Comprehensive imaging of cortical networks

Aberrant cortical activity, functional connectivity, and neural assembly architecture after photothrombotic stroke in mice

Despite substantial progress in mapping the trajectory of network plasticity resulting from focal ischemic stroke, the extent and nature of changes in neuronal excitability and activity within the peri-infarct cortex of mice remains poorly defined. Most of the available data have been acquired from anesthetized animals, acute tissue slices, or infer changes in excitability from immunoassays on extracted tissue, and thus may not reflect cortical activity dynamics in the intact cortex of an awake animal. Here, in vivo two-photon calcium imaging in awake, behaving mice was used to longitudinally track cortical activity, network functional connectivity, and neural assembly architecture for 2 months following photothrombotic stroke targeting the forelimb somatosensory cortex. Sensorimotor recovery was tracked over the weeks following stroke, allowing us to relate network changes to behavior. Our data revealed spatially restricted but long-lasting alterations in somatosensory neural network function and connectivity. Specifically, we demonstrate significant and long-lasting disruptions in neural assembly architecture concurrent with a deficit in functional connectivity between individual neurons. Reductions in neuronal spiking in peri-infarct cortex were transient but predictive of impairment in skilled locomotion measured in the tapered beam task. Notably, altered neural networks were highly localized, with assembly architecture and neural connectivity relatively unaltered a short distance from the peri-infarct cortex, even in regions within 'remapped' forelimb functional representations identified using mesoscale imaging with anaesthetized preparations 8 weeks after stroke. Thus, using longitudinal two-photon microscopy in awake animals, these data show a complex spatiotemporal relationship between peri-infarct neuronal network function and behavioral recovery. Moreover, the data highlight an apparent disconnect between dramatic functional remapping identified using strong sensory stimulation in anaesthetized mice compared to more subtle and spatially restricted changes in individual neuron and local network function in awake mice during stroke recovery.

A machine-learning tool to identify bistable states from calcium imaging data

Mapping neuronal activation using calcium imaging in vivo during behavioural tasks has advanced our understanding of nervous system function. In almost all of these studies, calcium imaging is used to infer spike probabilities because action potentials activate voltage-gated calcium channels and increase intracellular calcium levels. However, neurons not only fire action potentials, but also convey information via intrinsic dynamics such as by generating bistable membrane potential states. Although a number of tools for spike inference have been developed and are currently being used, no tool exists for converting calcium imaging signals to maps of cellular state in bistable neurons. Purkinje neurons in the larval zebrafish cerebellum exhibit membrane potential bistability, firing either tonically or in bursts. Several studies have implicated the role of a population code in cerebellar function, with bistability adding an extra layer of complexity to this code. In the present study, we develop a tool, CaMLSort, which uses convolutional recurrent neural networks to classify calcium imaging traces as arising from either tonic or bursting cells. We validate this classifier using a number of different methods and find that it performs well on simulated event rasters as well as real biological data that it had not previously seen. Moreover, we find that CaMLsort generalizes to other bistable neurons, such as dopaminergic neurons in the ventral tegmental area of mice. Thus, this tool offers a new way of analysing calcium imaging data from bistable neurons to understand how they participate in network computation and natural behaviours. KEY POINTS: Calcium imaging, compriising the gold standard of inferring neuronal activity, does not report cellular state in neurons that are bistable, such as Purkinje neurons in the cerebellum of larval zebrafish. We model the relationship between Purkinje neuron electrical activity and its corresponding calcium signal to compile a dataset of state-labelled simulated calcium signals. We apply machine-learning methods to this dataset to develop a tool that can classify the state of a Purkinje neuron using only its calcium signal, which works well on real data even though it was trained only on simulated data. CaMLsort (Calcium imaging and Machine Learning based tool to sort intracellular state) also generalizes well to bistable neurons in a different brain region (ventral tegmental area) in a different model organism (mouse). This tool can facilitate our understanding of how these neurons carry out their functions in a circuit.

Fractured columnar small-world functional network organization in volumes of L2/3 of mouse auditory cortex

The sensory cortices of the brain exhibit large-scale functional topographic organization, such as the tonotopic organization of the primary auditory cortex (A1) according to sound frequency. However, at the level of individual neurons, layer 2/3 (L2/3) A1 appears functionally heterogeneous. To identify if there exists a higher-order functional organization of meso-scale neuronal networks within L2/3 that bridges order and disorder, we used in vivo two-photon calcium imaging of pyramidal neurons to identify networks in three-dimensional volumes of L2/3 A1 in awake mice. Using tonal stimuli, we found diverse receptive fields with measurable colocalization of similarly tuned neurons across depth but less so across L2/3 sublayers. These results indicate a fractured microcolumnar organization with a column radius of ∼50 µm, with a more random organization of the receptive field over larger radii. We further characterized the functional networks formed within L2/3 by analyzing the spatial distribution of signal correlations (SCs). Networks show evidence of Rentian scaling in physical space, suggesting effective spatial embedding of subnetworks. Indeed, functional networks have characteristics of small-world topology, implying that there are clusters of functionally similar neurons with sparse connections between differently tuned neurons. These results indicate that underlying the regularity of the tonotopic map on large scales in L2/3 is significant tuning diversity arranged in a hybrid organization with microcolumnar structures and efficient network topologies.

Representational drift in barrel cortex is receptive field dependent

Cortical populations often exhibit changes in activity even when behavior is stable. How behavioral stability is maintained in the face of such 'representational drift' remains unclear. One possibility is that some neurons are stable despite broader instability. We examine whisker touch responses in superficial layers of primary vibrissal somatosensory cortex (vS1) over several weeks in mice stably performing an object detection task with two whiskers. While the number of touch neurons remained constant, individual neurons changed with time. Touch-responsive neurons with broad receptive fields were more stable than narrowly tuned neurons. Transitions between functional types were non-random: before becoming broadly tuned neurons, unresponsive neurons first pass through a period of narrower tuning. Broadly tuned neurons with higher pairwise correlations to other touch neurons were more stable than neurons with lower correlations. Thus, a small population of broadly tuned and synchronously active touch neurons exhibit elevated stability and may be particularly important for downstream readout.

