A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging

Probing neuronal activity with genetically encoded calcium and voltage fluorescent indicators

Monitoring neural activity in individual neurons is crucial for understanding neural circuits and brain functions. The emergence of optical imaging technologies has dramatically transformed the field of neuroscience, enabling detailed observation of large-scale neuronal populations with both cellular and subcellular resolution. This transformation will be further accelerated by the integration of these imaging technologies and advanced big data analysis. Genetically encoded fluorescent indicators to detect neural activity with high signal-to-noise ratios are pivotal in this advancement. In recent years, these indicators have undergone significant developments, greatly enhancing the understanding of neural dynamics and networks. This review highlights the recent progress in genetically encoded calcium and voltage indicators and discusses the future direction of imaging techniques with big data analysis that deepens our understanding of the complexities of the brain.

DELTA: a method for brain-wide measurement of synaptic protein turnover reveals localized plasticity during learning

Synaptic plasticity alters neuronal connections in response to experience, which is thought to underlie learning and memory. However, the loci of learning-related synaptic plasticity, and the degree to which plasticity is localized or distributed, remain largely unknown. Here we describe a new method, DELTA, for mapping brain-wide changes in synaptic protein turnover with single-synapse resolution, based on Janelia Fluor dyes and HaloTag knock-in mice. During associative learning, the turnover of the ionotropic glutamate receptor subunit GluA2, an indicator of synaptic plasticity, was enhanced in several brain regions, most markedly hippocampal area CA1. More broadly distributed increases in the turnover of synaptic proteins were observed in response to environmental enrichment. In CA1, GluA2 stability was regulated in an input-specific manner, with more turnover in layers containing input from CA3 compared to entorhinal cortex. DELTA will facilitate exploration of the molecular and circuit basis of learning and memory and other forms of plasticity at scales ranging from single synapses to the entire brain.

Transparent, metal-free PEDOT:PSS neural interfaces for simultaneous recording of low-noise electrophysiology and artifact-free two-photon imaging

Simultaneous two-photon imaging and electrophysiological recordings offer considerable potential for advancing neurological research and therapies. However, traditional metal-based neural interfaces suffer from photoelectric artifacts, while existing transparent implants rely on opaque interconnect lines to address conductivity limitations. Herein, we developed an optically transparent poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) neural electrode array with transparent electrodes and interconnect lines. Through a formamide, phosphoric acid, and ethylene glycol treatment, the metal-free PEDOT:PSS array achieved an impedance of 45.8 kΩ (at 1 kHz) even with a 20 × 20 µm² size. This advanced performance surpasses previous metal-free transparent neural interfaces and facilitates precise electrophysiological recordings, including extracellular action potentials and low-noise local field potentials. In vivo experiments demonstrated artifact-free two-photon imaging and reliable neural signal acquisition, while biocompatibility tests confirmed negligible cytotoxicity or immune responses. The developed metal-free PEDOT:PSS array provides a robust platform for neural recording and bioimaging, representing an advancement in transparent neural interface technology and integrated optical modalities.

Suite3D: Volumetric cell detection for two-photon microscopy

In two-photon imaging of neuronal activity it is common to acquire 3-dimensional volumes. However, these volumes are typically processed plane by plane, leading to duplicated cells across planes, reduced signal-to-noise ratio per cell, uncorrected axial movement, and missed cells. To overcome these limitations, we introduce Suite3D, a volumetric cell detection pipeline. Suite3D corrects for 3D brain motion, estimating axial motion and improving estimates of lateral motion. It detects neurons using 3D correlation, which improves the signal-to-background ratio and detectability of cells. Finally, it performs 3D segmentation, detecting cells across imaging planes. We validated Suite3D with data from conventional multi-plane microscopes and advanced volumetric microscopes, at various resolutions and in various brain regions. Suite3D successfully detected cells appearing on multiple imaging planes, improving cell detectability and signal quality, avoiding duplications, and running >20x faster than a prior volumetric pipeline. Suite3D offers a powerful solution for analyzing volumetric two-photon data.

Imaging 3D cell cultures with optical microscopy

Three-dimensional (3D) cell cultures have gained popularity in recent years due to their ability to represent complex tissues or organs more faithfully than conventional two-dimensional (2D) cell culture. This article reviews the application of both 2D and 3D microscopy approaches for monitoring and studying 3D cell cultures. We first summarize the most popular optical microscopy methods that have been used with 3D cell cultures. We then discuss the general advantages and disadvantages of various microscopy techniques for several broad categories of investigation involving 3D cell cultures. Finally, we provide perspectives on key areas of technical need in which there are clear opportunities for innovation. Our goal is to guide microscope engineers and biomedical end users toward optimal imaging methods for specific investigational scenarios and to identify use cases in which additional innovations in high-resolution imaging could be helpful.

Imaging the cerebral vasculature at different scales: translational tools to investigate the neurovascular interfaces

The improvements in imaging technology opened up the possibility to investigate the structure and function of cerebral vasculature and the neurovascular unit with unprecedented precision and gaining deep insights not only on the morphology of the vessels but also regarding their function and regulation related to the cerebral activity. In this review, we will dissect the different imaging capabilities regarding the cerebrovascular tree, the neurovascular unit, the haemodynamic response function, and thus, the vascular-neuronal coupling. We will discuss both clinical and preclinical setting, with a final discussion on the current scenery in cerebrovascular imaging where magnetic resonance imaging and multimodal microscopy emerge as the most potent and versatile tools, respectively, in the clinical and preclinical context.

Foundation model of neural activity predicts response to new stimulus types

The complexity of neural circuits makes it challenging to decipher the brain's algorithms of intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brain's computational objectives and neural coding. However, it is difficult for such models to generalize beyond their training distribution, limiting their utility. The emergence of foundation models1 trained on vast datasets has introduced a new artificial intelligence paradigm with remarkable generalization capabilities. Here we collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. Beyond neural response prediction, the model also accurately predicted anatomical cell types, dendritic features and neuronal connectivity within the MICrONS functional connectomics dataset2. Our work is a crucial step towards building foundation models of the brain. As neuroscience accumulates larger, multimodal datasets, foundation models will reveal statistical regularities, enable rapid adaptation to new tasks and accelerate research.

Learning produces an orthogonalized state machine in the hippocampus

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, 'state cells'). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.

