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Characterizing Cortex-Wide Dynamics with Wide-Field Calcium Imaging

Chi Ren and Takaki Komiyama
Journal of Neuroscience 12 May 2021, 41 (19) 4160-4168; DOI: https://doi.org/10.1523/JNEUROSCI.3003-20.2021
Chi Ren
Neurobiology Section, Center for Neural Circuits and Behavior, Department of Neurosciences, and Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, California 92093
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Takaki Komiyama
Neurobiology Section, Center for Neural Circuits and Behavior, Department of Neurosciences, and Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, California 92093
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Abstract

The brain functions through coordinated activity among distributed regions. Wide-field calcium imaging, combined with improved genetically encoded calcium indicators, allows sufficient signal-to-noise ratio and spatiotemporal resolution to afford a unique opportunity to capture cortex-wide dynamics on a moment-by-moment basis in behaving animals. Recent applications of this approach have been uncovering cortical dynamics at unprecedented scales during various cognitive processes, ranging from relatively simple sensorimotor integration to more complex decision-making tasks. In this review, we will highlight recent scientific advances enabled by wide-field calcium imaging in behaving mice. We then summarize several technical considerations and future opportunities for wide-field imaging to uncover large-scale circuit dynamics.

  • decision making
  • large-scale cortical dynamics
  • learning
  • multimodal recordings
  • sensorimotor integration
  • wide-field calcium imaging

Introduction

The brain is a modular structure in which communication across multiple regions functions to drive behavior and cognition. The emergent properties of such macroscopic interactions cannot be deduced simply by characterizing individual brain regions separately. Therefore, to better understand how the brain functions as a whole, it is critical to record from multiple brain regions simultaneously. Wide-field functional imaging is well suited for this purpose. In systems neuroscience, wide-field calcium imaging has been used to record activity across broad brain areas simultaneously through one-photon excitation (Cardin et al., 2020). Although this technique normally does not resolve single cells, it enables simultaneous capturing of neural dynamics across brain areas with a sufficient spatial and temporal resolution to resolve behaviorally relevant information (for comparisons of various large-scale imaging modalities, see Table 1). This review will mainly focus on macroscale wide-field calcium imaging applied to most of the dorsal cortex in mice. Similar approaches are also called “mesoscale” and “mesoscopic,” often emphasizing the spatial resolution that can resolve subregions within individual brain areas but does not achieve single-cell resolution.

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Table 1.

Comparison of several large-scale imaging modalities in mice based on the parameters typically used in recent studiesa

Wide-field functional imaging has traditionally been achieved by measuring the “intrinsic signal” or using fluorescent voltage-sensitive dyes. Intrinsic signals are changes in optical reflectance caused by changes in blood volume and oxygenation which correlate with local neural activity (Berwick et al., 2005; Ma et al., 2016b; Mateo et al., 2017). Unlike intrinsic signals, voltage-sensitive dyes serve as direct indicators of neural activity by responding to membrane potential changes; furthermore, they provide a higher temporal resolution owing to their faster kinetics (Orbach et al., 1985; Grinvald and Hildesheim, 2004). Although both approaches have been used to characterize large-scale functional properties of cortex (Blasdel and Salama, 1986; Grinvald et al., 1986; Frostig et al., 1990; Bonhoeffer and Grinvald, 1991; Prechtl et al., 1997; Mohajerani et al., 2010), their ability to capture cortical dynamics is limited because of relatively low signal-to-noise ratio (SNR). Therefore, extracting activity patterns often relies on averaging over repeated measurements, ignoring the variability in moment-by-moment interactions between cortical regions.

In recent years, the application of wide-field imaging in systems neuroscience has been revolutionized with the improvement of genetically encoded fluorescent indicators. These engineered proteins change the fluorescence intensity in response to a variety of neuronal events, including transmembrane voltage, intracellular calcium concentration, vesicle release, and changes in neurotransmitter concentration (Lin and Schnitzer, 2016; Sabatini and Tian, 2020). Among these protein sensors, genetically encoded calcium indicators, especially the GCaMP family (Tian et al., 2009; Akerboom et al., 2012; T. W. Chen et al., 2013; X. R. Sun et al., 2013; Y. Yang et al., 2018; Dana et al., 2019), have become a standard choice to visualize neural activity in both one-photon and multiphoton imaging. GCaMP fluorescence is sensitive to changes in intracellular calcium dynamics that are dominated by action potentials and thus reports neuronal spiking activity with high SNR. Genetic encoding of GCaMP also enables stable expression over time for longitudinal recordings. These advantages of GCaMP allow wide-field calcium imaging to overcome the difficulties often encountered with intrinsic signal imaging and voltage-sensitive dye imaging, making it an attractive approach to characterize large-scale cortical dynamics in behaving animals.

Several studies have conducted one-photon calcium imaging with GCaMP at a mesoscale level with the field of view (FOV) covering several adjacent cortical regions in adult animals (Vanni and Murphy, 2014; Niethard et al., 2016; Wekselblatt et al., 2016; T. W. Chen et al., 2017; Zhuang et al., 2017). This approach has also been used to investigate the developing circuits in both cortex and subcortical regions (Ackman et al., 2012; Burbridge et al., 2014; Gribizis et al., 2019). Meanwhile, a growing list of studies use wide-field calcium imaging to characterize cortical activity at a macroscopic level with an FOV encompassing most of the mouse dorsal cortex (Fig. 1). Such studies have deepened our understanding of cortex-wide dynamics in various cognitive processes, ranging from relatively simple sensorimotor integration to more complex decision-making tasks (Allen et al., 2017; Makino et al., 2017; Gilad et al., 2018; Musall et al., 2019; Pinto et al., 2019; Shimaoka et al., 2019; Gilad and Helmchen, 2020; Salkoff et al., 2020). In this review, we first focus on recent studies performing wide-field calcium imaging in behaving mice. Using these example studies, we highlight the versatility of wide-field calcium imaging for revealing novel insights into various questions. We then discuss several technical considerations for wide-field calcium imaging. Finally, we discuss future opportunities for the development and application of wide-field imaging to uncover large-scale circuit dynamics.

Figure 1.
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Figure 1.

Wide-field calcium imaging of cortex-wide activity. A, Left, Imaging setup. Middle, An FOV of wide-field calcium imaging in a mouse cortex expressing GCaMP6s in cortical excitatory neurons. Right, Cortical regions (based on the mouse brain atlas from the Allen Institute) simultaneously recorded by wide-field calcium imaging. Green dashed box represents the area within the FOV. M2, Secondary motor cortex; M1, primary motor cortex; S1, primary somatosensory cortex; PPC, posterior parietal cortex; S2, secondary somatosensory cortex; Aud., auditory cortex; RSC, retrosplenial cortex; Vis., visual cortex. B, Example cortex-wide image frames and fluorescence traces of individual pixels in a behaving mouse. Gray dashed lines indicate the time of image frames in fluorescence traces of example pixels.