Sparse and distributed cortical populations mediate sensorimotor integration

Touch information is central to sensorimotor integration, yet little is known about how cortical touch and movement representations interact. Touch- and movement-related activity is present in both somatosensory and motor cortices, making both candidate sites for touch-motor interactions. We studied touch-motor interactions in layer 2/3 of the primary vibrissal somatosensory and motor cortices of behaving mice. Volumetric two-photon calcium imaging revealed robust responses to whisker touch, whisking, and licking in both areas. Touch activity was dominated by a sparse population of broadly tuned neurons responsive to multiple whiskers that exhibited longitudinal stability and disproportionately influenced interareal communication. Movement representations were similarly dominated by sparse, stable, reciprocally projecting populations. In both areas, many broadly tuned touch cells also produced robust licking or whisking responses. These touch-licking and touch-whisking neurons showed distinct dynamics suggestive of specific roles in shaping movement. Cortical touch-motor interactions are thus mediated by specialized populations of highly responsive, broadly tuned neurons.

Learning in a sensory cortical microstimulation task is associated with elevated representational stability

Sensory cortical representations can be highly dynamic, raising the question of how representational stability impacts learning. We train mice to discriminate the number of photostimulation pulses delivered to opsin-expressing pyramidal neurons in layer 2/3 of primary vibrissal somatosensory cortex. We simultaneously track evoked neural activity across learning using volumetric two-photon calcium imaging. In well-trained animals, trial-to-trial fluctuations in the amount of photostimulus-evoked activity predicted animal choice. Population activity levels declined rapidly across training, with the most active neurons showing the largest declines in responsiveness. Mice learned at varied rates, with some failing to learn the task in the time provided. The photoresponsive population showed greater instability both within and across behavioral sessions among animals that failed to learn. Animals that failed to learn also exhibited a faster deterioration in stimulus decoding. Thus, greater stability in the stimulus response is associated with learning in a sensory cortical microstimulation task.

Cortical circuitry mediating inter-areal touch signal amplification

Sensory cortical areas are often organized into topographic maps which represent the sensory epithelium1,2. Individual areas are richly interconnected3, in many cases via reciprocal projections that respect the topography of the underlying map4,5. Because topographically matched cortical patches process the same stimulus, their interaction is likely central to many neural computations6-10. Here, we ask how topographically matched subregions of primary and secondary vibrissal somatosensory cortices (vS1 and vS2) interact during whisker touch. In the mouse, whisker touch-responsive neurons are topographically organized in both vS1 and vS2. Both areas receive thalamic touch input and are topographically interconnected4. Volumetric calcium imaging in mice actively palpating an object with two whiskers revealed a sparse population of highly active, broadly tuned touch neurons responsive to both whiskers. These neurons were especially pronounced in superficial layer 2 in both areas. Despite their rarity, these neurons served as the main conduits of touch-evoked activity between vS1 and vS2 and exhibited elevated synchrony. Focal lesions of the whisker touch-responsive region in vS1 or vS2 degraded touch responses in the unlesioned area, with whisker-specific vS1 lesions degrading whisker-specific vS2 touch responses. Thus, a sparse and superficial population of broadly tuned touch neurons recurrently amplifies touch responses across vS1 and vS2.

Microstimulation of sensory cortex engages natural sensory representations

Cortical activity patterns occupy a small subset of possible network states. If this is due to intrinsic network properties, microstimulation of sensory cortex should evoke activity patterns resembling those observed during natural sensory input. Here, we use optical microstimulation of virally transfected layer 2/3 pyramidal neurons in the mouse primary vibrissal somatosensory cortex to compare artificially evoked activity with natural activity evoked by whisker touch and movement ("whisking"). We find that photostimulation engages touch- but not whisking-responsive neurons more than expected by chance. Neurons that respond to photostimulation and touch or to touch alone exhibit higher spontaneous pairwise correlations than purely photoresponsive neurons. Exposure to several days of simultaneous touch and optogenetic stimulation raises both overlap and spontaneous activity correlations among touch and photoresponsive neurons. We thus find that cortical microstimulation engages existing cortical representations and that repeated co-presentation of natural and artificial stimulation enhances this effect.

Fast and sensitive GCaMP calcium indicators for imaging neural populations

Calcium imaging with protein-based indicators1,2 is widely used to follow neural activity in intact nervous systems, but current protein sensors report neural activity at timescales much slower than electrical signalling and are limited by trade-offs between sensitivity and kinetics. Here we used large-scale screening and structure-guided mutagenesis to develop and optimize several fast and sensitive GCaMP-type indicators3-8. The resulting 'jGCaMP8' sensors, based on the calcium-binding protein calmodulin and a fragment of endothelial nitric oxide synthase, have ultra-fast kinetics (half-rise times of 2 ms) and the highest sensitivity for neural activity reported for a protein-based calcium sensor. jGCaMP8 sensors will allow tracking of large populations of neurons on timescales relevant to neural computation.

Bringing to light the physiological and pathological firing patterns of human induced pluripotent stem cell-derived neurons using optical recordings

Human induced pluripotent stem cells (hiPSCs) are a promising approach to study neurological and neuropsychiatric diseases. Most methods to record the activity of these cells have major drawbacks as they are invasive or they do not allow single cell resolution. Genetically encoded voltage indicators (GEVIs) open the path to high throughput visualization of undisturbed neuronal activity. However, conventional GEVIs perturb membrane integrity through inserting multiple copies of transmembrane domains into the plasma membrane. To circumvent large add-ons to the plasma membrane, we used a minimally invasive novel hybrid dark quencher GEVI to record the physiological and pathological firing patterns of hiPSCs-derived sensory neurons from patients with inherited erythromelalgia, a chronic pain condition associated with recurrent attacks of redness and swelling in the distal extremities. We observed considerable differences in action potential firing patterns between patient and control neurons that were previously overlooked with other recording methods. Our system also performed well in hiPSC-derived forebrain neurons where it detected spontaneous synchronous bursting behavior, thus opening the path to future applications in other cell types and disease models including Parkinson's disease, Alzheimer's disease, epilepsy, and schizophrenia, conditions associated with disturbances of neuronal activity and synchrony.