The MICrONS Consortium, Bae JA, Baptiste M, Baptiste MR, Bishop CA, Bodor AL, Brittain D, Brooks V, Buchanan J, Bumbarger DJ, Castro MA, Celii B, Cobos E, Collman F, da Costa NM, Danskin B, Dorkenwald S, Elabbady L, Fahey PG, Fliss T, Froudarakis E, Gager J, Gamlin C, Gray-Roncal W, Halageri A, Hebditch J, Jia Z, Joyce E, Ellis-Joyce J, Jordan C, Kapner D, Kemnitz N, Kinn S, Kitchell LM, Koolman S, Kuehner K, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Matelsky J, McReynolds S, Miranda E, Mitchell E, Mondal SS, Moore M, Mu S, Muhammad T, Nehoran B, Neace E, Ogedengbe O, Papadopoulos C, Papadopoulos S, Patel S, Vega GJYP, Pitkow X, Popovych S, Ramos A, Reid RC, Reimer J, Rivlin PK, Rose V, Sauter ZM, Schneider-Mizell CM, Seung HS, Silverman B, Silversmith W, Sterling A, Sinz FH, Smith CL, Swanstrom R, Suckow S, Takeno M, Tan ZH, Tolias AS, Torres R, Turner NL, Walker EY, Wang T, Wanner A, Wester BA, Williams G, Williams S, Willie K, Willie R, Wong W, Wu J, Xu C, Yang R, Yatsenko D, Ye F, Yin W, Young R, Yu SC, Xenes D, Zhang C. Functional connectomics spanning multiple areas of mouse visual cortex

Understanding the brain requires understanding neurons' functional responses to the circuit architecture shaping them. Here we introduce the MICrONS functional connectomics dataset with dense calcium imaging of around 75,000 neurons in primary visual cortex (VISp) and higher visual areas (VISrl, VISal and VISlm) in an awake mouse that is viewing natural and synthetic stimuli. These data are co-registered with an electron microscopy reconstruction containing more than 200,000 cells and 0.5 billion synapses. Proofreading of a subset of neurons yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Released as an open-access resource, this dataset includes the tools for data retrieval and analysis1,2. Accompanying studies describe its use for comprehensive characterization of cell types3-6, a synaptic level connectivity diagram of a cortical column4, and uncovering cell-type-specific inhibitory connectivity that can be linked to gene expression data4,7. Functionally, we identify new computational principles of how information is integrated across visual space8, characterize novel types of neuronal invariances9 and bring structure and function together to uncover a general principle for connectivity between excitatory neurons within and across areas10,11.

Functional connectomics reveals general wiring rule in mouse visual cortex

Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain computes. In mouse primary visual cortex, excitatory neurons with similar response properties are more likely to be synaptically connected1-8; however, broader connectivity rules remain unknown. Here we leverage the millimetre-scale MICrONS dataset to analyse synaptic connectivity and functional properties of neurons across cortical layers and areas. Our results reveal that neurons with similar response properties are preferentially connected within and across layers and areas-including feedback connections-supporting the universality of 'like-to-like' connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections beyond what could be explained by the proximity of axons and dendrites. We also discovered a higher-order rule whereby postsynaptic neuron cohorts downstream of presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Recurrent neural networks trained on a simple classification task develop connectivity patterns that mirror both pairwise and higher-order rules, with magnitudes similar to those in MICrONS data. Ablation studies in these recurrent neural networks reveal that disrupting like-to-like connections impairs performance more than disrupting random connections. These findings suggest that these connectivity principles may have a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.

Glutamate indicators with increased sensitivity and tailored deactivation rates

Identifying the input-output operations of neurons requires measurements of synaptic transmission simultaneously at many of a neuron's thousands of inputs in the intact brain. To facilitate this goal, we engineered and screened 3365 variants of the fluorescent protein glutamate indicator iGluSnFR3 in neuron culture, and selected variants in the mouse visual cortex. Two variants have high sensitivity, fast activation (< 2 ms) and deactivation times tailored for recording large populations of synapses (iGluSnFR4s, 153 ms) or rapid dynamics (iGluSnFR4f, 26 ms). By imaging action-potential evoked signals on axons and visually-evoked signals on dendritic spines, we show that iGluSnFR4s/4f primarily detect local synaptic glutamate with single-vesicle sensitivity. The indicators detect a wide range of naturalistic synaptic transmission, including in the vibrissal cortex layer 4 and in hippocampal CA1 dendrites. iGluSnFR4 increases the sensitivity and scale (4s) or speed (4f) of tracking information flow in neural networks in vivo.

Modeling conditional distributions of neural and behavioral data with masked variational autoencoders

Extracting the relationship between high-dimensional neural recordings and complex behavior is a ubiquitous problem in neuroscience. Encoding and decoding models target the conditional distribution of neural activity given behavior and vice versa, while dimensionality reduction techniques extract low-dimensional representations thereof. Variational autoencoders (VAEs) are flexible tools for inferring such low-dimensional embeddings but struggle to accurately model arbitrary conditional distributions such as those arising in neural encoding and decoding, let alone simultaneously. Here, we present a VAE-based approach for calculating such conditional distributions. We first validate our approach on a task with known ground truth. Next, we retrieve conditional distributions over masked body parts of walking flies. Finally, we decode motor trajectories from neural activity in a monkey-reach task and query the same VAE for the encoding distribution. Our approach unifies dimensionality reduction and learning conditional distributions, allowing the scaling of common analyses in neuroscience to today's high-dimensional multi-modal datasets.

Three-photon population imaging of subcortical brain regions

Recording activity from large cell populations in deep neural circuits is essential for understanding brain function. Three-photon (3P) imaging is an emerging technology that allows for imaging of structure and function in subcortical brain structures. However, increased tissue heating, as well as the low repetition rate sources inherent to 3P imaging, have limited the fields of view (FOV) to areas of ≤0.3 mm 2 . Here we present a Large Imaging Field of view Three-photon (LIFT) microscope with a FOV of >3 mm 2 . LIFT combines high numerical aperture (NA) optimized sampling, using a custom scanning module, with deep learning-based denoising, to enable population imaging in deep brain regions. We demonstrate non-invasive calcium imaging in the mouse brain from >1500 cells across CA1, the surrounding white matter, and adjacent deep layers of the cortex, and show population imaging with high signal-to-noise in the rat cortex at a depth of 1.2 mm. The LIFT microscope was built with all off-the-shelf components and allows for a flexible choice of imaging scale and rate.