Propagation of cortical activity in sensorimotor integration

Generating appropriate actions requires integrating sensory information from the environment, and such sensorimotor processing often recruits distributed brain regions to achieve precise sensory perception, action selection, and movement execution. The spatiotemporal dynamics of large-scale cortical activity during sensorimotor transformation have been studied extensively in the rodent whisker system (Ferezou et al., 2007; Matyas et al., 2010; Kyriakatos et al., 2017; Sreenivasan et al., 2017; Gilad et al., 2018). A series of studies using wide-field voltage-sensitive dye imaging has revealed that a single whisker deflection evokes a highly distributed cortical sensory response, starting in barrel cortex and then propagating to primary motor cortex, to drive whisker movements (Ferezou et al., 2007; Matyas et al., 2010; Kyriakatos et al., 2017). The spread of the sensory response is attenuated during active whisking, when the animal's ability to detect weak stimuli is impaired, suggesting that the distributed sensory response is dynamically modulated by ongoing behavior (Ferezou et al., 2007; Kyriakatos et al., 2017). With wide-field calcium imaging, Gilad et al. (2018) further investigated the macroscopic cortical dynamics under different behavior strategies in a whisker-based texture discrimination task with delayed actions to report the choice (lick or no lick). During the delay period between the texture sensation and the chosen action, the activation of different cortical regions, especially the secondary motor cortex (M2) and a posterior cortical region area P, was contingent on the behavioral strategies animals deployed to solve the task. When mice took an active strategy (engaging their body toward the approaching texture), M2 showed sustained activity during the delay period, holding information about the future action. In contrast, in mice using a passive strategy in which they quietly awaited the texture touch, area P displayed enhanced activity during the delay period, holding information about the stimulus identity. Furthermore, optogenetic inactivation of M2 and area P during the delay period led to impairment in behavioral performance during active and passive strategies, respectively. These results support the model that cortical activity can be dynamically routed to different regions to hold the task-relevant information before converging to similar chosen actions (Gilad et al., 2018). It is worth noting that the unbiased observation with wide-field calcium imaging revealed a novel role of area P in texture discrimination. Area P has been mainly implicated in visual processing in previous literature (Garrett et al., 2014; Zhuang et al., 2017), and its function in tactile texture discrimination suggests that it may be generally involved in processing information related to object identity (Gilad et al., 2018). With wide-field imaging, these studies provide the first glimpse of the macroscopic activity pattern during sensorimotor integration and demonstrate its fundamental flexibility even in simple sensorimotor processing.

Distributed encoding of different types of information in cortex

The distributed activation of many brain areas has been observed in various sensorimotor tasks (Goard et al., 2016; Allen et al., 2017; Kyriakatos et al., 2017; Makino et al., 2017; Gilad et al., 2018; Hattori et al., 2019; Musall et al., 2019; Pinto et al., 2019; Shimaoka et al., 2019; Steinmetz et al., 2019; Gilad and Helmchen, 2020; Salkoff et al., 2020); however, systematic optogenetic inactivation generally localizes behavioral effects to only a few regions (Guo et al., 2014; Goard et al., 2016; Allen et al., 2017; Pinto et al., 2019; Zatka-Haas et al., 2020). Therefore, it is important to resolve the information represented in each cortical region and its relevance to the ongoing behavior. Compared with wide-field imaging using intrinsic signals or voltage-sensitive dyes, the higher SNR of wide-field calcium imaging enables a detailed examination of information encoded in cortical activity using regression and decoding analyses on a trial-by-trial or moment-by-moment basis, without averaging out the behaviorally relevant variability (Allen et al., 2017; Gilad et al., 2018; Musall et al., 2019; Pinto et al., 2019; Salkoff et al., 2020; Zatka-Haas et al., 2020). By monitoring a variety of behavioral information and task events encoded in cortex-wide activity, researchers are able to systematically relate behavioral processes to neural activity (Musall et al., 2019; Shimaoka et al., 2019; Salkoff et al., 2020; Zatka-Haas et al., 2020).

Task-relevant information, such as sensory stimuli and choice, is represented in distributed but specific sets of cortical regions, generating distinct cortical activity patterns during task performance (Gilad et al., 2018; Musall et al., 2019; Pinto et al., 2019; Salkoff et al., 2020; Zatka-Haas et al., 2020). Furthermore, the cortical activity pattern is modulated by task demands. Tasks with complex cognitive demands evoked activity profiles that were more different across cortical regions and engaged more spatially distributed information processing in the cortex (Pinto et al., 2019). For example, the encoding of sensory and choice information was more distributed in evidence accumulation or memory-guided tasks than simple perceptual decision-making tasks (Pinto et al., 2019; Salkoff et al., 2020; Zatka-Haas et al., 2020). The widespread cortical involvement in more demanding tasks was further confirmed with optogenetic inactivation (Pinto et al., 2019). These results suggest that the representation of task-relevant information in the large-scale cortical network is dynamically modulated by the cognitive processes required in different tasks, and more complex cognitive processes engage more spatially distributed computations across the cortex.

In contrast to task-relevant information, movement is represented in widespread areas of the dorsal cortex regardless of the task complexity (Musall et al., 2019; Shimaoka et al., 2019; Salkoff et al., 2020; Zatka-Haas et al., 2020) and learning stage (Musall et al., 2019). The widespread dominance of movement-related information in cortex has also been observed in spontaneous activity recorded with two-photon calcium imaging and in electrophysiological data collected from multiple brain regions during task performance (Steinmetz et al., 2019; Stringer et al., 2019). The prevalent encoding of movement can precede movement onset, arising in the primary motor cortex and expanding to the rest of cortical regions before movement (Zatka-Haas et al., 2020), suggesting an origin from efference copy of the motor command rather than sensory feedback generated by the movement. Further investigation revealed that uninstructed movements, which were not required for the task but spontaneously made by the animals, better explained the trial-by-trial variability in cortex-wide activity than instructed movements and task events. Meanwhile, uninstructed movements could also become correlated with instructed movements and stereotypically occurred around task events, affecting the trial-averaged neural activity (Musall et al., 2019). Although the function of such prevalent encoding of movements, if any, needs further investigation, the profound impact of movements on neural activity has raised the importance of careful behavioral monitoring in the interpretation of neural activity, especially for choices associated with asymmetric motor outputs (e.g., Go/NoGo tasks).

Learning-related dynamics in macroscopic cortical activity

Learning-induced plasticity has been under intense scrutiny with electrophysiological recordings and two-photon calcium imaging (Costa et al., 2004; Peters et al., 2014; Makino and Komiyama, 2015; Grewe et al., 2017). Most of these investigations have focused on the plasticity of local circuits in only one or a few brain regions, omitting one important piece of the puzzle: the interaction across many brain regions during learning. Taking advantage of the stable expression of genetically encoded calcium indicators over time, several recent studies have performed longitudinal wide-field calcium imaging to investigate learning-related macroscopic dynamics (Makino et al., 2017; Musall et al., 2019; Gilad and Helmchen, 2020). Makino et al. (2017) systematically characterized the reconfiguration of cortex-wide activity during motor learning. Consistent with what we have discussed in previous sections, motor learning evoked distributed activation of most of the cortex, forming a macroscopic sequential activity. With learning, this macroscopic sequence of activity during movement execution became more temporally compressed and reproducible from trial to trial, suggesting that more efficient and reliable signal transmission across cortical regions evolves as a function of learning. At the same time, learning rerouted the cortical activity flow. With learning, a novel activity stream originated from M2 and flowed to the rest of the cortex, and the activity of M2 became predictive of the activity of other cortical regions on a moment-by-moment basis. The novel function acquired by M2 during learning was further confirmed with perturbation experiments. Bilateral M2 inactivation with muscimol in expert animals reversed both the cortical dynamics and behavioral performance toward the naive stage of learning, suggesting an indispensable role of M2 in coordinating cortex-wide dynamics for learned behavior (Makino et al., 2017).