Dimensionality reduction of calcium-imaged neuronal population activity

Calcium imaging has been widely adopted for its ability to record from large neuronal populations. To summarize the time course of neural activity, dimensionality reduction methods, which have been applied extensively to population spiking activity, may be particularly useful. However, it is unclear if the dimensionality reduction methods applied to spiking activity are appropriate for calcium imaging. We thus carried out a systematic study of design choices based on standard dimensionality reduction methods. We also developed a method to perform deconvolution and dimensionality reduction simultaneously (Calcium Imaging Linear Dynamical System, CILDS). CILDS most accurately recovered the single-trial, low-dimensional time courses from simulated calcium imaging data. CILDS also outperformed the other methods on calcium imaging recordings from larval zebrafish and mice. More broadly, this study represents a foundation for summarizing calcium imaging recordings of large neuronal populations using dimensionality reduction in diverse experimental settings.

Refinements to rodent head fixation and fluid/food control for neuroscience

The use of head fixation in mice is increasingly common in research, its use having initially been restricted to the field of sensory neuroscience. Head restraint has often been combined with fluid control, rather than food restriction, to motivate behaviour, but this too is now in use for both restrained and non-restrained animals. Despite this, there is little guidance on how best to employ these techniques to optimise both scientific outcomes and animal welfare. This article summarises current practices and provides recommendations to improve animal wellbeing and data quality, based on a survey of the community, literature reviews, and the expert opinion and practical experience of an international working group convened by the UK's National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs). Topics covered include head fixation surgery and post-operative care, habituation to restraint, and the use of fluid/food control to motivate performance. We also discuss some recent developments that may offer alternative ways to collect data from large numbers of behavioural trials without the need for restraint. The aim is to provide support for researchers at all levels, animal care staff, and ethics committees to refine procedures and practices in line with the refinement principle of the 3Rs.

Deep-learning-augmented computational miniature mesoscope

Fluorescence microscopy is essential to study biological structures and dynamics. However, existing systems suffer from a trade-off between field of view (FOV), resolution, and system complexity, and thus cannot fulfill the emerging need for miniaturized platforms providing micron-scale resolution across centimeter-scale FOVs. To overcome this challenge, we developed a computational miniature mesoscope (CM²) that exploits a computational imaging strategy to enable single-shot, 3D high-resolution imaging across a wide FOV in a miniaturized platform. Here, we present CM² V2, which significantly advances both the hardware and computation. We complement the 3 × 3 microlens array with a hybrid emission filter that improves the imaging contrast by 5×, and design a 3D-printed free-form collimator for the LED illuminator that improves the excitation efficiency by 3×. To enable high-resolution reconstruction across a large volume, we develop an accurate and efficient 3D linear shift-variant (LSV) model to characterize spatially varying aberrations. We then train a multimodule deep learning model called CM²Net, using only the 3D-LSV simulator. We quantify the detection performance and localization accuracy of CM²Net to reconstruct fluorescent emitters under different conditions in simulation. We then show that CM²Net generalizes well to experiments and achieves accurate 3D reconstruction across a ~7-mm FOV and 800-μm depth, and provides ~6-μm lateral and ~25-μm axial resolution. This provides an ~8× better axial resolution and ~1400× faster speed compared to the previous model-based algorithm. We anticipate this simple, low-cost computational miniature imaging system will be useful for many large-scale 3D fluorescence imaging applications.

Transformation of primary sensory cortical representations from layer 4 to layer 2

Sensory input arrives from thalamus in cortical layer (L) 4, which outputs predominantly to superficial layers. L4 to L2 thus constitutes one of the earliest cortical feedforward networks. Despite extensive study, the transformation performed by this network remains poorly understood. We use two-photon calcium imaging to record neural activity in L2-4 of primary vibrissal somatosensory cortex (vS1) as mice perform an object localization task with two whiskers. Touch responses sparsen and become more reliable from L4 to L2, with nearly half of the superficial touch response confined to ~1 % of excitatory neurons. These highly responsive neurons have broad receptive fields and can more accurately decode stimulus features. They participate disproportionately in ensembles, small subnetworks with elevated pairwise correlations. Thus, from L4 to L2, cortex transitions from distributed probabilistic coding to sparse and robust ensemble-based coding, resulting in more efficient and accurate representations.

Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface

Calcium imaging is a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of fundamental principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon imaging of neuronal calcium signals from macaques engaged in a motor task. By imaging apical dendrites, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signalswhich successfully decoded movement direction online. By fusing two-photon functional imaging with CLARITY volumetric imaging, we verified that many imaged dendrites which contributed to oBCI decoding originated from layer 5 output neurons, including a putative Betz cell. This approach establishes new opportunities for studying motor control and designing BCIs via two photon imaging.

Differential modulation of excitatory and inhibitory populations of superficial dorsal horn neurons in lumbar spinal cord by Aβ-fiber electrical stimulation

Activation of Aβ-fibers is fundamental to numerous analgesic therapies, yet its effects on dorsal horn neuronal activity remain unclear. We used multiphoton microscopy of the genetically encoded calcium indicator GCaMP6s to characterize the effects of Aβ-fiber electrical stimulation (Aβ-ES) on neural activity. Specifically, we quantified somatic responses evoked by C-fiber intensity stimulation before and after a 10-minute train of dorsal root Aβ-ES in superficial dorsal horn (SDH) neurons, in mouse lumbar spinal cord. Aβ-ES did not alter C-fiber-evoked activity when GCaMP6s was virally expressed in all neurons, in an intact lumbar spinal cord preparation. However, when we restricted the expression of GCaMP6s to excitatory or inhibitory populations, we observed that Aβ-ES modestly potentiated evoked activity of excitatory neurons and depressed that of inhibitory neurons. Aβ-ES had no significant effects in a slice preparation in either SDH population. A larger proportion of SDH neurons was activated by Aβ-ES when delivered at a root rostral or caudal to the segment where the imaging and C-fiber intensity stimulation occurred. Aβ-ES effects on excitatory and inhibitory populations depended on the root used. Our findings suggest that Aβ-ES differentially modulates lumbar spinal cord SDH populations in a cell type- and input-specific manner. Furthermore, they underscore the importance of the Aβ-ES delivery site, suggesting that Aβ stimulation at a segment adjacent to where the pain is may improve analgesic efficacy.