Standardized measurements for monitoring and comparing multiphoton microscope systems

The goal of this protocol is to improve the characterization and performance standardization of multiphoton microscopy hardware across a large user base. We purposefully focus on hardware and only briefly touch on software and data analysis routines where relevant. Here we cover the measurement and quantification of laser power, pulse width optimization, field of view, resolution and photomultiplier tube performance. The intended audience is scientists with little expertise in optics who either build or use multiphoton microscopes in their laboratories. They can use our procedures to test whether their multiphoton microscope performs well and produces consistent data over the lifetime of their system. Individual procedures are designed to take 1-2 h to complete without the use of expensive equipment. The procedures listed here help standardize the microscopes and facilitate the reproducibility of data across setups.

A large field of view 2- and 3-photon microscope

A new multiphoton fluorescence microscope has been developed, offering cellular resolution across a large field of view deep within biological tissues. This opens new possibilities across a range of biological sciences, particularly within neuroscience where optical approaches can reveal signaling in real time throughout an extended network of cells distributed through the brain of an awake, behaving mouse.

TWINKLE: An open-source two-photon microscope for teaching and research

Many laboratories use two-photon microscopy through commercial suppliers, or homemade designs of considerable complexity. The integrated nature of these systems complicates customization, troubleshooting, and training on the principles of two-photon microscopy. Here, we present "Twinkle": a microscope for Two-photon Imaging in Neuroscience, and Kit for Learning and Education. It is a fully open, high performing and easy-to-set-up microscope that can effectively be used for both education and research. The instrument features a >1 mm field of view, using a modern objective with 3 mm working distance and 2 inch diameter optics combined with GaAsP photomultiplier tubes to maximize the fluorescence signal. We document our experiences using this system as a teaching tool in several two week long workshops, exemplify scientific use cases, and conclude with a broader note on the place of our work in the growing space of open scientific instrumentation.

Volumetric trans-scale imaging of massive quantity of heterogeneous cell populations in centimeter-wide tissue and embryo

We established a volumetric trans-scale imaging system with an ultra-large field-of-view (FOV) that enables simultaneous observation of millions of cellular dynamics in centimeter-wide three-dimensional (3D) tissues and embryos. Using a custom-made giant lens system with a magnification of ×2 and a numerical aperture (NA) of 0.25, and a CMOS camera with more than 100 megapixels, we built a trans-scale scope AMATERAS-2, and realized fluorescence imaging with a transverse spatial resolution of approximately 1.1 µm across an FOV of approximately 1.5×1.0 cm2. The 3D resolving capability was realized through a combination of optical and computational sectioning techniques tailored for our low-power imaging system. We applied the imaging technique to 1.2 cm-wide section of mouse brain, and successfully observed various regions of the brain with sub-cellular resolution in a single FOV. We also performed time-lapse imaging of a 1-cm-wide vascular network during quail embryo development for over 24 hr, visualizing the movement of over 4.0×105 vascular endothelial cells and quantitatively analyzing their dynamics. Our results demonstrate the potential of this technique in accelerating production of comprehensive reference maps of all cells in organisms and tissues, which contributes to understanding developmental processes, brain functions, and pathogenesis of disease, as well as high-throughput quality check of tissues used for transplantation medicine.

Transparent MXene Microelectrode Arrays for Multimodal Mapping of Neural Dynamics

Transparent microelectrode arrays have proven useful in neural sensing, offering a clear interface for monitoring brain activity without compromising high spatial and temporal resolution. The current landscape of transparent electrode technology faces challenges in developing durable, highly transparent electrodes while maintaining low interface impedance and prioritizing scalable processing and fabrication methods. To address these limitations, we introduce artifact-resistant transparent MXene microelectrode arrays optimized for high spatiotemporal resolution recording of neural activity. With 60% transmittance at 550 nm, these arrays enable simultaneous imaging and electrophysiology for multimodal neural mapping. Electrochemical characterization shows low impedance of 563 ± 99 kΩ at 1 kHz and a charge storage capacity of 58 mC cm⁻² without chemical doping. In vivo experiments in rodent models demonstrate the transparent arrays' functionality and performance. In a rodent model of chemically-induced epileptiform activity, we tracked ictal wavefronts via calcium imaging while simultaneously recording seizure onset. In the rat barrel cortex, we recorded multi-unit activity across cortical depths, showing the feasibility of recording high-frequency electrophysiological activity. The transparency and optical absorption properties of Ti₃C₂Tx MXene microelectrodes enable high-quality recordings and simultaneous light-based stimulation and imaging without contamination from light-induced artifacts.

Compact module for video-rate image mosaics in two-photon microscopy

Biological applications using multiphoton microscopy increasingly seek a larger field of view while maintaining sufficient temporal sampling to observe dynamic biological processes. Multiphoton imaging also requires high numerical aperture microscope objectives to realize efficient non-linear excitation and collection of fluorescence. This combination of low-magnification and high-numerical aperture poses a challenge for system design. To address this, the use of a liquid crystal polarization grating stack is proposed here to temporally sequence through multiple fields of view. This solution pans the native field of view with minimal latency and zero inertial movement of either the microscope or biological sample. Implemented as a simple add-on unit to existing multi-photon microscopes, this device increases the total field size by 4x, covering up to 7.6mm2. Performance constraints and functional demonstration of imaging neural activity are presented.

Distributed representations of temporally accumulated reward prediction errors in the mouse cortex

Reward prediction errors (RPEs) quantify the difference between expected and actual rewards, serving to refine future actions. Although reinforcement learning (RL) provides ample theoretical evidence suggesting that the long-term accumulation of these error signals improves learning efficiency, it remains unclear whether the brain uses similar mechanisms. To explore this, we constructed RL-based theoretical models and used multiregional two-photon calcium imaging in the mouse dorsal cortex. We identified a population of neurons whose activity was modulated by varying degrees of RPE accumulation. Consequently, RPE-encoding neurons were sequentially activated within each trial, forming a distributed assembly. RPE representations in mice aligned with theoretical predictions of RL, emerging during learning and being subject to manipulations of the reward function. Interareal comparisons revealed a region-specific code, with higher-order cortical regions exhibiting long-term encoding of RPE accumulation. These results present an additional layer of complexity in cortical RPE computation, potentially augmenting learning efficiency in animals.