The reorganization of cortex-wide activity is not unique to motor learning. Gilad and Helmchen (2020) reported a spatiotemporal refinement of cortex-wide activity flow in an associative learning task, where mice learned to report different textures through licking. At the early stage of learning, task engagement induced a general suppression in association cortices in the interval between the auditory cue signaling the trial start and the whisker-texture touch (the “pre-period”). As learning proceeded, activation increased in rostro-lateral cortex (part of the posterior parietal cortex) and the barrel cortex during the pre-period, building up an anticipatory activity stream arising in rostro-lateral cortex and flowing to the barrel cortex immediately. The specific enhancement of task-related cortical activation emerged in parallel with improved task performance and could contribute to the improved discrimination between different textures (Gilad and Helmchen, 2020). The cortex-wide dynamics observed in different learning tasks demonstrate that the learning-induced plasticity is not only confined to individual cortical regions separately, but also involves cortex-wide changes in the interaction between regions. Such reconfiguration of the large-scale cortical network during learning often involves association cortices and eventually produces more efficient processing of relevant information and more stable representations of learned behaviors.

Multimodal recordings with wide-field calcium imaging

Combining wide-field calcium imaging with complementary imaging modalities

Although wide-field calcium imaging has revealed many novel features of macroscopic cortical dynamics, its current applications are still restricted by two major factors: the lack of single-cell resolution and limited recording depth in brain tissue. These limitations can be mitigated by combining wide-field calcium imaging with other imaging modalities, such as two-photon calcium imaging and fMRI (Barson et al., 2020; Lake et al., 2020). Barson et al. (2020) successfully performed simultaneous wide-field and two-photon calcium imaging in awake mice. To avoid interference between the two imaging modalities, the light path of two-photon calcium imaging was redirected through a microprism mounted on the cortical surface. This multimodal setup is particularly advantageous for investigating the relationships between individual neurons and the entire cortex. For example, Barson et al. (2020) found that the activity of individual neurons in the same cortical region coincided with diverse cortex-wide activity patterns, such that different neurons correlated with different cortex-wide activity patterns. The activity of neighboring neurons can couple with distinct cortical activity patterns, which may arise from different anatomic connectivity. Furthermore, the association between the activity of individual neurons and cortex-wide activity can be modulated by behavioral states (Barson et al., 2020). These results suggest diverse and dynamic associations between local and global neural networks, where information can be dynamically routed depending on behavioral contexts and cognitive processes.

To complement the limited accessibility in the recording depth of wide-field calcium imaging, Lake et al. (2020) combined wide-field calcium imaging and fMRI, which allows simultaneous recording of large-scale cortical and subcortical activity. They found that calcium signals from excitatory neurons partially explained the variance in fMRI BOLD signals. Since the fMRI BOLD signal is an indiscriminate representation of integrated brain activity while wide-field calcium imaging can achieve cell type specificity, this multimodal recording setup could be instrumental in quantifying the contributions of different cell types to the fMRI BOLD signal (Lake et al., 2020).

Combining wide-field calcium imaging with electrophysiological recordings

The relatively simple surgical preparation and imaging setup make wide-field calcium imaging a feasible platform to be combined with electrophysiological recordings. To minimize obstruction of the FOV in wide-field imaging, this combination can be achieved by either inserting a traditional probe (e.g., glass pipette or silicon probe) at an angle (Xiao et al., 2017; Clancy et al., 2019; Peters et al., 2021) or using a flexible transparent probe (Liu et al., 2021). This multimodal recoding setup has been effective in investigating the relationships between cortical or subcortical single-neuron activity and large-scale cortical activity (Xiao et al., 2017; Clancy et al., 2019), as well as the communication between the cortex and subcortical regions (Liu et al., 2021; Peters et al., 2021). Consistent with observations from simultaneous wide-field and two-photon calcium imaging (Barson et al., 2020), multimodal recordings combining wide-field calcium imaging with electrophysiological recordings revealed that the cortex-wide activity patterns associated with single cortical or subcortical neurons were variable from neuron to neuron and modulated by behavior states (Xiao et al., 2017; Clancy et al., 2019).

A more systematic characterization of the functional mapping between cortex and subcortical regions was recently achieved with the Neuropixel probe, which significantly boosted the sampling power of electrophysiological recordings. By simultaneously recording in the cortex with wide-field calcium imaging and in the striatum with Neuropixel probes, Peters et al. (2021) revealed a topographical mapping between cortical and striatal activity. This functional mapping was consistent with the anatomic corticostriatal projections and independent of the animal's behavior states, suggesting that corticostriatal projections reliably propagate cortical activity to the associated striatal regions regardless of the behavioral state (Peters et al., 2021).

In addition to functional mapping, pairing wide-field calcium imaging with electrophysiological recordings can capture real-time interactions between cortex and subcortical regions. Liu et al., 2021 characterized the coordination between the cortex and the hippocampus during awake hippocampal sharp-wave ripples using a newly developed flexible transparent probe (Neuro-FITM). They found that diverse patterns of cortex-wide activity accompanied sharp-wave ripples. In contrast to the conventional view that cortical activity is mainly triggered by hippocampal sharp-wave ripples, the cortical activation preceded hippocampal sharp-wave ripples in a majority of cases. Furthermore, the ongoing cortical patterns could be decoded from the spiking activity of hippocampal neuron populations, indicating a predictable relationship between cortical and hippocampal activity patterns. These results support the model that the hippocampus and the cortex interact during sharp-wave ripples in a selective and diverse manner at the macroscale (Liu et al., 2021).

Combining wide-field calcium imaging and other recording modalities extends the application of wide-field calcium imaging in at least two aspects. First, it bridges the gap between neural activity at different spatial scales and helps study how local circuits relate to larger neural networks (Xiao et al., 2017; Clancy et al., 2019; Barson et al., 2020). As typical two-photon calcium imaging and electrophysiological recordings often focus on a single brain area, investigations of the relationship between individual neurons and the larger brain network will contribute to a more comprehensive interpretation of local neural dynamics. Second, it compensates for the limited accessibility in the recording depth of wide-field calcium imaging and offers an attractive platform to investigate the dynamics of large-scale neural networks spanning the cortex and subcortical regions during various cognitive processes (Lake et al., 2020; Liu et al., 2021; Peters et al., 2021).