Limiting RyR2 Open Time Prevents Alzheimer's Disease-Related Neuronal Hyperactivity and Memory Loss but Not β-Amyloid Accumulation

Neuronal hyperactivity is an early primary dysfunction in Alzheimer’s disease (AD) in humans and animal models, but effective neuronal hyperactivity-directed anti-AD therapeutic agents are lacking. Here we define a previously unknown mode of ryanodine receptor 2 (RyR2) control of neuronal hyperactivity and AD progression. We show that a single RyR2 point mutation, E4872Q, which reduces RyR2 open time, prevents hyperexcitability, hyperactivity, memory impairment, neuronal cell death, and dendritic spine loss in a severe early-onset AD mouse model (5xFAD). The RyR2-E4872Q mutation upregulates hippocampal CA1-pyramidal cell A-type K⁺ current, a well-known neuronal excitability control that is downregulated in AD. Pharmacologically limiting RyR2 open time with the R-carvedilol enantiomer (but not racemic carvedilol) prevents and rescues neuronal hyperactivity, memory impairment, and neuron loss even in late stages of AD. These AD-related deficits are prevented even with continued β-amyloid accumulation. Thus, limiting RyR2 open time may be a hyperactivity-directed, non-β-amyloid-targeted anti-AD strategy.

Yao et al. show that genetically or pharmacologically limiting the open duration of ryanodine receptor 2 upregulates the A-type potassium current and prevents neuronal hyperexcitability and hyperactivity, memory impairment, neuronal cell death, and dendritic spine loss in a severe early-onset Alzheimer’s disease mouse model, even with continued accumulation of β-amyloid.

Photoactivated voltage imaging in tissue with an archaerhodopsin-derived reporter

Photoactivated genetically encoded voltage indicators (GEVIs) have the potential to enable optically sectioned voltage imaging at the intersection of a photoactivation beam and an imaging beam. We developed a pooled high-throughput screen to identify archaerhodopsin mutants with enhanced photoactivation. After screening ~10⁵ cells, we identified a novel GEVI, NovArch, whose one-photon near-infrared fluorescence is reversibly enhanced by weak one-photon blue or two-photon near-infrared excitation. Because the photoactivation leads to fluorescent signals catalytically rather than stoichiometrically, high fluorescence signals, optical sectioning, and high time resolution are achieved simultaneously at modest blue or two-photon laser power. We demonstrate applications of the combined molecular and optical tools to optical mapping of membrane voltage in distal dendrites in acute mouse brain slices and in spontaneously active neurons in vivo.

Place Cells in Head-Fixed Mice Navigating a Floating Real-World Environment

The hippocampal place cell system in rodents has provided a major paradigm for the scientific investigation of memory function and dysfunction. Place cells have been observed in area CA1 of the hippocampus of both freely moving animals, and of head-fixed animals navigating in virtual reality environments. However, spatial coding in virtual reality preparations has been observed to be impaired. Here we show that the use of a real-world environment system for head-fixed mice, consisting of an air-floating track with proximal cues, provides some advantages over virtual reality systems for the study of spatial memory. We imaged the hippocampus of head-fixed mice injected with the genetically encoded calcium indicator GCaMP6s while they navigated circularly constrained or open environments on the floating platform. We observed consistent place tuning in a substantial fraction of cells despite the absence of distal visual cues. Place fields remapped when animals entered a different environment. When animals re-entered the same environment, place fields typically remapped over a time period of multiple days, faster than in freely moving preparations, but comparable with virtual reality. Spatial information rates were within the range observed in freely moving mice. Manifold analysis indicated that spatial information could be extracted from a low-dimensional subspace of the neural population dynamics. This is the first demonstration of place cells in head-fixed mice navigating on an air-lifted real-world platform, validating its use for the study of brain circuits involved in memory and affected by neurodegenerative disorders.

Developmental divergence of sensory stimulus representation in cortical interneurons

Vasocative-intestinal-peptide (VIP⁺) and somatostatin (SST⁺) interneurons are involved in modulating barrel cortex activity and perception during active whisking. Here we identify a developmental transition point of structural and functional rearrangements onto these interneurons around the start of active sensation at P14. Using in vivo two-photon Ca²⁺ imaging, we find that before P14, both interneuron types respond stronger to a multi-whisker stimulus, whereas after P14 their responses diverge, with VIP⁺ cells losing their multi-whisker preference and SST⁺ neurons enhancing theirs. Additionally, we find that Ca²⁺ signaling dynamics increase in precision as the cells and network mature. Rabies virus tracings followed by tissue clearing, as well as photostimulation-coupled electrophysiology reveal that SST⁺ cells receive higher cross-barrel inputs compared to VIP⁺ neurons at both time points. In addition, whereas prior to P14 both cell types receive direct input from the sensory thalamus, after P14 VIP⁺ cells show reduced inputs and SST⁺ cells largely shift to motor-related thalamic nuclei.

Sensory neuronal circuits adapt during maturation when animals start to actively interact with the external world. The authors reveal structural and functional rearrangements of the input cortical interneurons receive around the time the animals start active sensation.

Improved hyperacuity estimation of spike timing from calcium imaging

Two-photon imaging is a major recording technique used in neuroscience. However, it suffers from several limitations, including a low sampling rate, the nonlinearity of calcium responses, the slow dynamics of calcium dyes and a low SNR, all of which severely limit the potential of two-photon imaging to elucidate neuronal dynamics with high temporal resolution. We developed a hyperacuity algorithm (HA_time) based on an approach that combines a generative model and machine learning to improve spike detection and the precision of spike time inference. Bayesian inference was performed to estimate the calcium spike model, assuming constant spike shape and size. A support vector machine using this information and a jittering method maximizing the likelihood of estimated spike times enhanced spike time estimation precision approximately fourfold (range, 2–7; mean, 3.5–4.0; 2SEM, 0.1–0.25) compared to the sampling interval. Benchmark scores of HA_time for biological data from three different brain regions were among the best of the benchmark algorithms. Simulation of broader data conditions indicated that our algorithm performed better than others with high firing rate conditions. Furthermore, HA_time exhibited comparable performance for conditions with and without ground truths. Thus HA_time is a useful tool for spike reconstruction from two-photon imaging.