Bridging Neuroscience and AI: Environmental Enrichment as a model for forward knowledge transfer in continual learning

Continual learning (CL) refers to an agent's capability to learn from a continuous stream of data and transfer knowledge without forgetting old information. One crucial aspect of CL is forward transfer, i.e., improved and faster learning on a new task by leveraging information from prior knowledge. While this ability comes naturally to biological brains, it poses a significant challenge for artificial intelligence (AI). Here, we suggest that environmental enrichment (EE) can be used as a biological model for studying forward transfer, inspiring human-like AI development. EE refers to animal studies that enhance cognitive, social, motor, and sensory stimulation and is a model for what, in humans, is referred to as 'cognitive reserve'. Enriched animals show significant improvement in learning speed and performance on new tasks, typically exhibiting forward transfer. We explore anatomical, molecular, and neuronal changes post-EE and discuss how artificial neural networks (ANNs) can be used to predict neural computation changes after enriched experiences. Finally, we provide a synergistic way of combining neuroscience and AI research that paves the path toward developing AI capable of rapid and efficient new task learning.

The Impact of Optical Undersampling on the Ca2+ Signal Resolution in Ca2+ Imaging of Spontaneous Neuronal Activity

BACKGROUND

In neuroscience, Ca2+ imaging is a prevalent technique used to infer neuronal electrical activity, often relying on optical signals recorded at low sampling rates (3 to 30 Hz) across multiple neurons simultaneously. This study investigated whether increasing the sampling rate preserves critical information that may be missed at slower acquisition speeds.

METHODS

Primary neuronal cultures were prepared from the cortex of newborn pups. Neurons were loaded with Oregon Green BAPTA-1 AM (OGB1-AM) fluorescent indicator. Spontaneous neuronal activity was recorded at low (14 Hz) and high (500 Hz) sampling rates, and the same neurons (n = 269) were analyzed under both conditions. We compared optical signal amplitude, duration, and frequency.

RESULTS

Although recurring Ca2+ transients appeared visually similar at 14 Hz and 500 Hz, quantitative analysis revealed significantly faster rise times and shorter durations (half-widths) at the higher sampling rate. Small-amplitude Ca2+ transients, undetectable at 14 Hz, became evident at 500 Hz, particularly in the neuropil (putative dendrites and axons), but not in nearby cell bodies. Large Ca2+ transients exhibited greater amplitudes and faster temporal dynamics in dendrites compared with somas, potentially due to the higher surface-to-volume ratio of dendrites. In neurons bulk-loaded with OGB1-AM, cell nucleus-mediated signal distortions were observed in every neuron examined (n = 57). Specifically, two regions of interest (ROIs) on different segments of the same cell body displayed significantly different signal amplitudes and durations due to dye accumulation in the nucleus.

CONCLUSIONS

Our findings reveal that Ca2+ signal undersampling leads to three types of information loss: (1) distortion of rise times and durations for large-amplitude transients, (2) failure to detect small-amplitude transients in cell bodies, and (3) omission of small-amplitude transients in the neuropil.

Window into the Brain: In Vivo Multiphoton Imaging

Decoding the principles underlying neuronal information processing necessitates the emergence of techniques and methodologies to monitor multiscale brain networks in behaving animals over long periods of time. Novel advances in biophotonics, specifically progress in multiphoton microscopy, combined with the development of optical indicators for neuronal activity have provided the possibility to concurrently track brain functions at scales ranging from individual neurons to thousands of neurons across connected brain regions. This Review presents state-of-the-art multiphoton imaging modalities and optical indicators for in vivo brain imaging, highlighting recent advancements and current challenges in the field.

TWINKLE: An open-source two-photon microscope for teaching and research

Many laboratories use two-photon microscopy through commercial suppliers, or homemade designs of considerable complexity. The integrated nature of these systems complicates customization, troubleshooting, and training on the principles of two-photon microscopy. Here, we present "Twinkle": a microscope for Two-photon Imaging in Neuroscience, and Kit for Learning and Education. It is a fully open, high performing and easy-to-set-up microscope that can effectively be used for both education and research. The instrument features a > 1 mm field of view, using a modern objective with 3 mm working distance and 2 inch diameter optics combined with GaAsP photomultiplier tubes to maximize the fluorescence signal. We document our experiences using this system as a teaching tool in several two week long workshops, exemplify scientific use cases, and conclude with a broader note on the place of our work in the growing space of open scientific instrumentation.

CaBLAM! A high-contrast bioluminescent Ca2+ indicator derived from an engineered Oplophorus gracilirostris luciferase

Ca2+ plays many critical roles in cell physiology and biochemistry, leading researchers to develop a number of fluorescent small molecule dyes and genetically encodable probes that optically report changes in Ca2+ concentrations in living cells. Though such fluorescence-based genetically encoded Ca2+ indicators (GECIs) have become a mainstay of modern Ca2+ sensing and imaging, bioluminescence-based GECIs-probes that generate light through oxidation of a small-molecule by a luciferase or photoprotein-have several distinct advantages over their fluorescent counterparts. Bioluminescent tags do not photobleach, do not suffer from nonspecific autofluorescent background, and do not lead to phototoxicity since they do not require the extremely bright extrinsic excitation light typically required for fluorescence imaging, especially with 2-photon microscopy. Current BL GECIs perform poorly relative to fluorescent GECIs, producing small changes in bioluminescence intensity due to high baseline signal at resting Ca2+ concentrations and suboptimal Ca2+ affinities. Here, we describe the development of a new bioluminescent GECI, "CaBLAM," which displays much higher contrast (dynamic range) than previously described bioluminescent GECIs and has a Ca2+ affinity suitable for capturing physiological changes in cytosolic Ca2+ concentration. Derived from a new variant of Oplophorus gracilirostris luciferase with superior in vitro properties and a highly favorable scaffold for insertion of sensor domains, CaBLAM allows for single-cell and subcellular resolution imaging of Ca2+ dynamics at high frame rates in cultured neurons and in vivo. CaBLAM marks a significant milestone in the GECI timeline, enabling Ca2+ recordings with high spatial and temporal resolution without perturbing cells with intense excitation light.