Technical considerations of wide-field calcium imaging

Although wide-field calcium imaging is a powerful tool for monitoring large-scale cortical dynamics and the technique per se is relatively simple to set up using conventional wide-field microscopes, several considerations should be kept in mind in the implementation of wide-field calcium imaging. First, wide-field calcium signals are likely dominated by activity from superficial cortical layers because of the strong scattering of both excitation and emission light in brain tissue. In one-photon excitation, the intensity of excitation light (∼480 nm) of GCaMP already drops to ∼10% at a depth of 200 μm (Yizhar et al., 2011), suggesting that most signals come from cortical layer 1 and layer 2/3. Second, as wide-field calcium imaging does not possess single-cell resolution, the signal in each pixel is an integration of both somatic and neuropil activity. The latter mainly consists of activity from the dense neuropils in layer 1, including dendrites from local neurons whose somata reside in layers 2/3 and 5, as well as axons innervating these layer 1 dendrites. Although the majority of wide-field calcium signals reflect local activity (Makino et al., 2017), the contributions of long-range axonal projections are not negligible. Soma-targeting of GCaMP would ensure a cleaner representation of local neural activity in future studies (Y. Chen et al., 2020; Shemesh et al., 2020).

In addition, the raw fluorescence signal of wide-field calcium images is contaminated by hemodynamic changes. The excitation and emission wavelengths of GCaMP reside in the absorption spectrum of oxyhemoglobin and deoxyhemoglobin, so changes in blood oxygenation can contaminate measures of GCaMP fluorescence signals. Currently, there are several methods available to correct hemodynamic contamination. For example, using a secondary wavelength of light allows the estimation of reflectance changes caused by hemodynamics, which can then be used for a regression-based subtraction of hemodynamic signals (Ma et al., 2016a; Wekselblatt et al., 2016; Valley et al., 2020). Low-pass filtering of wide-field signals has also been used to reduce hemodynamic contamination, as hemodynamic artifacts are the strongest in the frequency range corresponding to the heartbeat (Vanni and Murphy, 2014; Xiao et al., 2017). Another analytical correction for hemodynamic signals is to extract hemodynamic components using principal component analysis followed by independent component analysis, and reconstructing the corrected wide-field signals from the remaining components that reflect neural activity (Makino et al., 2017). Alternatively or in addition, repeating the same experiments in animals expressing activity-insensitive GFP can be used as a control to test whether the observed wide-field signals are mainly attributable to neural activity instead of hemodynamic artifacts (Vanni and Murphy, 2014; Wekselblatt et al., 2016).

The temporal resolution of wide-field calcium signals is limited by the relatively slow kinetics of existing calcium indicators. For example, GCaMP6f failed to track synchronous population activity beyond 40 Hz (Li et al., 2019). Deconvolution of wide-field calcium signals can improve the temporal resolution. The heterogeneous spiking activity of many neurons contributing to wide-field calcium signals makes it difficult to generate a general deconvolution algorithm, but attempts are being made to provide the ground truth by simultaneous electrophysiological recordings in the cortex during wide-field calcium imaging (Stern et al., 2020; Peters et al., 2021).

Another issue of consideration arises from parcellation methods used to define cortical regions, as different methods can generate very different results (Barson et al., 2020; Lake et al., 2020). The most common method is to segment the cortex based on an anatomic reference atlas (Wang et al., 2020). The advantage of this approach is the consistency across different studies and research groups, making it convenient to compare results from different studies. However, anatomic reference atlases inevitably ignore individual variations in anatomic structures. Such static atlases also fail to track the dynamic organization of functional cortical modules in different sensory and cognitive processes, which may mask real activity features because of imprecise parcellation (Barson et al., 2020; Saxena et al., 2020). An alternative approach is to define cortical regions based on activity and generate a unique atlas for individual animals. Related methods include grouping pixels using clustering analyses (Barson et al., 2020; Lake et al., 2020) and extracting functional modules using non-negative matrix factorization (Saxena et al., 2020) or independent component analysis (Makino et al., 2017). Compared with anatomic atlases, atlases derived from neural activity can more faithfully represent functional organization of the cortex in individual animals. They may also be able to detect neural dynamics localized to regions that do not correspond to standard areas in anatomic atlases. However, functional modules often vary across individual animals and different studies (Makino et al., 2017; Barson et al., 2020; Lake et al., 2020). Different research groups also use different terminologies to refer to regions in their functional atlases. All these factors make it difficult to compare and interpret results across studies. An open platform that allows researchers to register their functional atlases to a common anatomic framework based on coordinates or certain landmarks (e.g., surface blood vessels) would help comparisons across studies.

Finally, as is common in neural recording experiments, caution is warranted in interpreting cortex-wide activity patterns. Functional connectivity and information flows revealed in recent studies using wide-field calcium imaging were extracted by correlational analyses. In these analyses, whether and how cortical regions are connected and influence each other is unclear. Furthermore, cortical regions exhibiting task-related activity may not actually contribute to task performance (Goard et al., 2016; Allen et al., 2017; Pinto et al., 2019; Zatka-Haas et al., 2020). Combining wide-field calcium imaging with activity manipulations (Allen et al., 2017; Makino et al., 2017) and anatomic tracing (Oh et al., 2014) will provide additional insights into causal relationships between cortical regions and their roles in behavior.

Perspectives

The recent improvements to genetically encoded calcium sensors have resurrected broader interests in using wide-field imaging to investigate large-scale cortical dynamics in behaving animals. As we have discussed above, with wide-field calcium imaging, significant progress has been made to uncover the macroscopic properties of cortical dynamics in various cognitive processes. In the future, we see transformative opportunities for the application of wide-field imaging in the following directions.

Characterizing cell type-specific functions with genetically restricted expression of indicators

The majority of existing studies using wide-field calcium imaging focused on the dynamics of pan-cortical excitatory neurons, but cortical circuits consist of different neuronal types and each carries out distinct functions. For example, cortical excitatory neurons can be further defined by their transcriptomics and anatomic connections, and distinct subpopulations route different information from a specific set of inputs to outputs (Economo et al., 2018; Tasic et al., 2018; Harris et al., 2019). The recent expansion of transgenic mouse lines to target specific subpopulations of excitatory, inhibitory, and modulatory neurons allows genetic targeting of these distinct subpopulations (Madisen et al., 2015; Daigle et al., 2018). By restricting the expression of activity indicators, the monitoring of cell type-specific macroscopic dynamics will dissect the role of different neuronal types and help researchers understand how different components cooperate in cortical circuits at the macroscale. It will also provide valuable datasets for the development of large-scale computational models with cell-type resolution.

Macroscopic dynamics of various neurotransmitters

The nervous system uses a large variety of neurotransmitters and modulators, each of which has unique functions. There has been a recent expansion of toolkits with genetically encoded indicators of various neurotransmitters (Lin and Schnitzer, 2016; Leopold et al., 2019; Dong et al., 2020; Jing et al., 2020; Ravotto et al., 2020; Sabatini and Tian, 2020; F. Sun et al., 2020; Wan et al., 2021; Wu et al., 2020). Wide-field imaging of indicators of specific neurotransmitters/modulators will allow direct tracking of how different molecular signaling is orchestrated at the macroscale. Several pioneering studies have started characterizing cortex-wide patterns of specific neurotransmitters/modulators in spontaneous brain activity (Xie et al., 2016; Lohani et al., 2020). Of particular interest in the future is how different neuromodulatory systems function at the macroscale, because they often project broadly to the cortex and have widespread impacts on behavior and cognition (Avery and Krichmar, 2017).

