via whole-brain imaging, behavioral analysis, functional perturbations, and network modeling. Here, we harness these tools to generate a brain-scale circuit model of the optomotor response, an orienting behavior evoked by visual motion. We show that such motion is processed by diverse neural response types distributed across multiple brain regions. To transform sensory input into action, these regions sequentially integrate eye- and direction-specific sensory streams, refine representations via interhemispheric inhibition, and demix locomotor instructions to independently drive turning and forward swimming. While experiments revealed many neural response types throughout the brain, modeling identified the dimensions of functional connectivity most critical for the behavior. We thus reveal how distributed neurons collaborate to generate behavior and illustrate a paradigm for distilling functional circuit models from whole-brain data.

High-Throughput, High-Resolution Mapping of Protein Localization in Mammalian Brain by In Vivo Genome Editing.

A scalable and high-throughput method to identify precise subcellular localization of endogenous proteins is essential for integrative understanding of a cell at the molecular level. Here, we developed a simple and generalizable technique to image endogenous proteins with high specificity, resolution, and contrast in single cells in mammalian brain tissue. The technique, single-cell labeling of endogenous proteins by clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9-mediated homology-directed repair (SLENDR), uses in vivo genome editing to insert a sequence encoding an epitope tag or a fluorescent protein to a gene of interest by CRISPR-Cas9-mediated homology-directed repair (HDR). Single-cell, HDR-mediated genome editing was achieved by delivering the editing machinery to dividing neuronal progenitors through in utero electroporation. We demonstrate that SLENDR allows rapid determination of the localization and dynamics of many endogenous proteins in various cell types, regions, and ages in the brain. Thus, SLENDR provides a high-throughput platform to map the subcellular localization of endogenous proteins with the resolution of micro- to nanometers in the brain.

Mapping of Brain Activity by Automated Volume Analysis of Immediate Early Genes.

Understanding how neural information is processed in physiological and pathological states would benefit from precise detection, localization, and quantification of the activity of all neurons across the entire brain, which has not, to date, been achieved in the mammalian brain. We introduce a pipeline for high-speed acquisition of brain activity at cellular resolution through profiling immediate early gene expression using immunostaining and light-sheet fluorescence imaging, followed by automated mapping and analysis of activity by an open-source software program we term ClearMap. We validate the pipeline first by analysis of brain regions activated in response to haloperidol. Next, we report new cortical regions downstream of whisker-evoked sensory processing during active exploration. Last, we combine activity mapping with axon tracing to uncover new brain regions differentially activated during parenting behavior. This pipeline is widely applicable to different experimental paradigms, including animal species for which transgenic activity reporters are not readily available.

Functional Ultrasound Neuroimaging.

Functional ultrasound (fUS) is a neuroimaging method that uses ultrasound to track changes in cerebral blood volume as an indirect readout of neuronal activity at high spatiotemporal resolution. fUS is capable of imaging head-fixed or freely behaving rodents and of producing volumetric images of the entire mouse brain. It has been applied to many species, including primates and humans. Now that fUS is reaching maturity, it is being adopted by the neuroscience community. However, the nature of the fUS signal and the different implementations of fUS are not necessarily accessible to nonspecialists. This review aims to introduce these ultrasound concepts to all neuroscientists. We explain the physical basis of the fUS signal and the principles of the method, present the state of the art of its hardware implementation, and give concrete examples of current applications in neuroscience. Finally, we suggest areas for improvement during the next few years.

Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI.

Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements.

Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis.

Systems-level identification and analysis of cellular circuits in the brain will require the development of whole-brain imaging with single-cell resolution. To this end, we performed comprehensive chemical screening to develop a whole-brain clearing and imaging method, termed CUBIC (clear, unobstructed brain imaging cocktails and computational analysis). CUBIC is a simple and efficient method involving the immersion of brain samples in chemical mixtures containing aminoalcohols, which enables rapid whole-brain imaging with single-photon excitation microscopy. CUBIC is applicable to multicolor imaging of fluorescent proteins or immunostained samples in adult brains and is scalable from a primate brain to subcellular structures. We also developed a whole-brain cell-nuclear counterstaining protocol and a computational image analysis pipeline that, together with CUBIC reagents, enable the visualization and quantification of neural activities induced by environmental stimulation. CUBIC enables time-course expression profiling of whole adult brains with single-cell resolution.

Molecular profiling of neurons based on connectivity.