Rastermap: a discovery method for neural population recordings

Neurophysiology has long progressed through exploratory experiments and chance discoveries. Anecdotes abound of researchers listening to spikes in real time and noticing patterns of activity related to ongoing stimuli or behaviors. With the advent of large-scale recordings, such close observation of data has become difficult. To find patterns in large-scale neural data, we developed 'Rastermap', a visualization method that displays neurons as a raster plot after sorting them along a one-dimensional axis based on their activity patterns. We benchmarked Rastermap on realistic simulations and then used it to explore recordings of tens of thousands of neurons from mouse cortex during spontaneous, stimulus-evoked and task-evoked epochs. We also applied Rastermap to whole-brain zebrafish recordings; to wide-field imaging data; to electrophysiological recordings in rat hippocampus, monkey frontal cortex and various cortical and subcortical regions in mice; and to artificial neural networks. Finally, we illustrate high-dimensional scenarios where Rastermap and similar algorithms cannot be used effectively.

Population imaging of internal state circuits relevant to psychiatric disease: a review

Internal states involve brain-wide changes that subserve coordinated behavioral and physiological responses for adaptation to changing environments and body states. Investigations of single neurons or small populations have yielded exciting discoveries for the field of neuroscience, but it has been increasingly clear that the encoding of internal states involves the simultaneous representation of multiple different variables in distributed neural ensembles. Thus, an understanding of the representation and regulation of internal states requires capturing large population activity and benefits from approaches that allow for parsing intermingled, genetically defined cell populations. We will explain imaging technologies that permit recording from large populations of single neurons in rodents and the unique capabilities of these technologies in comparison to electrophysiological methods. We will focus on findings for appetitive and aversive states given their high relevance to a wide range of psychiatric disorders and briefly explain how these approaches have been applied to models of psychiatric disease in rodents. We discuss challenges for studying internal states which must be addressed with future studies as well as the therapeutic implications of findings from rodents for improving treatments for psychiatric diseases.

Low-NA two-photon lithography patterning of metal/dielectric tapered optical fibers for depth-selective, volumetric optical neural interfaces

Optical neural implants allow neuroscientists to access deep brain regions, enabling to decipher complex patterns of neural activity. In this field, the use of optical fibers is rapidly increasing, and the ability to generate high-quality metal patterns on their non-planar surface would further extend their application. Here, we propose to use alternating metal shielding and dielectric confinement to engineer the mode-division properties of tapered optical fiber neural implants. This is accomplished through an unconventional application of two-photon lithography (TPL), which employs a low-numerical aperture objective to pattern extensive waveguide sections at both low and high curvature radii. The low-NA TPL is used to polymerize a mask of photoresist, while the rest of the taper undergoes wet metal etching. This implies no direct destructive interaction between the laser beam and the metal to be removed, preserving the optical properties of the dielectric waveguide and of the metal coating. The advantages provided by the presented fabrication method, combined with the intrinsic modal properties of the dielectric waveguide, enable the engineering of the light guiding mechanisms, achieving depth-selective light delivery with a high extinction ratio. The device's light emission and collection properties were investigated in quasi-transparent media and highly scattering brain slices, finding that our proposed method facilitates 360° symmetric light collection around the dielectric-confined section with depth resolution. This opens a perspective for the realization of optical neural implants that can interface the implant axis all-around, with low-NA TPL that can also be applied on other types of non-planar surfaces.

In vivo dual-plane 3-photon microscopy: spanning the depth of the mouse neocortex

Cortical computations arise from patterns of neuronal activity that span across all cortical layers and cell types. Three-photon excitation has extended the depth limit of in vivo imaging within the mouse brain to encompass all cortical layers. However, simultaneous three-photon imaging throughout cortical layers has yet to be demonstrated. Here, we combine non-unity magnification remote focusing with adaptive optics to achieve single-cell resolution imaging from two temporally multiplexed planes separated by up to 600 µm. This approach enables the simultaneous acquisition of neuronal activity from genetically defined cell types in any pair of cortical layers across the mouse neocortical column.

Analysis methods for large-scale neuronal recordings

Simultaneous recordings from hundreds or thousands of neurons are becoming routine because of innovations in instrumentation, molecular tools, and data processing software. Such recordings can be analyzed with data science methods, but it is not immediately clear what methods to use or how to adapt them for neuroscience applications. We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding questions in neuroscience. We review a variety of approaches, ranging from the mathematically simple to the complex, from exploratory to hypothesis-driven, and from recently developed to more established methods. We also illustrate some of the common statistical pitfalls in analyzing large-scale neural data.

Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems

Latent dynamical systems have been widely used to characterize the dynamics of neural population activity in the brain. However, these models typically ignore the fact that the brain contains multiple cell types. This limits their ability to capture the functional roles of distinct cell classes, and to predict the effects of cell-specific perturbations on neural activity or behavior. To overcome these limitations, we introduce the "cell-type dynamical systems" (CTDS) model. This model extends latent linear dynamical systems to contain distinct latent variables for each cell class, with biologically inspired constraints on both dynamics and emissions. To illustrate our approach, we consider neural recordings with distinct excitatory (E) and inhibitory (I) populations. The CTDS model defines separate latents for both cell types, and constrains the dynamics so that E (I) latents have a strictly positive (negative) effects on other latents. We applied CTDS to recordings from rat frontal orienting fields (FOF) and anterior dorsal striatum (ADS) during an auditory decision-making task. The model achieved higher accuracy than a standard linear dynamical system (LDS), and revealed that the animal's choice can be decoded from both E and I latents and thus is not restricted to a single cell-class. We also performed in-silico optogenetic perturbation experiments in the FOF and ADS, and found that CTDS was able to replicate the experimentally observed effects of different perturbations on behavior, whereas a standard LDS model-which does not differentiate between cell types-did not. Crucially, our model allowed us to understand the effects of these perturbations by revealing the dynamics of different cell-specific latents. Finally, CTDS can also be used to identify cell types for neurons whose class labels are unknown in electrophysiological recordings. These results illustrate the power of the CTDS model to provide more accurate and more biologically interpretable descriptions of neural population dynamics and their relationship to behavior.