Expanding toolkits of novel genetically encoded indicators and miniaturized imaging devices

Genetically encoded indicators with enhanced brightness, sensitivity, stability, and faster kinetics will be fundamental to improving the SNR and temporal resolution of wide-field imaging in future studies. Some recently developed indicators for specific neurotransmitters hold promise for applications in in vivo wide-field imaging (Feng et al., 2019; Jing et al., 2020; Lohani et al., 2020; F. Sun et al., 2020). Improvements in voltage indicators could enable future wide-field voltage imaging to capture macroscopic dynamics at millisecond resolution with cell type specificity and longitudinal monitoring (Knöpfel and Song, 2019; Piatkevich et al., 2019; Pal and Tian, 2020). Furthermore, indicators targeting specific subcellular compartments (e.g., soma, axon) will help further determine the relative contributions of different sources in wide-field signals (Broussard et al., 2018; Y. Chen et al., 2020; Shemesh et al., 2020). Meanwhile, broadening the color spectrum of genetically encoded indicators could enable simultaneous imaging of different circuit components and investigations of their interactions (Inoue et al., 2019; Montagni et al., 2019). In addition, virtually all studies with wide-field imaging so far have been performed in head-fixed animals. Miniaturized devices for wide-field imaging in freely-moving animals would uncover large-scale neural dynamics in more naturalistic behavioral contexts (Scott et al., 2018; Adams et al., 2020; Rynes et al., 2021).

Activity manipulations with simultaneous wide-field imaging

Little is known about how large-scale cortical networks are influenced by individual brain regions and how they might be able to compensate for loss of individual regions. Such interactions between local and global networks can be probed by combining manipulations on certain brain regions or neuronal populations with simultaneous wide-field imaging of the cortex. This approach can dissect the functions of individual circuit components and their different contributions in various cognitive processes or at different developmental stages. For example, bilateral M2 inactivation with muscimol combined with simultaneous wide-field calcium imaging uncovered an indispensable role for M2 in orchestrating cortex-wide dynamics acquired with learning (Makino et al., 2017). Manipulating activity in specific areas will also help reveal how large-scale cortical circuits adapt to insults and neurologic disorders. Here a careful comparison between acute (e.g., optogenetics, pharmacogenetics, and pharmacology) and chronic (e.g., lesion) manipulation methods would be critical.

Analysis methods

The rich dataset obtained by wide-field imaging brings both challenges and unprecedented opportunities to gain insights into large-scale cortical dynamics. Sophisticated data-science tools, including dimensionality reduction techniques, will help extract latent and novel patterns from such high dimensional measurements of neural activity and facilitate data-driven discoveries (Williamson et al., 2019). Meanwhile, other analysis tools and computational approaches created for other large-scale recording techniques, such as fMRI and ECoG recordings, can be transferred to wide-field imaging for analyzing network properties (Bressler and Menon, 2010; Rubinov and Sporns, 2010; Pourahmadi and Noorbaloochi, 2016; Cohen et al., 2017). We predict an exponential growth in collaborations between experimental neuroscientists and data scientists to interpret these and other high-dimensional data. For example, creating data-guided circuit models that operate similarly to biological neural networks would provide insights for understanding large-scale cortical networks and in turn guide future experiments (Chaudhuri et al., 2015; Brunton and Beyeler, 2019; Mejias and Wang, 2019).

Conclusion

Wide-field calcium imaging enables large-scale, unbiased observation of many cortical regions with a sufficient spatiotemporal resolution to capture moment-by-moment features in macroscopic neural dynamics. This technique has started to reveal how cortex-wide dynamics support various cognitive processes, including sensorimotor integration, decision-making, and learning. In addition, combining wide-field calcium imaging with complementary recording modalities provides a novel platform to examine the relationship between local and global neural networks and to characterize the interactions between the cortex and subcortical regions. Although several technical considerations still exist, future applications of wide-field imaging together with rapidly growing data science tools will advance our understanding of how different cell types, neurotransmitters, and brain regions cooperate at the macroscale to give rise to perception and behavior.

Footnotes

  • This work was supported by National Institutes of Health Grants R01 NS091010, R01 EY025349, R01 DC014690, and P30 EY022589, National Science Foundation 1940202, and David & Lucile Packard Foundation to T.K.

  • The authors declare no competing financial interests.