Mesoscopic Imaging: Shining a Wide Light on Large-Scale Neural Dynamics

Optical imaging has revolutionized our ability to monitor brain activity, spanning spatial scales from synapses to cells to circuits. Here, we summarize the rapid development and application of mesoscopic imaging, a widefield fluorescence-based approach that balances high spatiotemporal resolution with extraordinarily large fields of view. By leveraging the continued expansion of fluorescent reporters for neuronal activity and novel strategies for indicator expression, mesoscopic analysis enables measurement and correlation of network dynamics with behavioral state and task performance. Moreover, the combination of widefield imaging with cellular resolution methods such as two-photon microscopy and electrophysiology is bridging boundaries between cellular and network analyses. Overall, mesoscopic imaging provides a powerful option in the optical toolbox for investigation of brain function.

A comparison of neuronal population dynamics measured with calcium imaging and electrophysiology

Calcium imaging with fluorescent protein sensors is widely used to record activity in neuronal populations. The transform between neural activity and calcium-related fluorescence involves nonlinearities and low-pass filtering, but the effects of the transformation on analyses of neural populations are not well understood. We compared neuronal spikes and fluorescence in matched neural populations in behaving mice. We report multiple discrepancies between analyses performed on the two types of data, including changes in single-neuron selectivity and population decoding. These were only partially resolved by spike inference algorithms applied to fluorescence. To model the relation between spiking and fluorescence we simultaneously recorded spikes and fluorescence from individual neurons. Using these recordings we developed a model transforming spike trains to synthetic-imaging data. The model recapitulated the differences in analyses. Our analysis highlights challenges in relating electrophysiology and imaging data, and suggests forward modeling as an effective way to understand differences between these data.

Functional organization of mouse primary auditory cortex in adult C57BL/6 and F1 (CBAxC57) mice

The primary auditory cortex (A1) plays a key role for sound perception since it represents one of the first cortical processing stations for sounds. Recent studies have shown that on the cellular level the frequency organization of A1 is more heterogeneous than previously appreciated. However, many of these studies were performed in mice on the C57BL/6 background which develop high frequency hearing loss with age making them a less optimal choice for auditory research. In contrast, mice on the CBA background retain better hearing sensitivity in old age. Since potential strain differences could exist in A1 organization between strains, we performed comparative analysis of neuronal populations in A1 of adult (~ 10 weeks) C57BL/6 mice and F1 (CBAxC57) mice. We used in vivo 2-photon imaging of pyramidal neurons in cortical layers L4 and L2/3 of awake mouse primary auditory cortex (A1) to characterize the populations of neurons that were active to tonal stimuli. Pure tones recruited neurons of widely ranging frequency preference in both layers and strains with neurons in F1 (CBAxC57) mice exhibiting a wider range of frequency preference particularly to higher frequencies. Frequency selectivity was slightly higher in C57BL/6 mice while neurons in F1 (CBAxC57) mice showed a greater sound-level sensitivity. The spatial heterogeneity of frequency preference was present in both strains with F1 (CBAxC57) mice exhibiting higher tuning diversity across all measured length scales. Our results demonstrate that the tone evoked responses and frequency representation in A1 of adult C57BL/6 and F1 (CBAxC57) mice are largely similar.

The gut-brain axis mediates sugar preference

The taste of sugar is one of the most basic sensory percepts for humans and other animals. Animals can develop a strong preference for sugar even if they lack sweet taste receptors, indicating a mechanism independent of taste^1-3. Here we examined the neural basis for sugar preference and demonstrate that a population of neurons in the vagal ganglia and brainstem are activated via the gut-brain axis to create preference for sugar. These neurons are stimulated in response to sugar but not artificial sweeteners, and are activated by direct delivery of sugar to the gut. Using functional imaging we monitored activity of the gut-brain axis, and identified the vagal neurons activated by intestinal delivery of glucose. Next, we engineered mice in which synaptic activity in this gut-to-brain circuit was genetically silenced, and prevented the development of behavioural preference for sugar. Moreover, we show that co-opting this circuit by chemogenetic activation can create preferences to otherwise less-preferred stimuli. Together, these findings reveal a gut-to-brain post-ingestive sugar-sensing pathway critical for the development of sugar preference. In addition, they explain the neural basis for differences in the behavioural effects of sweeteners versus sugar, and uncover an essential circuit underlying the highly appetitive effects of sugar.

Cortical Network Dynamics Is Altered in Mouse Models of Huntington's Disease

Huntington's disease (HD) is a neurodegenerative disorder characterized by involuntary movements, cognitive deficits, and psychiatric disturbances. Although evidence indicates that projections from motor cortical areas play a key role in the development of dysfunctional striatal activity and motor phenotype, little is known about the changes in cortical microcircuits and their role in the development of the HD phenotype. Here we used two-photon laser-scanning microscopy to evaluate network dynamics of motor cortical neurons in layers II/III in behaving transgenic R6/2 and knock-in Q175+/- mice. Symptomatic R6/2 mice displayed increased motion manifested by a significantly greater number of motion epochs, whereas symptomatic Q175 mice displayed decreased motion. In both models, calcium transients in symptomatic mice displayed reduced amplitude, suggesting decreased bursting activity. Changes in frequency were genotype- and time-dependent; for R6/2 mice, the frequency was reduced during both motion and nonmotion, whereas in symptomatic Q175 mice, the reduction only occurred during nonmotion. In presymptomatic Q175 mice, frequency was increased during both behavioral states. Interneuronal correlation coefficients were generally decreased in both models, suggesting disrupted interneuronal communication in HD cerebral cortex. These results indicate similar and contrasting effects of the HD mutation on cortical ensemble activity depending on mouse model and disease stage.

Experimental pathology by intravital microscopy and genetically encoded fluorescent biosensors

The invention of two‐photon excitation microscopes widens the potential application of intravital microscopy (IVM) to the broad field of experimental pathology. Moreover, the recent development of fluorescent protein‐based, genetically encoded biosensors provides an ideal tool to visualize the cell function in live animals. We start from a brief review of IVM with two‐photon excitation microscopes and genetically encoded biosensors based on the principle of Förster resonance energy transfer (FRET). Then, we describe how IVM using biosensors has revealed the pathogenesis of several disease models.