Brain-wide neural recordings in mice navigating physical spaces enabled by robotic neural recording headstages

Technologies that can record neural activity at cellular resolution at multiple spatial and temporal scales are typically much larger than the animals that are being recorded from and are thus limited to recording from head-fixed subjects. Here we have engineered robotic neural recording devices-'cranial exoskeletons'-that assist mice in maneuvering recording headstages that are orders of magnitude larger and heavier than the mice, while they navigate physical behavioral environments. We discovered optimal controller parameters that enable mice to locomote at physiologically realistic velocities while maintaining natural walking gaits. We show that mice learn to work with the robot to make turns and perform decision-making tasks. Robotic imaging and electrophysiology headstages were used to record recordings of Ca2+ activity of thousands of neurons distributed across the dorsal cortex and spiking activity of hundreds of neurons across multiple brain regions and multiple days, respectively.

Long-term mesoscale imaging of 3D intercellular dynamics across a mammalian organ

A comprehensive understanding of physio-pathological processes necessitates non-invasive intravital three-dimensional (3D) imaging over varying spatial and temporal scales. However, huge data throughput, optical heterogeneity, surface irregularity, and phototoxicity pose great challenges, leading to an inevitable trade-off between volume size, resolution, speed, sample health, and system complexity. Here, we introduce a compact real-time, ultra-large-scale, high-resolution 3D mesoscope (RUSH3D), achieving uniform resolutions of 2.6 × 2.6 × 6 μm3 across a volume of 8,000 × 6,000 × 400 μm3 at 20 Hz with low phototoxicity. Through the integration of multiple computational imaging techniques, RUSH3D facilitates a 13-fold improvement in data throughput and an orders-of-magnitude reduction in system size and cost. With these advantages, we observed premovement neural activity and cross-day visual representational drift across the mouse cortex, the formation and progression of multiple germinal centers in mouse inguinal lymph nodes, and heterogeneous immune responses following traumatic brain injury-all at single-cell resolution, opening up a horizon for intravital mesoscale study of large-scale intercellular interactions at the organ level.

Functional connectomics reveals general wiring rule in mouse visual cortex

Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain implements computation. In the mouse primary visual cortex (V1), excitatory neurons with similar response properties are more likely to be synaptically connected, but previous studies have been limited to within V1, leaving much unknown about broader connectivity rules. In this study, we leverage the millimeter-scale MICrONS dataset to analyze synaptic connectivity and functional properties of individual neurons across cortical layers and areas. Our results reveal that neurons with similar responses are preferentially connected both within and across layers and areas - including feedback connections - suggesting the universality of the 'like-to-like' connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections, beyond what could be explained by the physical proximity of axons and dendrites. We also found a higher-order rule where postsynaptic neuron cohorts downstream of individual presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Notably, recurrent neural networks (RNNs) trained on a simple classification task develop connectivity patterns mirroring both pairwise and higher-order rules, with magnitude similar to those in the MICrONS data. Lesion studies in these RNNs reveal that disrupting 'like-to-like' connections has a significantly greater impact on performance compared to lesions of random connections. These findings suggest that these connectivity principles may play a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.

Dense and Persistent Odor Representations in the Olfactory Bulb of Awake Mice

Recording and analysis of neural activity are often biased toward detecting sparse subsets of highly active neurons, masking important signals carried in low-magnitude and variable responses. To investigate the contribution of seemingly noisy activity to odor encoding, we used mesoscale calcium imaging from mice of both sexes to record odor responses from the dorsal surface of bilateral olfactory bulbs (OBs). The outer layer of the mouse OB is comprised of dendrites organized into discrete "glomeruli," which are defined by odor receptor-specific sensory neuron input. We extracted activity from a large population of glomeruli and used logistic regression to classify odors from individual trials with high accuracy. We then used add-in and dropout analyses to determine subsets of glomeruli necessary and sufficient for odor classification. Classifiers successfully predicted odor identity even after excluding sparse, highly active glomeruli, indicating that odor information is redundantly represented across a large population of glomeruli. Additionally, we found that random forest (RF) feature selection informed by Gini inequality (RF Gini impurity, RFGI) reliably ranked glomeruli by their contribution to overall odor classification. RFGI provided a measure of "feature importance" for each glomerulus that correlated with intuitive features like response magnitude. Finally, in agreement with previous work, we found that odor information persists in glomerular activity after the odor offset. Together, our findings support a model of OB odor coding where sparse activity is sufficient for odor identification, but information is widely, redundantly available across a large population of glomeruli, with each glomerulus representing information about more than one odor.

Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky

Color is an important visual feature that informs behavior, and the retinal basis for color vision has been studied across various vertebrate species. While many studies have investigated how color information is processed in visual brain areas of primate species, we have limited understanding of how it is organized beyond the retina in other species, including most dichromatic mammals. In this study, we systematically characterized how color is represented in the primary visual cortex (V1) of mice. Using large-scale neuronal recordings and a luminance and color noise stimulus, we found that more than a third of neurons in mouse V1 are color-opponent in their receptive field center, while the receptive field surround predominantly captures luminance contrast. Furthermore, we found that color-opponency is especially pronounced in posterior V1 that encodes the sky, matching the statistics of natural scenes experienced by mice. Using unsupervised clustering, we demonstrate that the asymmetry in color representations across cortex can be explained by an uneven distribution of green-On/UV-Off color-opponent response types that are represented in the upper visual field. Finally, a simple model with natural scene-inspired parametric stimuli shows that green-On/UV-Off color-opponent response types may enhance the detection of 'predatory'-like dark UV-objects in noisy daylight scenes. The results from this study highlight the relevance of color processing in the mouse visual system and contribute to our understanding of how color information is organized in the visual hierarchy across species.

SHIELD: Skull-shaped hemispheric implants enabling large-scale electrophysiology datasets in the mouse brain

To understand the neural basis of behavior, it is essential to measure spiking dynamics across many interacting brain regions. Although new technologies, such as Neuropixels probes, facilitate multi-regional recordings, significant surgical and procedural hurdles remain for these experiments to achieve their full potential. Here, we describe skull-shaped hemispheric implants enabling large-scale electrophysiology datasets (SHIELD). These 3D-printed skull-replacement implants feature customizable insertion holes, allowing dozens of cortical and subcortical structures to be recorded in a single mouse using repeated multi-probe insertions over many days. We demonstrate the procedure's high success rate, biocompatibility, lack of adverse effects on behavior, and compatibility with imaging and optogenetics. To showcase SHIELD's scientific utility, we use multi-probe recordings to reveal novel insights into how alpha rhythms organize spiking activity across visual and sensorimotor networks. Overall, this method enables powerful, large-scale electrophysiological experiments for the study of distributed neural computation.

