Wide-field Calcium Imaging Research Articles

Wide-field calcium imaging of cortical activation and functional connectivity in externally- and internally-driven locomotion

The role of the cerebral cortex in self-initiated versus sensory-driven movements is central to understanding volitional action. Whether the differences in these two movement classes are due to specific cortical areas versus more cortex-wide engagement is debated. Using wide-field Ca2+ imaging, we compared neural dynamics during spontaneous and motorized treadmill locomotion, determining the similarities and differences in cortex-wide activation and functional connectivity (FC). During motorized locomotion, the cortex exhibits greater activation globally prior to and during locomotion starting compared to spontaneous and less during steady-state walking, during stopping, and after termination. Both conditions are characterized by FC increases in anterior secondary motor cortex (M2) nodes and decreases in all other regions. There are also cortex-wide differences; most notably, M2 decreases in FC with all other nodes during motorized stopping and after termination. Therefore, both internally- and externally-generated movements widely engage the cortex, with differences represented in cortex-wide activation and FC patterns.

Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data

BackgroundWide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming, invasive and often suffers from low inter- and intra-rater reliability. Therefore, an automated sleep state classification method that operates on spatiotemporal WFCI data is desired. New methodA hybrid network architecture consisting of a convolutional neural network (CNN) to extract spatial features of image frames and a bidirectional long short-term memory network (BiLSTM) with attention mechanism to identify temporal dependencies among different time points was proposed to classify WFCI data into states of wakefulness, NREM and REM sleep. ResultsSleep states were classified with an accuracy of 84 % and Cohen’s κ of 0.64. Gradient-weighted class activation maps revealed that the frontal region of the cortex carries more importance when classifying WFCI data into NREM sleep while posterior area contributes most to the identification of wakefulness. The attention scores indicated that the proposed network focuses on short- and long-range temporal dependency in a state-specific manner. Comparison with existing methodOn a held out, repeated 3-hour WFCI recording, the CNN-BiLSTM achieved a κ of 0.67, comparable to a κ of 0.65 corresponding to the human EEG/EMG-based scoring. ConclusionsThe CNN-BiLSTM effectively classifies sleep states from spatiotemporal WFCI data and will enable broader application of WFCI in sleep research.

Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes

Biological visual systems have evolved to process natural scenes. A full understanding of visual cortical functions requires a comprehensive characterization of how neuronal populations in each visual area encode natural scenes. Here, we utilized widefield calcium imaging to record V4 cortical response to tens of thousands of natural images in male macaques. Using this large dataset, we developed a deep-learning digital twin of V4 that allowed us to map the natural image preferences of the neural population at 100-µm scale. This detailed map revealed a diverse set of functional domains in V4, each encoding distinct natural image features. We validated these model predictions using additional widefield imaging and single-cell resolution two-photon imaging. Feature attribution analysis revealed that these domains lie along a continuum from preferring spatially localized shape features to preferring spatially dispersed surface features. These results provide insights into the organizing principles that govern natural scene encoding in V4.

Fluorescence microscopy for brain activity imaging: one-photon microscopy and its application to pharmacological research

The development of genetically-encoded fluorescent probes for the detection of intracellular calcium ions and various neurotransmitters has progressed significantly in recent years, and there is a growing need for techniques that rapidly and efficiently image these signals in the living brain for pharmacological studies of the central nervous system. In this article, we discuss one-photon fluorescence microscopy techniques used for brain activity imaging, particularly wide-field imaging and head-mounted miniaturized microscopy, and introduce their basic principles, recent advances, and applications in pharmacological research. Wide-field calcium imaging is suitable for mesoscopic observation of cortical activity during behavioral tasks in head-fixed awake mice, while head-mounted miniaturized microscopes can be attached to the animal's head to image brain activity associated with naturalistic behaviors such as social behavior and sleep. One-photon microscopy allows for the development of a simple and cost-effective imaging system using an affordable excitation light source such as a light-emitting diode. Its excitation light illuminates the entire field of view simultaneously, making it easy to perform high-speed imaging using a high-sensitivity camera. In contrast, the short wavelength of the excitation light limits the field of observation to areas on or near the brain surface due to its strong light scattering. Moreover, the out-of-focus fluorescence makes it difficult to obtain images with a high signal-to-noise ratio and spatial resolution. The use of one-photon microscopy in brain activity imaging has been limited compared to two-photon microscopy, but its advantages have recently been revisited. Therefore, this technique is expected to become a useful method for pharmacologists to visualize the activity of the living brain.