The gut-brain axis mediates sugar preference

The taste of sugar is one of the most basic sensory percepts for humans and other animals. Animals can develop a strong preference for sugar even if they lack sweet taste receptors, indicating a mechanism independent of taste1-3. Here we examined the neural basis for sugar preference and demonstrate that a population of neurons in the vagal ganglia and brainstem are activated via the gut-brain axis to create preference for sugar. These neurons are stimulated in response to sugar but not artificial sweeteners, and are activated by direct delivery of sugar to the gut. Using functional imaging we monitored activity of the gut-brain axis, and identified the vagal neurons activated by intestinal delivery of glucose. Next, we engineered mice in which synaptic activity in this gut-to-brain circuit was genetically silenced, and prevented the development of behavioural preference for sugar. Moreover, we show that co-opting this circuit by chemogenetic activation can create preferences to otherwise less-preferred stimuli. Together, these findings reveal a gut-to-brain post-ingestive sugar-sensing pathway critical for the development of sugar preference. In addition, they explain the neural basis for differences in the behavioural effects of sweeteners versus sugar, and uncover an essential circuit underlying the highly appetitive effects of sugar.

Interpreting in vivo calcium signals from neuronal cell bodies, axons, and dendrites: a review

Calcium imaging is emerging as a popular technique in neuroscience. A major reason is that intracellular calcium transients are reflections of electrical events in neurons. For example, calcium influx in the soma and axonal boutons accompanies spiking activity, whereas elevations in dendrites and dendritic spines are associated with synaptic inputs and local regenerative events. However, calcium transients have complex spatiotemporal dynamics, and since most optical methods visualize only one of the somatic, axonal, and dendritic compartments, a straightforward inference of the underlying electrical event is typically challenging. We highlight experiments that have directly calibrated in vivo calcium signals recorded using fluorescent indicators against electrophysiological events. We address commonly asked questions such as: Can calcium imaging be used to characterize neurons with high firing rates? Can the fluorescent signal report a decrease in spiking activity? What is the evidence that calcium transients in subcellular compartments correspond to distinct presynaptic axonal and postsynaptic dendritic events? By reviewing the empirical evidence and limitations, we suggest that, despite some caveats, calcium imaging is a versatile method to characterize a variety of neuronal events in vivo.

Neuronal Avalanches in Input and Associative Layers of Auditory Cortex

The primary auditory cortex processes acoustic sequences for the perception of behaviorally meaningful sounds such as speech. Sound information arrives at its input layer four from where activity propagates to associative layer 2/3. It is currently not known whether there is a characteristic organization of neuronal population activity across layers and sound levels during sound processing. Here, we identify neuronal avalanches, which in theory and experiments have been shown to maximize dynamic range and optimize information transfer within and across networks, in primary auditory cortex. We used in vivo 2-photon imaging of pyramidal neurons in cortical layers L4 and L2/3 of mouse A1 to characterize the populations of neurons that were active spontaneously, i.e., in the absence of a sound stimulus, and those recruited by single-frequency tonal stimuli at different sound levels. Single-frequency sounds recruited neurons of widely ranging frequency selectivity in both layers. We defined neuronal ensembles as neurons being active within or during successive temporal windows at the temporal resolution of our imaging. For both layers, neuronal ensembles were highly variable in size during spontaneous activity as well as during sound presentation. Ensemble sizes distributed according to power laws, the hallmark of neuronal avalanches, and were similar across sound levels. Avalanches activated by sound were composed of neurons with diverse tuning preference, yet with selectivity independent of avalanche size. Our results suggest that optimization principles identified for avalanches guide population activity in L4 and L2/3 of auditory cortex during and in-between stimulus processing.

Genetic Dissection of Neural Circuits: A Decade of Progress

Tremendous progress has been made since Neuron published our Primer on genetic dissection of neural circuits 10 years ago. Since then, cell-type-specific anatomical, neurophysiological, and perturbation studies have been carried out in a multitude of invertebrate and vertebrate organisms, linking neurons and circuits to behavioral functions. New methods allow systematic classification of cell types and provide genetic access to diverse neuronal types for studies of connectivity and neural coding during behavior. Here we evaluate key advances over the past decade and discuss future directions.

Hydra: Imaging Nerve Nets in Action

Mapping whole-brain activity during behavior represents one of the biggest and most exciting challenges of systems neuroscience. New research has taken advantage of the unique biology of an ancient organism to bring us a step closer to that goal.

Rapid volumetric optoacoustic imaging of neural dynamics across the mouse brain

Efforts to scale neuroimaging towards the direct visualization of mammalian brain-wide neuronal activity have faced major challenges. Although high-resolution optical imaging of the whole brain in small animals has been achieved ex vivo, the real-time and direct monitoring of large-scale neuronal activity remains difficult, owing to the performance gap between localized, largely invasive, optical microscopy of rapid, cellular-resolved neuronal activity and whole-brain macroscopy of slow haemodynamics and metabolism. Here, we demonstrate both ex vivo and non-invasive in vivo functional optoacoustic (OA) neuroimaging of mice expressing the genetically encoded calcium indicator GCaMP6f. The approach offers rapid, high-resolution three-dimensional snapshots of whole-brain neuronal activity maps using single OA excitations, and of stimulus-evoked slow haemodynamics and fast calcium activity in the presence of strong haemoglobin background absorption. By providing direct neuroimaging at depths and spatiotemporal resolutions superior to optical fluorescence imaging, functional OA neuroimaging bridges the gap between functional microscopy and whole-brain macroscopy.

New Neuroscience of Homeostasis and Drives for Food, Water, and Salt

Well-being requires the maintenance of energy stores, water, and sodium within permissive zones. The brain, as ringleader, orchestrates their homeostatic control. It senses disturbances, decides what needs to be done next, and then restores balance by altering physiological processes and ingestive drives (i.e., hunger, thirst, and salt appetite). But how the brain orchestrates this control has been unknown until recently — largely because we have lacked the ability to elucidate and then probe the underlying neuronal “wiring diagrams.” This has changed with the advent of new, transformative neuroscientific tools. When targeted to specific neurons, these tools make it possible to selectively map a neuron’s connections, measure its responses to various homeostatic challenges, and experimentally manipulate its activity. This review examines these approaches and then highlights how they are advancing, and in some cases profoundly changing, our understanding of energy, water, and salt homeostasis and the linked ingestive drives.