Linking brain activity across scales with simultaneous opto- and electrophysiology

The brain enables adaptive behavior via the dynamic coordination of diverse neuronal signals across spatial and temporal scales: from fast action potential patterns in microcircuits to slower patterns of distributed activity in brain-wide networks. Understanding principles of multiscale dynamics requires simultaneous monitoring of signals in multiple, distributed network nodes. Combining optical and electrical recordings of brain activity is promising for collecting data across multiple scales and can reveal aspects of coordinated dynamics invisible to standard, single-modality approaches. We review recent progress in combining opto- and electrophysiology, focusing on mouse studies that shed new light on the function of single neurons by embedding their activity in the context of brain-wide activity patterns. Optical and electrical readouts can be tailored to desired scales to tackle specific questions. For example, fast dynamics in single cells or local populations recorded with multi-electrode arrays can be related to simultaneously acquired optical signals that report activity in specified subpopulations of neurons, in non-neuronal cells, or in neuromodulatory pathways. Conversely, two-photon imaging can be used to densely monitor activity in local circuits while sampling electrical activity in distant brain areas at the same time. The refinement of combined approaches will continue to reveal previously inaccessible and under-appreciated aspects of coordinated brain activity.

Recent advances in light patterned optogenetic photostimulation in freely moving mice

Optogenetics opened the door to a new era of neuroscience. New optical developments are under way to enable high-resolution neuronal activity imaging and selective photostimulation of neuronal ensembles in freely moving animals. These advancements could allow researchers to interrogate, with cellular precision, functionally relevant neuronal circuits in the framework of naturalistic brain activity. We provide an overview of the current state-of-the-art of imaging and photostimulation in freely moving rodents and present a road map for future optical and engineering developments toward miniaturized microscopes that could reach beyond the currently existing systems.

Advancing the path to in-vivo imaging in freely moving mice via multimode-multicore fiber based holographic endoscopy

Significance

Hair-thin multimode optical fiber-based holographic endoscopes have gained considerable interest in modern neuroscience for their ability to achieve cellular and even subcellular resolution during in-vivo deep brain imaging. However, the application of multimode fibers in freely moving animals presents a persistent challenge as it is difficult to maintain optimal imaging performance while the fiber undergoes deformations.

Aim

We propose a fiber solution for challenging in-vivo applications with the capability of deep brain high spatial resolution imaging and neuronal activity monitoring in anesthetized as well as awake behaving mice.

Approach

We used our previously developed M 3 CF multimode-multicore fiber to record fluorescently labeled neurons in anesthetized mice. Our M 3 CF exhibits a cascaded refractive index structure, enabling two distinct regimes of light transport that imitate either a multimode or a multicore fiber. The M 3 CF has been specifically designed for use in the initial phase of an in-vivo experiment, allowing for the navigation of the endoscope's distal end toward the targeted brain structure. The multicore regime enables the transfer of light to and from each individual neuron within the field of view. For chronic experiments in awake behaving mice, it is crucial to allow for disconnecting the fiber and the animal between experiments. Therefore, we provide here an effective solution and establish a protocol for reconnection of two segments of M 3 CF with hexagonally arranged corelets.

Results

We successfully utilized the M 3 CF to image neurons in anaesthetized transgenic mice expressing enhanced green fluorescent protein. Additionally, we compared imaging results obtained with the M 3 CF with larger numerical aperture (NA) fibers in fixed whole-brain tissue.

Conclusions

This study focuses on addressing challenges and providing insights into the use of multimode-multicore fibers as imaging solutions for in-vivo applications. We suggest that the upcoming version of the M 3 CF increases the overall NA between the two cladding layers to allow for access to high resolution spatial imaging. As the NA increases in the multimode regime, the fiber diameter and ring structure must be reduced to minimize the computational burden and invasiveness.

Potential of plasmonics and nanoscale light-matter interactions for the next generation of optical neural interfaces

Within the realm of optical neural interfaces, the exploration of plasmonic resonances to interact with neural cells has captured increasing attention among the neuroscience community. The interplay of light with conduction electrons in nanometer-sized metallic nanostructures can induce plasmonic resonances, showcasing a versatile capability to both sense and trigger cellular events. We describe the perspective of generating propagating or localized surface plasmon polaritons on the tip of an optical neural implant, widening the possibility for neuroscience labs to explore the potential of plasmonic neural interfaces.

Neurophotonics beyond the surface: unmasking the brain's complexity exploiting optical scattering

The intricate nature of the brain necessitates the application of advanced probing techniques to comprehensively study and understand its working mechanisms. Neurophotonics offers minimally invasive methods to probe the brain using optics at cellular and even molecular levels. However, multiple challenges persist, especially concerning imaging depth, field of view, speed, and biocompatibility. A major hindrance to solving these challenges in optics is the scattering nature of the brain. This perspective highlights the potential of complex media optics, a specialized area of study focused on light propagation in materials with intricate heterogeneous optical properties, in advancing and improving neuronal readouts for structural imaging and optical recordings of neuronal activity. Key strategies include wavefront shaping techniques and computational imaging and sensing techniques that exploit scattering properties for enhanced performance. We discuss the potential merger of the two fields as well as potential challenges and perspectives toward longer term in vivo applications.

Foundation model of neural activity predicts response to new stimulus types and anatomy

The complexity of neural circuits makes it challenging to decipher the brain's algorithms of intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brain's computational objectives and neural coding. However, these models struggle to generalize beyond their training distribution, limiting their utility. The emergence of foundation models, trained on vast datasets, has introduced a new AI paradigm with remarkable generalization capabilities. We collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. It could also be adapted to new tasks beyond neural prediction, accurately predicting anatomical cell types, dendritic features, and neuronal connectivity within the MICrONS functional connectomics dataset. Our work is a crucial step toward building foundation brain models. As neuroscience accumulates larger, multi-modal datasets, foundation models will uncover statistical regularities, enabling rapid adaptation to new tasks and accelerating research.