An LSTM-based adversarial variational autoencoder framework for self-supervised neural decoding of behavioral choices

Objective.This paper presents data-driven solutions to address two challenges in the problem of linking neural data and behavior: (1) unsupervised analysis of behavioral data and automatic label generation from behavioral observations, and (2) extraction of subject-invariant features for the development of generalized neural decoding models. Approach. For behavioral analysis and label generation, an unsupervised method, which employs an autoencoder to transform behavioral data into a cluster-friendly feature space is presented. The model iteratively refines the assigned clusters with soft clustering assignment loss, and gradually improves the learned feature representations. To address subject variability in decoding neural activity, adversarial learning in combination with a long short-term memory-based adversarial variational autoencoder (LSTM-AVAE) model is employed. By using an adversary network to constrain the latent representations, the model captures shared information among subjects’ neural activity, making it proper for cross-subject transfer learning. Main results. The proposed approach is evaluated using cortical recordings of Thy1-GCaMP6s transgenic mice obtained via widefield calcium imaging during a motivational licking behavioral experiment. The results show that the proposed model achieves an accuracy of 89.7% in cross-subject neural decoding, outperforming other well-known autoencoder-based feature learning models. These findings suggest that incorporating an adversary network eliminates subject dependency in representations, leading to improved cross-subject transfer learning performance, while also demonstrating the effectiveness of LSTM-based models in capturing the temporal dependencies within neural data. Significance. Results demonstrate the feasibility of the proposed framework in unsupervised clustering and label generation of behavioral data, as well as achieving high accuracy in cross-subject neural decoding, indicating its potentials for relating neural activity to behavior.

Developmental maturation of millimeter-scale functional networks across brain areas.

Interacting with the environment to process sensory information, generate perceptions, and shape behavior engages neural networks in brain areas with highly varied representations, ranging from unimodal sensory cortices to higher-order association areas. Recent work suggests a much greater degree of commonality across areas, with distributed and modular networks present in both sensory and non-sensory areas during early development. However, it is currently unknown whether this initially common modular structure undergoes an equally common developmental trajectory, or whether such a modular functional organization persists in some areas-such as primary visual cortex-but not others. Here we examine the development of network organization across diverse cortical regions in ferrets of both sexes using in vivo widefield calcium imaging of spontaneous activity. We find that all regions examined, including both primary sensory cortices (visual, auditory, and somatosensory-V1, A1, and S1, respectively) and higher order association areas (prefrontal and posterior parietal cortices) exhibit a largely similar pattern of changes over an approximately 3 week developmental period spanning eye opening and the transition to predominantly externally-driven sensory activity. We find that both a modular functional organization and millimeter-scale correlated networks remain present across all cortical areas examined. These networks weakened over development in most cortical areas, but strengthened in V1. Overall, the conserved maintenance of modular organization across different cortical areas suggests a common pathway of network refinement, and suggests that a modular organization-known to encode functional representations in visual areas-may be similarly engaged in highly diverse brain areas.

Self-organization of modular activity in immature cortical networks

During development, cortical activity is organized into distributed modular patterns that are a precursor of the mature columnar functional architecture. Theoretically, such structured neural activity can emerge dynamically from local synaptic interactions through a recurrent network with effective local excitation with lateral inhibition (LE/LI) connectivity. Utilizing simultaneous widefield calcium imaging and optogenetics in juvenile ferret cortex prior to eye opening, we directly test several critical predictions of an LE/LI mechanism. We show that cortical networks transform uniform stimulations into diverse modular patterns exhibiting a characteristic spatial wavelength. Moreover, patterned optogenetic stimulation matching this wavelength selectively biases evoked activity patterns, while stimulation with varying wavelengths transforms activity towards this characteristic wavelength, revealing a dynamic compromise between input drive and the network’s intrinsic tendency to organize activity. Furthermore, the structure of early spontaneous cortical activity – which is reflected in the developing representations of visual orientation – strongly overlaps that of uniform opto-evoked activity, suggesting a common underlying mechanism as a basis for the formation of orderly columnar maps underlying sensory representations in the brain.