The complexity and cellular heterogeneity of neural circuitry presents a major challenge to understanding the role of discrete neural populations in controlling behavior. While neuroanatomical methods enable high-resolution mapping of neural circuitry, these approaches do not allow systematic molecular profiling of neurons based on their connectivity. Here, we report the development of an approach for molecularly profiling projective neurons. We show that ribosomes can be tagged with a camelid nanobody raised against GFP and that this system can be engineered to selectively capture translating mRNAs from neurons retrogradely labeled with GFP. Using this system, we profiled neurons projecting to the nucleus accumbens. We then used an AAV to selectively profile midbrain dopamine neurons projecting to the nucleus accumbens. By comparing the captured mRNAs from each experiment, we identified a number of markers specific to VTA dopaminergic projection neurons. The current method provides a means for profiling neurons based on their projections.

Psychiatric disorders: diagnosis to therapy.

Recent findings in a range of scientific disciplines are challenging the conventional wisdom regarding the etiology, classification, and treatment of psychiatric disorders. This Review focuses on the current state of the psychiatric diagnostic nosology and recent progress in three areas: genomics, neuroimaging, and therapeutics development. The accelerating pace of novel and unexpected findings is transforming the understanding of mental illness and represents a hopeful sign that the approaches and models that have sustained the field for the past 40 years are yielding to a flood of new data and presaging the emergence of a new and more powerful scientific paradigm.

Mobile cognition: imaging the human brain in the 'real world'.

Cognitive neuroscience studies in humans have enabled decades of impactful discoveries but have primarily been limited to recording the brain activity of immobile participants in a laboratory setting. In recent years, advances in neuroimaging technologies have enabled recordings of human brain activity to be obtained during freely moving behaviours in the real world. Here, we propose that these mobile neuroimaging methods can provide unique insights into the neural mechanisms of human cognition and contribute to the development of novel treatments for neurological and psychiatric disorders. We further discuss the challenges associated with studying naturalistic human behaviours in complex real-world settings as well as strategies for overcoming them. We conclude that mobile neuroimaging methods have the potential to bring about a new era of cognitive neuroscience in which neural mechanisms can be studied with increased ecological validity and with the ability to address questions about natural behaviour and cognitive processes in humans engaged in dynamic real-world experiences.

Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight.

Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.

Network models to enhance the translational impact of cross-species studies.

Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.

The impact of the human thalamus on brain-wide information processing.

The thalamus is a small, bilateral structure in the diencephalon that integrates signals from many areas of the CNS. This critical anatomical position allows the thalamus to influence whole-brain activity and adaptive behaviour. However, traditional research paradigms have struggled to attribute specific functions to the thalamus, and it has remained understudied in the human neuroimaging literature. Recent advances in analytical techniques and increased accessibility to large, high-quality data sets have brought forth a series of studies and findings that (re-)establish the thalamus as a core region of interest in human cognitive neuroscience, a field that otherwise remains cortico-centric. In this Perspective, we argue that using whole-brain neuroimaging approaches to investigate the thalamus and its interaction with the rest of the brain is key for understanding systems-level control of information processing. To this end, we highlight the role of the thalamus in shaping a range of functional signatures, including evoked activity, interregional connectivity, network topology and neuronal variability, both at rest and during the performance of cognitive tasks.

Brain charts for the human lifespan.

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

Reproducible brain-wide association studies require thousands of individuals.

Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1-3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain-behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available-with a total sample size of around 50,000 individuals-to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.

Light-Sheet Microscopy in Neuroscience.

Light-sheet microscopy is an imaging approach that offers unique advantages for a diverse range of neuroscience applications. Unlike point-scanning techniques such as confocal and two-photon microscopy, light-sheet microscopes illuminate an entire plane of tissue, while imaging this plane onto a camera. Although early implementations of light sheet were optimized for longitudinal imaging of embryonic development in small specimens, emerging implementations are capable of capturing light-sheet images in freely moving, unconstrained specimens and even the intact in vivo mammalian brain. Meanwhile, the unique photobleaching and signal-to-noise benefits afforded by light-sheet microscopy's parallelized detection deliver the ability to perform volumetric imaging at much higher speeds than can be achieved using point scanning. This review describes the basic principles and evolution of light-sheet microscopy, followed by perspectives on emerging applications and opportunities for both imaging large, cleared, and expanded neural tissues and high-speed, functional imaging in vivo.

Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging.

Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.