  • Correspondence should be addressed to Takaki Komiyama at tkomiyama{at}ucsd.edu

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References

  1. ↵
    1. Ackman JB,
    2. Burbridge TJ,
    3. Crair MC
    (2012) Retinal waves coordinate patterned activity throughout the developing visual system. Nature 490:219–225. doi:10.1038/nature11529 pmid:23060192
    OpenUrlCrossRefPubMed
  2. ↵
    1. Adams J,
    2. Boominathan V,
    3. Gao S,
    4. Rodriguez A,
    5. Yan D,
    6. Kemere C,
    7. Veeraraghavan A,
    8. Robinson J
    (2020) In vivo fluorescence imaging with a flat, lensless microscope. bioRxiv 135236.
  3. ↵
    1. Akerboom J,
    2. Chen TW,
    3. Wardill TJ,
    4. Tian L,
    5. Marvin JS,
    6. Mutlu S,
    7. Calderón NC,
    8. Esposti F,
    9. Borghuis BG,
    10. Sun XR,
    11. Gordus A,
    12. Orger MB,
    13. Portugues R,
    14. Engert F,
    15. Macklin JJ,
    16. Filosa A,
    17. Aggarwal A,
    18. Kerr RA,
    19. Takagi R,
    20. Kracun S, et al
    . (2012) Optimization of a GCaMP calcium indicator for neural activity imaging. J Neurosci 32:13819–13840. doi:10.1523/JNEUROSCI.2601-12.2012 pmid:23035093
    OpenUrlAbstract/FREE Full Text
  4. ↵
    1. Allen WE,
    2. Kauvar IV,
    3. Chen MZ,
    4. Richman EB,
    5. Yang SJ,
    6. Chan K,
    7. Gradinaru V,
    8. Deverman BE,
    9. Luo L,
    10. Deisseroth K
    (2017) Global representations of goal-directed behavior in distinct cell types of mouse neocortex. Neuron 94:891–907.e6. doi:10.1016/j.neuron.2017.04.017 pmid:28521139
    OpenUrlCrossRefPubMed
  5. ↵
    1. Avery MC,
    2. Krichmar JL
    (2017) Neuromodulatory systems and their interactions: a review of models, theories, and experiments. Front Neural Circuits 11:108. doi:10.3389/fncir.2017.00108 pmid:29311844
    OpenUrlCrossRefPubMed
  6. ↵
    1. Barson D,
    2. Hamodi AS,
    3. Shen X,
    4. Lur G,
    5. Constable RT,
    6. Cardin JA,
    7. Crair MC,
    8. Higley MJ
    (2020) Simultaneous mesoscopic and two-photon imaging of neuronal activity in cortical circuits. Nat Methods 17:107–113. doi:10.1038/s41592-019-0625-2 pmid:31686040
    OpenUrlCrossRefPubMed
    1. Bauer AQ,
    2. Kraft AW,
    3. Wright PW,
    4. Snyder AZ,
    5. Lee JM,
    6. Culver JP
    (2014) Optical imaging of disrupted functional connectivity following ischemic stroke in mice. Neuroimage 99:388–401. doi:10.1016/j.neuroimage.2014.05.051 pmid:24862071
    OpenUrlCrossRefPubMed
  7. ↵
    1. Berwick J,
    2. Johnston D,
    3. Jones M,
    4. Martindale J,
    5. Redgrave P,
    6. McLoughlin N,
    7. Schiessl I,
    8. Mayhew JE
    (2005) Neurovascular coupling investigated with two-dimensional optical imaging spectroscopy in rat whisker barrel cortex. Eur J Neurosci 22:1655–1666. doi:10.1111/j.1460-9568.2005.04347.x pmid:16197506
    OpenUrlCrossRefPubMed
  8. ↵
    1. Blasdel GG,
    2. Salama G
    (1986) Voltage-sensitive dyes reveal a modular organization in monkey striate cortex. Nature 321:579–585. doi:10.1038/321579a0 pmid:3713842
    OpenUrlCrossRefPubMed
  9. ↵
    1. Bonhoeffer T,
    2. Grinvald A
    (1991) Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns. Nature 353:429–431. doi:10.1038/353429a0 pmid:1896085
    OpenUrlCrossRefPubMed
  10. ↵
    1. Bressler SL,
    2. Menon V
    (2010) Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn Sci 14:277–290. doi:10.1016/j.tics.2010.04.004 pmid:20493761
    OpenUrlCrossRefPubMed
  11. ↵
    1. Broussard GJ,
    2. Liang Y,
    3. Fridman M,
    4. Unger EK,
    5. Meng G,
    6. Xiao X,
    7. Ji N,
    8. Petreanu L,
    9. Tian L
    (2018) In vivo measurement of afferent activity with axon-specific calcium imaging. Nat Neurosci 21:1272–1280. doi:10.1038/s41593-018-0211-4 pmid:30127424
    OpenUrlCrossRefPubMed
  12. ↵
    1. Brunton BW,
    2. Beyeler M
    (2019) Data-driven models in human neuroscience and neuroengineering. Curr Opin Neurobiol 58:21–29. doi:10.1016/j.conb.2019.06.008 pmid:31325670
    OpenUrlCrossRefPubMed
  13. ↵
    1. Burbridge TJ,
    2. Xu HP,
    3. Ackman JB,
    4. Ge X,
    5. Zhang Y,
    6. Ye MJ,
    7. Zhou ZJ,
    8. Xu J,
    9. Contractor A,
    10. Crair MC
    (2014) Visual circuit development requires patterned activity mediated by retinal acetylcholine receptors. Neuron 84:1049–1064. doi:10.1016/j.neuron.2014.10.051 pmid:25466916
    OpenUrlCrossRefPubMed
  14. ↵
    1. Cardin JA,
    2. Crair MC,
    3. Higley MJ
    (2020) Mesoscopic imaging: shining a wide light on large-scale neural dynamics. Neuron 108:33–43. doi:10.1016/j.neuron.2020.09.031 pmid:33058764
    OpenUrlCrossRefPubMed
  15. ↵
    1. Chaudhuri R,
    2. Knoblauch K,
    3. Gariel MA,
    4. Kennedy H,
    5. Wang XJ
    (2015) A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex. Neuron 88:419–431. doi:10.1016/j.neuron.2015.09.008 pmid:26439530
    OpenUrlCrossRefPubMed
  16. ↵
    1. Chen TW,
    2. Wardill TJ,
    3. Sun Y,
    4. Pulver SR,
    5. Renninger SL,
    6. Baohan A,
    7. Schreiter ER,
    8. Kerr RA,
    9. Orger MB,
    10. Jayaraman V,
    11. Looger LL,
    12. Svoboda K,
    13. Kim DS
    (2013) Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499:295–300. doi:10.1038/nature12354 pmid:23868258
    OpenUrlCrossRefPubMed
  17. ↵
    1. Chen TW,
    2. Li N,
    3. Daie K,
    4. Svoboda K
    (2017) A map of anticipatory activity in mouse motor cortex. Neuron 94:866–879.e4. doi:10.1016/j.neuron.2017.05.005 pmid:28521137
    OpenUrlCrossRefPubMed
  18. ↵
    1. Chen Y,
    2. Jang H,
    3. Spratt PW,
    4. Kosar S,
    5. Taylor DE,
    6. Essner RA,
    7. Bai L,
    8. Leib DE,
    9. Kuo TW,
    10. Lin YC,
    11. Patel M,
    12. Subkhangulova A,
    13. Kato S,
    14. Feinberg EH,
    15. Bender KJ,
    16. Knight ZA,
    17. Garrison JL
    (2020) Soma-targeted imaging of neural circuits by ribosome tethering. Neuron 107:454–469.e6. doi:10.1016/j.neuron.2020.05.005 pmid:32574560