Group ICA of wide-field calcium imaging data reveals the retrosplenial cortex as a major contributor to cortical activity during anesthesia.

Large-scale cortical dynamics play a crucial role in many cognitive functions such as goal-directed behaviors, motor learning and sensory processing. It is well established that brain states including wakefulness, sleep, and anesthesia modulate neuronal firing and synchronization both within and across different brain regions. However, how the brain state affects cortical activity at the mesoscale level is less understood. This work aimed to identify the cortical regions engaged in different brain states. To this end, we employed group ICA (Independent Component Analysis) to wide-field imaging recordings of cortical activity in mice during different anesthesia levels and the awake state. Thanks to this approach we identified independent components (ICs) representing elements of the cortical networks that are common across subjects under decreasing levels of anesthesia toward the awake state. We found that ICs related to the retrosplenial cortices exhibited a pronounced dependence on brain state, being most prevalent in deeper anesthesia levels and diminishing during the transition to the awake state. Analyzing the occurrence of the ICs we found that activity in deeper anesthesia states was characterized by a strong correlation between the retrosplenial components and this correlation decreases when transitioning toward wakefulness. Overall these results indicate that during deeper anesthesia states coactivation of the posterior-medial cortices is predominant over other connectivity patterns, whereas a richer repertoire of dynamics is expressed in lighter anesthesia levels and the awake state.

Diverse and asymmetric patterns of single-neuron projectome in regulating interhemispheric connectivity

The corpus callosum, historically considered primarily for homotopic connections, supports many heterotopic connections, indicating complex interhemispheric connectivity. Understanding this complexity is crucial yet challenging due to diverse cell-specific wiring patterns. Here, we utilized public AAV bulk tracing and single-neuron tracing data to delineate the anatomical connection patterns of mouse brains and conducted wide-field calcium imaging to assess functional connectivity across various brain states in male mice. The single-neuron data uncovered complex and dense interconnected patterns, particularly for interhemispheric-heterotopic connections. We proposed a metric “heterogeneity” to quantify the complexity of the connection patterns. Computational modeling of these patterns suggested that the heterogeneity of upstream projections impacted downstream homotopic functional connectivity. Furthermore, higher heterogeneity observed in interhemispheric-heterotopic projections would cause lower strength but higher stability in functional connectivity than their intrahemispheric counterparts. These findings were corroborated by our wide-field functional imaging data, underscoring the important role of heterotopic-projection heterogeneity in interhemispheric communication.

Antipsychotic drugs selectively decorrelate long-range interactions in deep cortical layers.

Psychosis is characterized by a diminished ability of the brain to distinguish externally driven activity patterns from self-generated activity patterns. Antipsychotic drugs are a class of small molecules with relatively broad binding affinity for a variety of neuromodulator receptors that, in humans, can prevent or ameliorate psychosis. How these drugs influence the function of cortical circuits, and in particular their ability to distinguish between externally and self-generated activity patterns, is still largely unclear. To have experimental control over self-generated sensory feedback, we used a virtual reality environment in which the coupling between movement and visual feedback can be altered. We then used widefield calcium imaging to determine the cell type-specific functional effects of antipsychotic drugs in mouse dorsal cortex under different conditions of visuomotor coupling. By comparing cell type-specific activation patterns between locomotion onsets that were experimentally coupled to self-generated visual feedback and locomotion onsets that were not coupled, we show that deep cortical layers were differentially activated in these two conditions. We then show that the antipsychotic drug clozapine disrupted visuomotor integration at locomotion onsets also primarily in deep cortical layers. Given that one of the key components of visuomotor integration in cortex is long-range cortico-cortical connections, we tested whether the effect of clozapine was detectable in the correlation structure of activity patterns across dorsal cortex. We found that clozapine as well as two other antipsychotic drugs, aripiprazole and haloperidol, resulted in a strong reduction in correlations of layer 5 activity between cortical areas and impaired the spread of visuomotor prediction errors generated in visual cortex. Our results are consistent with the interpretation that a major functional effect of antipsychotic drugs is a selective alteration of long-range layer 5-mediated communication.