Detection and analysis of spatiotemporal patterns in brain activity

There is growing evidence that population-level brain activity is often organized into propagating waves that are structured in both space and time. Such spatiotemporal patterns have been linked to brain function and observed across multiple recording methodologies and scales. The ability to detect and analyze these patterns is thus essential for understanding the working mechanisms of neural circuits. Here we present a mathematical and computational framework for the identification and analysis of multiple classes of wave patterns in neural population-level recordings. By drawing a conceptual link between spatiotemporal patterns found in the brain and coherent structures such as vortices found in turbulent flows, we introduce velocity vector fields to characterize neural population activity. These vector fields are calculated for both phase and amplitude of oscillatory neural signals by adapting optical flow estimation methods from the field of computer vision. Based on these velocity vector fields, we then introduce order parameters and critical point analysis to detect and characterize a diverse range of propagating wave patterns, including planar waves, sources, sinks, spiral waves, and saddle patterns. We also introduce a novel vector field decomposition method that extracts the dominant spatiotemporal structures in a recording. This enables neural data to be represented by the activity of a small number of independent spatiotemporal modes, providing an alternative to existing dimensionality reduction techniques which separate space and time components. We demonstrate the capabilities of the framework and toolbox with simulated data, local field potentials from marmoset visual cortex and optical voltage recordings from whole mouse cortex, and we show that pattern dynamics are non-random and are modulated by the presence of visual stimuli. These methods are implemented in a MATLAB toolbox, which is freely available under an open-source licensing agreement.

Structured activity such as propagating wave patterns at the level of neural circuits can arise from highly variable firing activity of individual neurons. This property makes the brain, a quintessential example of a complex system, analogous to other complex physical systems such as turbulent fluids, in which structured patterns like vortices similarly emerge from molecules that behave irregularly. In this study, by uniquely adapting techniques for the identification of coherent structures in fluid turbulence, we develop new analytical and computational methods for the reliable detection of a diverse range of propagating wave patterns in large-scale neural recordings, for comprehensive analysis and visualization of these patterns, and for analysis of their dominant spatiotemporal modes. We demonstrate that these methods can be used to uncover the essential spatiotemporal properties of neural population activity recorded by different modalities, thus offering new insights into understanding the working mechanisms of neural systems.

Cortical circuit activity underlying sleep slow oscillations and spindles

Slow oscillations and sleep spindles are hallmarks of the EEG during slow-wave sleep (SWS). Both oscillatory events, especially when co-occurring in the constellation of spindles nesting in the slow oscillation upstate, are considered to support memory formation and underlying synaptic plasticity. The regulatory mechanisms of this function at the circuit level are poorly understood. Here, using two-photon imaging in mice, we relate EEG-recorded slow oscillations and spindles to calcium signals recorded from the soma of cortical putative pyramidal-like (Pyr) cells and neighboring parvalbumin-positive interneurons (PV-Ins) or somatostatin-positive interneurons (SOM-Ins). Pyr calcium activity was increased more than threefold when spindles co-occurred with slow oscillation upstates compared with slow oscillations or spindles occurring in isolation. Independent of whether or not a spindle was nested in the slow oscillation upstate, the slow oscillation downstate was preceded by enhanced calcium signal in SOM-Ins that vanished during the upstate, whereas spindles were associated with strongly increased PV-In calcium activity. Additional wide-field calcium imaging of Pyr cells confirmed the enhanced calcium activity and its widespread topography associated with spindles nested in slow oscillation upstates. In conclusion, when spindles are nested in slow oscillation upstates, maximum Pyr activity appears to concur with strong perisomatic inhibition of Pyr cells via PV-Ins and low dendritic inhibition via SOM-Ins (i.e., conditions that might optimize synaptic plasticity within local cortical circuits).

Genetically encoded fluorescent voltage indicators: are we there yet?

In order to understand how brain activity produces adaptive behavior we need large-scale, high-resolution recordings of neuronal activity. Fluorescent genetically encoded voltage indicators (GEVIs) offer the potential for these recordings to be performed chronically from targeted cells in a minimally invasive manner. As the number of GEVIs successfully tested for in vivo use grows, so has the number of open questions regarding the improvements that would facilitate broad adoption of this technology that surpasses mere 'proof of principle' studies. Our aim in this review is not to provide a status check of the current state of the field, as excellent publications covering this topic already exist. Here, we discuss specific questions regarding GEVI development and application that we think are crucial in achieving this goal.

Three dimensional two-photon brain imaging in freely moving mice using a miniature fiber coupled microscope with active axial-scanning

We present a miniature head mounted two-photon fiber-coupled microscope (2P-FCM) for neuronal imaging with active axial focusing enabled using a miniature electrowetting lens. We show three-dimensional two-photon imaging of neuronal structure and record neuronal activity from GCaMP6s fluorescence from multiple focal planes in a freely-moving mouse. Two-color simultaneous imaging of GFP and tdTomato fluorescence is also demonstrated. Additionally, dynamic control of the axial scanning of the electrowetting lens allows tilting of the focal plane enabling neurons in multiple depths to be imaged in a single plane. Two-photon imaging allows increased penetration depth in tissue yielding a working distance of 450 μm with an additional 180 μm of active axial focusing. The objective NA is 0.45 with a lateral resolution of 1.8 μm, an axial resolution of 10 μm, and a field-of-view of 240 μm diameter. The 2P-FCM has a weight of only ~2.5 g and is capable of repeatable and stable head-attachment. The 2P-FCM with dynamic axial scanning provides a new capability to record from functionally distinct neuronal layers, opening new opportunities in neuroscience research.

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

Linking neuronal lineage and wiring specificity

Brain function requires precise neural circuit assembly during development. Establishing a functional circuit involves multiple coordinated steps ranging from neural cell fate specification to proper matching between pre- and post-synaptic partners. How neuronal lineage and birth timing influence wiring specificity remains an open question. Recent findings suggest that the relationships between lineage, birth timing, and wiring specificity vary in different neuronal circuits. In this review, we summarize our current understanding of the cellular, molecular, and developmental mechanisms linking neuronal lineage and birth timing to wiring specificity in a few specific systems in Drosophila and mice, and review different methods employed to explore these mechanisms.