Heterogeneous orientation tuning in the primary visual cortex of mice diverges from Gabor-like receptive fields in primates

A key feature of neurons in the primary visual cortex (V1) of primates is their orientation selectivity. Recent studies using deep neural network models showed that the most exciting input (MEI) for mouse V1 neurons exhibit complex spatial structures that predict non-uniform orientation selectivity across the receptive field (RF), in contrast to the classical Gabor filter model. Using local patches of drifting gratings, we identified heterogeneous orientation tuning in mouse V1 that varied up to 90° across sub-regions of the RF. This heterogeneity correlated with deviations from optimal Gabor filters and was consistent across cortical layers and recording modalities (calcium vs. spikes). In contrast, model-synthesized MEIs for macaque V1 neurons were predominantly Gabor like, consistent with previous studies. These findings suggest that complex spatial feature selectivity emerges earlier in the visual pathway in mice than in primates. This may provide a faster, though less general, method of extracting task-relevant information.

Advanced rehabilitation in ischaemic stroke research

At present, due to the rapid progress of treatment technology in the acute phase of ischaemic stroke, the mortality of patients has been greatly reduced but the number of disabled survivors is increasing, and most of them are elderly patients. Physicians and rehabilitation therapists pay attention to develop all kinds of therapist techniques including physical therapy techniques, robot-assisted technology and artificial intelligence technology, and study the molecular, cellular or synergistic mechanisms of rehabilitation therapies to promote the effect of rehabilitation therapy. Here, we discussed different animal and in vitro models of ischaemic stroke for rehabilitation studies; the compound concept and technology of neurological rehabilitation; all kinds of biological mechanisms of physical therapy; the significance, assessment and efficacy of neurological rehabilitation; the application of brain-computer interface, rehabilitation robotic and non-invasive brain stimulation technology in stroke rehabilitation.

Amygdala and Cortex Relationships during Learning of a Sensory Discrimination Task

During learning of a sensory discrimination task, the cortical and subcortical regions display complex spatiotemporal dynamics. During learning, both the amygdala and cortex link stimulus information to its appropriate association, for example, a reward. In addition, both structures are also related to nonsensory parameters such as body movements and licking during the reward period. However, the emergence of the cortico-amygdala relationships during learning is largely unknown. To study this, we combined wide-field cortical imaging with fiber photometry to simultaneously record cortico-amygdala population dynamics as male mice learn a whisker-dependent go/no-go task. We were able to simultaneously record neuronal populations from the posterior cortex and either the basolateral amygdala (BLA) or central/medial amygdala (CEM). Prior to learning, the somatosensory and associative cortex responded during sensation, while amygdala areas did not show significant responses. As mice became experts, amygdala responses emerged early during the sensation period, increasing in the CEM, while decreasing in the BLA. Interestingly, amygdala and cortical responses were associated with task-related body movement, displaying significant responses ∼200 ms before movement initiation which led to licking for the reward. A correlation analysis between the cortex and amygdala revealed negative and positive correlation with the BLA and CEM, respectively, only in the expert case. These results imply that learning induces an involvement of the cortex and amygdala which may aid to link sensory stimuli with appropriate associations.

Cortico-basal ganglia plasticity in motor learning

One key function of the brain is to control our body's movements, allowing us to interact with the world around us. Yet, many motor behaviors are not innate but require learning through repeated practice. Among the brain's motor regions, the cortico-basal ganglia circuit is particularly crucial for acquiring and executing motor skills, and neuronal activity in these regions is directly linked to movement parameters. Cell-type-specific adaptations of activity patterns and synaptic connectivity support the learning of new motor skills. Functionally, neuronal activity sequences become structured and associated with learned movements. On the synaptic level, specific connections become potentiated during learning through mechanisms such as long-term synaptic plasticity and dendritic spine dynamics, which are thought to mediate functional circuit plasticity. These synaptic and circuit adaptations within the cortico-basal ganglia circuitry are thus critical for motor skill acquisition, and disruptions in this plasticity can contribute to movement disorders.

Pan-cortical cellular imaging in freely behaving mice using a miniaturized micro-camera array microscope (mini-MCAM)

Understanding how circuits in the brain simultaneously coordinate their activity to mediate complex ethnologically relevant behaviors requires recording neural activities from distributed populations of neurons in freely behaving animals. Current miniaturized imaging microscopes are typically limited to imaging a relatively small field of view, precluding the measurement of neural activities across multiple brain regions. Here we present a miniaturized micro-camera array microscope (mini-MCAM) that consists of four fluorescence imaging micro-cameras, each capable of capturing neural activity across a 4.5 mm x 2.55 mm field of view (FOV). Cumulatively, the mini-MCAM images over 30 mm 2 area of sparsely expressed GCaMP6s neurons distributed throughout the dorsal cortex, in regions including the primary and secondary motor, somatosensory, visual, retrosplenial, and association cortices across both hemispheres. We demonstrate cortex-wide cellular resolution in vivo Calcium (Ca 2+ ) imaging using the mini-MCAM in both head-fixed and freely behaving mice.

Expansion-assisted selective plane illumination microscopy for nanoscale imaging of centimeter-scale tissues

Recent advances in tissue processing, labeling, and fluorescence microscopy are providing unprecedented views of the structure of cells and tissues at sub-diffraction resolutions and near single molecule sensitivity, driving discoveries in diverse fields of biology, including neuroscience. Biological tissue is organized over scales of nanometers to centimeters. Harnessing molecular imaging across intact, three-dimensional samples on this scale requires new types of microscopes with larger fields of view and working distance, as well as higher throughput. We present a new expansion-assisted selective plane illumination microscope (ExA-SPIM) with aberration-free 1×1×3 μm optical resolution over a large field of view (10.6×8.0 mm 2 ) and working distance (35 mm) at speeds up to 946 megavoxels/sec. Combined with new tissue clearing and expansion methods, the microscope allows imaging centimeter-scale samples with 250×250×750 nm optical resolution (4× expansion), including entire mouse brains, with high contrast and without sectioning. We illustrate ExA-SPIM by reconstructing individual neurons across the mouse brain, imaging cortico-spinal neurons in the macaque motor cortex, and visualizing axons in human white matter.