Antipsychotic drugs selectively decorrelate long-range interactions in deep cortical layers

Psychosis is characterized by a diminished ability of the brain to distinguish externally driven activity patterns from self-generated activity patterns. Antipsychotic drugs are a class of small molecules with relatively broad binding affinity for a variety of neuromodulator receptors that, in humans, can prevent or ameliorate psychosis. How these drugs influence the function of cortical circuits, and in particular their ability to distinguish between externally and self-generated activity patterns, is still largely unclear. To have experimental control over self-generated sensory feedback, we used a virtual reality environment in which the coupling between movement and visual feedback can be altered. We then used widefield calcium imaging to determine the cell type-specific functional effects of antipsychotic drugs in mouse dorsal cortex under different conditions of visuomotor coupling. By comparing cell type-specific activation patterns between locomotion onsets that were experimentally coupled to self-generated visual feedback and locomotion onsets that were not coupled, we show that deep cortical layers were differentially activated in these two conditions. We then show that the antipsychotic drug clozapine disrupted visuomotor integration at locomotion onsets also primarily in deep cortical layers. Given that one of the key components of visuomotor integration in cortex is long-range cortico-cortical connections, we tested whether the effect of clozapine was detectable in the correlation structure of activity patterns across dorsal cortex. We found that clozapine as well as two other antipsychotic drugs, aripiprazole and haloperidol, resulted in a strong reduction in correlations of layer 5 activity between cortical areas and impaired the spread of visuomotor prediction errors generated in visual cortex. Our results are consistent with the interpretation that a major functional effect of antipsychotic drugs is a selective alteration of long-range layer 5-mediated communication.

Common modular architecture across diverse cortical areas in early development

In order to deal with a complex environment, animals form a diverse range of neural representations that vary across cortical areas, ranging from largely unimodal sensory input to higher-order representations of goals, outcomes, and motivation. The developmental origin of this diversity is currently unclear, as representations could arise through processes that are already area-specific from the earliest developmental stages or alternatively, they could emerge from an initially common functional organization shared across areas. Here, we use spontaneous activity recorded with two-photon and widefield calcium imaging to reveal the functional organization across the early developing cortex in ferrets, a species with a well-characterized columnar organization and modular structure of spontaneous activity in the visual cortex. We find that in animals 7 to 14 d prior to eye-opening and ear canal opening, spontaneous activity in both sensory areas (auditory and somatosensory cortex, A1 and S1, respectively), and association areas (posterior parietal and prefrontal cortex, PPC and PFC, respectively) showed an organized and modular structure that is highly similar to the organization in V1. In all cortical areas, this modular activity was distributed across the cortical surface, forming functional networks that exhibit millimeter-scale correlations. Moreover, this modular structure was evident in highly coherent spontaneous activity at the cellular level, with strong correlations among local populations of neurons apparent in all cortical areas examined. Together, our results demonstrate a common distributed and modular organization across the cortex during early development, suggesting that diverse cortical representations develop initially according to similar design principles.

Movement-Related Modulation in Mouse Auditory Cortex Is Widespread Yet Locally Diverse.

Neurons in the mouse auditory cortex are strongly influenced by behavior, including both suppression and enhancement of sound-evoked responses during movement. The mouse auditory cortex comprises multiple fields with different roles in sound processing and distinct connectivity to movement-related centers of the brain. Here, we asked whether movement-related modulation in male mice might differ across auditory cortical fields, thereby contributing to the heterogeneity of movement-related modulation at the single-cell level. We used wide-field calcium imaging to identify distinct cortical fields and cellular-resolution two-photon calcium imaging to visualize the activity of layer 2/3 excitatory neurons within each field. We measured each neuron's responses to three sound categories (pure tones, chirps, and amplitude-modulated white noise) as mice rested and ran on a non-motorized treadmill. We found that individual neurons in each cortical field typically respond to just one sound category. Some neurons are only active during rest and others during locomotion, and those that are responsive across conditions retain their sound-category tuning. The effects of locomotion on sound-evoked responses vary at the single-cell level, with both suppression and enhancement of neural responses, and the net modulatory effect of locomotion is largely conserved across cortical fields. Movement-related modulation in auditory cortex also reflects more complex behavioral patterns, including instantaneous running speed and nonlocomotor movements such as grooming and postural adjustments, with similar patterns seen across all auditory cortical fields. Our findings underscore the complexity of movement-related modulation throughout the mouse auditory cortex and indicate that movement-related modulation is a widespread phenomenon.