FISSA: A neuropil decontamination toolbox for calcium imaging signals

In vivo calcium imaging has become a method of choice to image neuronal population activity throughout the nervous system. These experiments generate large sequences of images. Their analysis is computationally intensive and typically involves motion correction, image segmentation into regions of interest (ROIs), and extraction of fluorescence traces from each ROI. Out of focus fluorescence from surrounding neuropil and other cells can strongly contaminate the signal assigned to a given ROI. In this study, we introduce the FISSA toolbox (Fast Image Signal Separation Analysis) for neuropil decontamination. Given pre-defined ROIs, the FISSA toolbox automatically extracts the surrounding local neuropil and performs blind-source separation with non-negative matrix factorization. Using both simulated and in vivo data, we show that this toolbox performs similarly or better than existing published methods. FISSA requires only little RAM, and allows for fast processing of large datasets even on a standard laptop. The FISSA toolbox is available in Python, with an option for MATLAB format outputs, and can easily be integrated into existing workflows. It is available from Github and the standard Python repositories.

Dopaminergic dysfunction in neurodevelopmental disorders: recent advances and synergistic technologies to aid basic research

Neurodevelopmental disorders (NDDs) represent a diverse group of syndromes characterized by abnormal development of the central nervous system and whose symptomatology includes cognitive, emotional, sensory, and motor impairments. The identification of causative genetic defects has allowed for creation of transgenic NDD mouse models that have revealed pathophysiological mechanisms of disease phenotypes in a neural circuit- and cell type-specific manner. Mouse models of several syndromes, including Rett syndrome, Fragile X syndrome, Angelman syndrome, Neurofibromatosis type 1, etc., exhibit abnormalities in the structure and function of dopaminergic circuitry, which regulates motivation, motor behavior, sociability, attention, and executive function. Recent advances in technologies for functional circuit mapping, including tissue clearing, viral vector-based tracing methods, and optical readouts of neural activity, have refined our knowledge of dopaminergic circuits in unperturbed states, yet these tools have not been widely applied to NDD research. Here, we will review recent findings exploring dopaminergic function in NDD models and discuss the promise of new tools to probe NDD pathophysiology in these circuits.

Frequency selectivity of echo responses in the mouse primary auditory cortex

In the primary auditory cortex (A1), neuronal ensembles are activated relative to anticipated sound events following rhythmic stimulation, but whether the echo responses of the neurons are related to their frequency selectivity remains unknown. Therefore, we used in vivo two-photon Ca²⁺ imaging to record the neuronal activities in the mouse A1 to elucidate the relationship between their echo responses and frequency selectivity. We confirmed the presence of echo responses in a subgroup of mouse Layer 2/3 A1 neurons following a train of rhythmic pure tone stimulation. After testing with a range of frequencies, we found that these echo responses occurred preferentially close to the best frequencies of the neurons. The local organization of the echo responses of the neurons was heterogeneous in the A1. Therefore, these results indicate that the observed echo responses of neurons within A1 are highly related to their frequency selectivity.

Neurophysiological Assessment of Huntington's Disease Model Mice

Electrophysiological and cell imaging techniques are powerful tools for understanding alterations in neuronal activity in Huntington's disease (HD), a fatal neurological disorder caused by an expansion of CAG repeats in the HTT gene. Changes in neuronal activity often precede the behavioral manifestations of HD, therefore, understanding the electrophysiology of HD is critical for identifying potential prodromal markers and therapeutic targets. This chapter outlines the basic methodology behind four major electrophysiological and imaging techniques used in HD mouse models: patch clamp recordings, optogenetics, in vivo electrophysiology, and Ca²⁺ imaging, as well as some of the advancements in HD research using each of these techniques.

Dynamical networks: Finding, measuring, and tracking neural population activity using network science

Systems neuroscience is in a headlong rush to record from as many neurons at the same time as possible. As the brain computes and codes using neuron populations, it is hoped these data will uncover the fundamentals of neural computation. But with hundreds, thousands, or more simultaneously recorded neurons come the inescapable problems of visualizing, describing, and quantifying their interactions. Here I argue that network science provides a set of scalable, analytical tools that already solve these problems. By treating neurons as nodes and their interactions as links, a single network can visualize and describe an arbitrarily large recording. I show that with this description we can quantify the effects of manipulating a neural circuit, track changes in population dynamics over time, and quantitatively define theoretical concepts of neural populations such as cell assemblies. Using network science as a core part of analyzing population recordings will thus provide both qualitative and quantitative advances to our understanding of neural computation.

Linking neuronal structure to function in rodent hippocampus: a methodological prospective

Since the discovery of place cells, hippocampus-dependent spatial navigation has proven to be an ideal model system for resolving the relationship between neural coding and behavior. Electrical recordings from the hippocampal formation in freely moving animals have revealed a rich repertoire of spatial firing patterns and have enormously advanced our understanding of the neural principles of spatial representation. However, limited progress has been achieved in resolving the underlying cellular mechanisms. This is partially attributable to the inability of standard recording techniques to link neuronal structure to function directly. In this review, we summarize recent efforts aimed at filling this gap. We also highlight the development of methodologies that allow functional measurements from identified neuronal elements in behaving rodents. Recent progress in the dentate gyrus serves as a showcase to reveal the potential of such methodologies and the necessity of resolving structure-function relationships in order to access the cellular mechanisms of hippocampal circuit computations.

NeuroSeg: automated cell detection and segmentation for in vivo two-photon Ca2+ imaging data

Two-photon Ca²⁺ imaging has become a popular approach for monitoring neuronal population activity with cellular or subcellular resolution in vivo. This approach allows for the recording of hundreds to thousands of neurons per animal and thus leads to a large amount of data to be processed. In particular, manually drawing regions of interest is the most time-consuming aspect of data analysis. However, the development of automated image analysis pipelines, which will be essential for dealing with the likely future deluge of imaging data, remains a major challenge. To address this issue, we developed NeuroSeg, an open-source MATLAB program that can facilitate the accurate and efficient segmentation of neurons in two-photon Ca²⁺ imaging data. We proposed an approach using a generalized Laplacian of Gaussian filter to detect cells and weighting-based segmentation to separate individual cells from the background. We tested this approach on an in vivo two-photon Ca²⁺ imaging dataset obtained from mouse cortical neurons with differently sized view fields. We show that this approach exhibits superior performance for cell detection and segmentation compared with the existing published tools. In addition, we integrated the previously reported, activity-based segmentation into our approach and found that this combined method was even more promising. The NeuroSeg software, including source code and graphical user interface, is freely available and will be a useful tool for in vivo brain activity mapping.