    OpenUrlCrossRefPubMed
  19. ↵
    1. Clancy KB,
    2. Orsolic I,
    3. Mrsic-Flogel TD
    (2019) Locomotion-dependent remapping of distributed cortical networks. Nat Neurosci 22:778–786. doi:10.1038/s41593-019-0357-8 pmid:30858604
    OpenUrlCrossRefPubMed
  20. ↵
    1. Cohen JD,
    2. Daw N,
    3. Engelhardt B,
    4. Hasson U,
    5. Li K,
    6. Niv Y,
    7. Norman KA,
    8. Pillow J,
    9. Ramadge PJ,
    10. Turk-Browne NB,
    11. Willke TL
    (2017) Computational approaches to fMRI analysis. Nat Neurosci 20:304–313. doi:10.1038/nn.4499 pmid:28230848
    OpenUrlCrossRefPubMed
  21. ↵
    1. Costa RM,
    2. Dana C,
    3. Nicolelis MA
    (2004) Differential corticostriatal plasticity during fast and slow motor skill learning in mice. Curr Biol 14:1124–1134. doi:10.1016/j.cub.2004.06.053 pmid:15242609
    OpenUrlCrossRefPubMed
  22. ↵
    1. Daigle TL,
    2. Madisen L,
    3. Hage TA,
    4. Valley MT,
    5. Knoblich U,
    6. Larsen RS,
    7. Takeno MM,
    8. Huang L,
    9. Gu H,
    10. Larsen R,
    11. Mills M,
    12. Bosma-Moody A,
    13. Siverts LA,
    14. Walker M,
    15. Graybuck LT,
    16. Yao Z,
    17. Fong O,
    18. Nguyen TN,
    19. Garren E,
    20. Lenz GH, et al
    . (2018) A suite of transgenic driver and reporter mouse lines with enhanced brain-cell-type targeting and functionality. Cell 174:465–480.e22. doi:10.1016/j.cell.2018.06.035 pmid:30007418
    OpenUrlCrossRefPubMed
  23. ↵
    1. Dana H,
    2. Sun Y,
    3. Mohar B,
    4. Hulse BK,
    5. Kerlin AM,
    6. Hasseman JP,
    7. Tsegaye G,
    8. Tsang A,
    9. Wong A,
    10. Patel R,
    11. Macklin JJ,
    12. Chen Y,
    13. Konnerth A,
    14. Jayaraman V,
    15. Looger LL,
    16. Schreiter ER,
    17. Svoboda K,
    18. Kim DS
    (2019) High-performance calcium sensors for imaging activity in neuronal populations and microcompartments. Nat Methods 16:649–657. doi:10.1038/s41592-019-0435-6 pmid:31209382
    OpenUrlCrossRefPubMed
  24. ↵
    1. Dong A,
    2. He K,
    3. Dudok B,
    4. Farrell JS,
    5. Guan W,
    6. Liput DJ,
    7. Puhl HL,
    8. Cai R,
    9. Duan J,
    10. Albarran E,
    11. Ding J,
    12. Lovinger DM,
    13. Li B,
    14. Soltesz I,
    15. Li Y
    (2020) A fluorescent sensor for spatiotemporally resolved endocannabinoid dynamics in vitro and in vivo. bioRxiv 329169.
  25. ↵
    1. Economo MN,
    2. Viswanathan S,
    3. Tasic B,
    4. Bas E,
    5. Winnubst J,
    6. Menon V,
    7. Graybuck LT,
    8. Nguyen TN,
    9. Smith KA,
    10. Yao Z,
    11. Wang L,
    12. Gerfen CR,
    13. Chandrashekar J,
    14. Zeng H,
    15. Looger LL,
    16. Svoboda K
    (2018) Distinct descending motor cortex pathways and their roles in movement. Nature 563:79–84. doi:10.1038/s41586-018-0642-9 pmid:30382200
    OpenUrlCrossRefPubMed
  26. ↵
    1. Feng J,
    2. Zhang C,
    3. Lischinsky JE,
    4. Jing M,
    5. Zhou J,
    6. Wang H,
    7. Zhang Y,
    8. Dong A,
    9. Wu Z,
    10. Wu H,
    11. Chen W,
    12. Zhang P,
    13. Zou J,
    14. Hires SA,
    15. Zhu JJ,
    16. Cui G,
    17. Lin D,
    18. Du J,
    19. Li Y
    (2019) A genetically encoded fluorescent sensor for rapid and specific in vivo detection of norepinephrine. Neuron 102:745–761.e8. doi:10.1016/j.neuron.2019.02.037 pmid:30922875
    OpenUrlCrossRefPubMed
  27. ↵
    1. Ferezou I,
    2. Haiss F,
    3. Gentet LJ,
    4. Aronoff R,
    5. Weber B,
    6. Petersen CC
    (2007) Spatiotemporal dynamics of cortical sensorimotor integration in behaving mice. Neuron 56:907–923. doi:10.1016/j.neuron.2007.10.007 pmid:18054865
    OpenUrlCrossRefPubMed
  28. ↵
    1. Frostig RD,
    2. Lieke EE,
    3. Ts'o DY,
    4. Grinvald A
    (1990) Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals. Proc Natl Acad Sci USA 87:6082–6086. doi:10.1073/pnas.87.16.6082 pmid:2117272
    OpenUrlAbstract/FREE Full Text
  29. ↵
    1. Garrett ME,
    2. Nauhaus I,
    3. Marshel JH,
    4. Callaway EM,
    5. Garrett ME,
    6. Marshel JH,
    7. Nauhaus I,
    8. Garrett ME
    (2014) Topography and areal organization of mouse visual cortex. J Neurosci 34:12587–12600. doi:10.1523/JNEUROSCI.1124-14.2014 pmid:25209296
    OpenUrlAbstract/FREE Full Text
  30. ↵
    1. Gilad A,
    2. Gallero-Salas Y,
    3. Groos D,
    4. Helmchen F
    (2018) Behavioral strategy determines frontal or posterior location of short-term memory in neocortex. Neuron 99:814–828.e7. doi:10.1016/j.neuron.2018.07.029 pmid:30100254
    OpenUrlCrossRefPubMed
  31. ↵
    1. Gilad A,
    2. Helmchen F
    (2020) Spatiotemporal refinement of signal flow through association cortex during learning. Nat Commun 11:1744. doi:10.1038/s41467-020-15534-z pmid:32269226
    OpenUrlCrossRefPubMed
  32. ↵
    1. Goard MJ,
    2. Pho GN,
    3. Woodson J,
    4. Sur M
    (2016) Distinct roles of visual, parietal, and frontal motor cortices in memory-guided sensorimotor decisions. Elife 5:e13764. doi:10.7554/eLife.13764
    OpenUrlCrossRefPubMed
  33. ↵
    1. Grewe BF,
    2. Gründemann J,
    3. Kitch LJ,
    4. Lecoq JA,
    5. Parker JG,
    6. Marshall JD,
    7. Larkin MC,
    8. Jercog PE,
    9. Grenier F,
    10. Li JZ,
    11. Lüthi A,
    12. Schnitzer MJ
    (2017) Neural ensemble dynamics underlying a long-term associative memory. Nature 543:670–675. doi:10.1038/nature21682 pmid:28329757
    OpenUrlCrossRefPubMed
  34. ↵
    1. Gribizis A,
    2. Ge X,
    3. Daigle TL,
    4. Ackman JB,
    5. Zeng H,
    6. Lee D,
    7. Crair MC
    (2019) Visual cortex gains independence from peripheral drive before eye opening. Neuron 104:711–723.e3. doi:10.1016/j.neuron.2019.08.015 pmid:31561919
    OpenUrlCrossRefPubMed
  35. ↵
    1. Grinvald A,
    2. Hildesheim R
    (2004) VSDI: a new era in functional imaging of cortical dynamics. Nat Rev Neurosci 5:874–885. doi:10.1038/nrn1536 pmid:15496865
    OpenUrlCrossRefPubMed
  36. ↵
    1. Grinvald A,