A Siamese Convolutional Neural Network for Identifying Mild Traumatic Brain Injury and Predicting Recovery.

Timely diagnosis of mild traumatic brain injury (mTBI) remains challenging due to the rapid recovery of acute symptoms and the absence of evidence of injury in static neuroimaging scans. Furthermore, while longitudinal tracking of mTBI is essential in understanding how the diseases progresses/regresses over time for enhancing personalized patient care, a standardized approach for this purpose is not yet available. Recent functional neuroimaging studies have provided evidence of brain function alterations following mTBI, suggesting mTBI-detection models can be built based on these changes. Most of these models, however, rely on manual feature engineering, but the optimal set of features for detecting mTBI may be unknown. Data-driven approaches, on the other hand, may uncover hidden relationships in an automated manner, making them suitable for the problem of mTBI detection. This paper presents a data-driven framework based on Siamese Convolutional Neural Network (SCNN) to detect mTBI and to monitor the recovery state from mTBI over time. The proposed framework is tested on the cortical images of Thy1-GCaMP6s mice, obtained via widefield calcium imaging, acquired in a longitudinal study. Results show that the proposed model achieves a classification accuracy of 96.5%. To track the state of the injured brain over time, a reference distance map is constructed, which together with the SCNN model, are employed to assess the recovery state in subsequent sessions after injury, revealing that the recovery progress varies among subjects. The promising results of this work suggest that a similar approach could be potentially applicable for monitoring recovery from mTBI, in humans.

Hippocampal cognitive and relational map paradigms explored by multisensory encoding recording with wide-field calcium imaging

Hippocampal cognitive and relational map paradigms explored by multisensory encoding recording with wide-field calcium imaging

Bilateral widefield calcium imaging reveals circuit asymmetries and lateralized functional activation of the mouse auditory cortex

Coordinated functioning of the two cortical hemispheres is crucial for perception. The human auditory cortex (ACx) shows functional lateralization with the left hemisphere specialized for processing speech, whereas the right analyzes spectral content. In mice, virgin females demonstrate a left-hemisphere response bias to pup vocalizations that strengthens with motherhood. However, how this lateralized function is established is unclear. We developed a widefield imaging microscope to simultaneously image both hemispheres of mice to bilaterally monitor functional responses. We found that global ACx topography is symmetrical and stereotyped. In both male and virgin female mice, the secondary auditory cortex (A2) in the left hemisphere shows larger responses than right to high-frequency tones and adult vocalizations; however, only virgin female mice show a left-hemisphere bias in A2 in response to adult pain calls. These results indicate hemispheric bias with both sex-independent and -dependent aspects. Analyzing cross-hemispheric functional correlations showed that asymmetries exist in the strength of correlations between DM-AAF and A2-AAF, while other ACx areas showed smaller differences. We found that A2 showed lower cross-hemisphere correlation than other cortical areas, consistent with the lateralized functional activation of A2. Cross-hemispheric activity correlations are lower in deaf, otoferlin knockout (OTOF-/-) mice, indicating that the development of functional cross-hemispheric connections is experience dependent. Together, our results reveal that ACx is topographically symmetric at the macroscopic scale but that higher-order A2 shows sex-dependent and independent lateralized responses due to asymmetric intercortical functional connections. Moreover, our results suggest that sensory experience is required to establish functional cross-hemispheric connectivity.

Calcium Imaging in Mouse Superior Colliculus.

The superior colliculus (SC), an evolutionarily conserved midbrain structure in all vertebrates, is the most sophisticated visual center before the emergence of the cerebral cortex. It receives direct inputs from ~30 types of retinal ganglion cells (RGCs), with each encoding a specific visual feature. It remains elusive whether the SC simply inherits retinal features or if additional and potentially de novo processing occurs in the SC. To reveal the neural coding of visual information in the SC, we provide here a detailed protocol to optically record visual responses with two complementary methods in awake mice. One method uses two-photon microscopy to image calcium activity at single-cell resolution without ablating the overlaying cortex, while the other uses wide-field microscopy to image the whole SC of a mutant mouse whose cortex is largely undeveloped. This protocol details these two methods, including animal preparation, viral injection, headplate implantation, plug implantation, data acquisition, and data analysis. The representative results show that the two-photon calcium imaging reveals visually evoked neuronal responses at single-cell resolution, and the wide-field calcium imaging revealsneural activity across the entire SC. By combining these two methods, one can reveal the neural coding in the SC at different scales, and such combination can also be applied to other brain regions.

Peripersonal encoding of forelimb proprioception in the mouse somatosensory cortex

Conscious perception of limb movements depends on proprioceptive neural responses in the somatosensory cortex. In contrast to tactile sensations, proprioceptive cortical coding is barely studied in the mammalian brain and practically non-existent in rodent research. To understand the cortical representation of this important sensory modality we developed a passive forelimb displacement paradigm in behaving mice and also trained them to perceptually discriminate where their limb is moved in space. We delineated the rodent proprioceptive cortex with wide-field calcium imaging and optogenetic silencing experiments during behavior. Our results reveal that proprioception is represented in both sensory and motor cortical areas. In addition, behavioral measurements and responses of layer 2/3 neurons imaged with two-photon microscopy reveal that passive limb movements are both perceived and encoded in the mouse cortex as a spatial direction vector that interfaces the limb with the body’s peripersonal space.

Convolutional neural network classifies visual stimuli from cortical response recorded with wide-field imaging in mice

Objective. The optic nerve is a good location for a visual neuroprosthesis. It can be targeted when a subject cannot receive a retinal prosthesis and it is less invasive than a cortical implant. The effectiveness of an electrical neuroprosthesis depends on the combination of the stimulation parameters which must be optimized, and an optimization strategy might be performing closed-loop stimulation using the evoked cortical response as feedback. However, it is necessary to identify target cortical activation patterns and to associate the cortical activity with the visual stimuli present in the visual field of the subjects. Visual stimuli decoding should be performed on large areas of the visual cortex, and with a method as translational as possible to shift the study to human subjects in the future. The aim of this work is to develop an algorithm that meets these requirements and can be leveraged to automatically associate a cortical activation pattern with the visual stimulus that generated it. Approach. Three mice were presented with ten different visual stimuli, and their primary visual cortex response was recorded using wide-field calcium imaging. Our decoding algorithm relies on a convolutional neural network (CNN), trained to classify the visual stimuli from the correspondent wide-field images. Several experiments were performed to identify the best training strategy and investigate the possibility of generalization. Main results. The best classification accuracy was 75.38% ± 4.77%, obtained pre-training the CNN on the MNIST digits dataset and fine-tuning it on our dataset. Generalization was possible pre-training the CNN to classify Mouse 1 dataset and fine-tuning it on Mouse 2 and Mouse 3, with accuracies of 64.14% ± 10.81% and 51.53% ± 6.48% respectively. Significance. The combination of wide-field calcium imaging and CNNs can be used to classify the cortical responses to simple visual stimuli and might be a viable alternative to existing decoding methodologies. It also allows us to consider the cortical activation as reliable feedback in future optic nerve stimulation experiments.

Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data

Widefield microscopy can provide optical access to multi-millimeter fields of view and thousands of neurons in mammalian brains at video rate. However, tissue scattering and background contamination results in signal deterioration, making the extraction of neuronal activity challenging, laborious and time consuming. Here we present our deep-learning-based widefield neuron finder (DeepWonder), which is trained by simulated functional recordings and effectively works on experimental data to achieve high-fidelity neuronal extraction. Equipped with systematic background contribution priors, DeepWonder conducts neuronal inference with an order-of-magnitude-faster speed and improved accuracy compared with alternative approaches. DeepWonder removes background contaminations and is computationally efficient. Specifically, DeepWonder accomplishes 50-fold signal-to-background ratio enhancement when processing terabytes-scale cortex-wide functional recordings, with over 14,000 neurons extracted in 17 h.