    2. Lieke E,
    3. Frostig RD,
    4. Gilbert CD,
    5. Wiesel TN
    (1986) Functional architecture of cortex revealed by optical imaging of intrinsic signals. Nature 324:361–363. doi:10.1038/324361a0 pmid:3785405
    OpenUrlCrossRefPubMed
  37. ↵
    1. Guo ZV,
    2. Li N,
    3. Huber D,
    4. Ophir E,
    5. Gutnisky D,
    6. Ting JT,
    7. Feng G,
    8. Svoboda K
    (2014) Flow of cortical activity underlying a tactile decision in mice. Neuron 81:179–194. doi:10.1016/j.neuron.2013.10.020 pmid:24361077
    OpenUrlCrossRefPubMed
  38. ↵
    1. Harris JA,
    2. Mihalas S,
    3. Hirokawa KE,
    4. Whitesell JD,
    5. Choi H,
    6. Bernard A,
    7. Bohn P,
    8. Caldejon S,
    9. Casal L,
    10. Cho A,
    11. Feiner A,
    12. Feng D,
    13. Gaudreault N,
    14. Gerfen CR,
    15. Graddis N,
    16. Groblewski PA,
    17. Henry AM,
    18. Ho A,
    19. Howard R,
    20. Knox JE, et al
    . (2019) Hierarchical organization of cortical and thalamic connectivity. Nature 575:195–202. doi:10.1038/s41586-019-1716-z pmid:31666704
    OpenUrlCrossRefPubMed
  39. ↵
    1. Hattori R,
    2. Danskin B,
    3. Babic Z,
    4. Mlynaryk N,
    5. Komiyama T
    (2019) Area-specificity and plasticity of history-dependent value coding during learning. Cell 177:1858–1872.e15. doi:10.1016/j.cell.2019.04.027 pmid:31080067
    OpenUrlCrossRefPubMed
  40. ↵
    1. Inoue M,
    2. Takeuchi A,
    3. Manita S,
    4. Horigane SI,
    5. Sakamoto M,
    6. Kawakami R,
    7. Yamaguchi K,
    8. Otomo K,
    9. Yokoyama H,
    10. Kim R,
    11. Yokoyama T,
    12. Takemoto-Kimura S,
    13. Abe M,
    14. Okamura M,
    15. Kondo Y,
    16. Quirin S,
    17. Ramakrishnan C,
    18. Imamura T,
    19. Sakimura K,
    20. Nemoto T, et al
    . (2019) Rational engineering of XCaMPs, a multicolor GECI suite for in vivo imaging of complex brain circuit dynamics. Cell 177:1346–1360.e24. doi:10.1016/j.cell.2019.04.007 pmid:31080068
    OpenUrlCrossRefPubMed
    1. Ji N,
    2. Freeman J,
    3. Smith SL
    (2016) Technologies for imaging neural activity in large volumes. Nat Neurosci 19:1154–1164. doi:10.1038/nn.4358 pmid:27571194
    OpenUrlCrossRefPubMed
  41. ↵
    1. Jing M,
    2. Li Y,
    3. Zeng J,
    4. Huang P,
    5. Skirzewski M,
    6. Kljakic O,
    7. Peng W,
    8. Qian T,
    9. Tan K,
    10. Zou J,
    11. Trinh S,
    12. Wu R,
    13. Zhang S,
    14. Pan S,
    15. Hires SA,
    16. Xu M,
    17. Li H,
    18. Saksida LM,
    19. Prado VF,
    20. Bussey TJ, et al
    . (2020) An optimized acetylcholine sensor for monitoring in vivo cholinergic activity. Nat Methods 17:1139–1146. doi:10.1038/s41592-020-0953-2 pmid:32989318
    OpenUrlCrossRefPubMed
    1. Jung WB,
    2. Shim HJ,
    3. Kim SG
    (2019) Mouse BOLD fMRI at ultrahigh field detects somatosensory networks including thalamic nuclei. Neuroimage 195:203–214. doi:10.1016/j.neuroimage.2019.03.063 pmid:30946950
    OpenUrlCrossRefPubMed
    1. Karimi Abadchi J,
    2. Nazari-Ahangarkolaee M,
    3. Gattas S,
    4. Bermudez-Contreras E,
    5. Luczak A,
    6. McNaughton BL,
    7. Mohajerani MH
    (2020) Spatiotemporal patterns of neocortical activity around hippocampal sharp-wave ripples. Elife 9:1–26. doi:10.7554/eLife.51972
    OpenUrlCrossRef
  42. ↵
    1. Knöpfel T,
    2. Song C
    (2019) Optical voltage imaging in neurons: moving from technology development to practical tool. Nat Rev Neurosci 20:719–727. doi:10.1038/s41583-019-0231-4 pmid:31705060
    OpenUrlCrossRefPubMed
    1. Kura S,
    2. Xie H,
    3. Fu B,
    4. Ayata C,
    5. Boas DA,
    6. Sakadžić S
    (2018) Intrinsic optical signal imaging of the blood volume changes is sufficient for mapping the resting state functional connectivity in the rodent cortex. J Neural Eng 15:035003. doi:10.1088/1741-2552/aaafe4 pmid:29451130
    OpenUrlCrossRefPubMed
  43. ↵
    1. Kyriakatos A,
    2. Sadashivaiah V,
    3. Zhang Y,
    4. Motta A,
    5. Auffret M,
    6. Petersen CC,
    7. Petersen C
    (2017) Voltage-sensitive dye imaging of mouse neocortex during a whisker detection task. Neurophotonics 4:031204. doi:10.1117/1.NPh.4.3.031204 pmid:27921068
    OpenUrlCrossRefPubMed
  44. ↵
    1. Lake EM,
    2. Ge X,
    3. Shen X,
    4. Herman P,
    5. Hyder F,
    6. Cardin JA,
    7. Higley MJ,
    8. Scheinost D,
    9. Papademetris X,
    10. Crair MC,
    11. Constable RT
    (2020) Simultaneous cortex-wide fluorescence Ca2+ imaging and whole-brain fMRI. Nat Methods 17:1262–1271. doi:10.1038/s41592-020-00984-6 pmid:33139894
    OpenUrlCrossRefPubMed
  45. ↵
    1. Leopold AV,
    2. Shcherbakova DM,
    3. Verkhusha VV
    (2019) Fluorescent biosensors for neurotransmission and neuromodulation: engineering and applications. Front Cell Neurosci 13:474.
    OpenUrlCrossRef
  46. ↵
    1. Li P,
    2. Geng X,
    3. Jiang H,
    4. Caccavano A,
    5. Vicini S,
    6. Wu JY
    (2019) Measuring sharp waves and oscillatory population activity with the genetically encoded calcium indicator GCaMP6f. Front Cell Neurosci 13:274.
    OpenUrl
  47. ↵
    1. Lin MZ,
    2. Schnitzer MJ
    (2016) Genetically encoded indicators of neuronal activity. Nat Neurosci 19:1142–1153. doi:10.1038/nn.4359 pmid:27571193
    OpenUrlCrossRefPubMed
  48. ↵
    1. Liu X,
    2. Ren C,
    3. Lu Y,
    4. Liu Y,
    5. Kim J,
    6. Leutgeb S,
    7. Komiyama T,
    8. Kuzum D
    (2021) Multimodal neural recordings with Neuro-FITM uncover diverse patterns of cortical-hippocampal interactions. Nat Neurosci. Advance online publication. Retrieved April 19, 20201. doi: 10.1038/s41593-021-00841-5.
    OpenUrlCrossRef
  49. ↵
    1. Lohani S,
    2. Moberly AH,
    3. Benisty H,
    4. Landa B,
    5. Jing M,
    6. Li Y,
    7. Higley MJ,
    8. Cardin JA
    (2020) Dual color mesoscopic imaging reveals spatiotemporally heterogeneous coordination of cholinergic and neocortical activity. bioRxiv 418632.
    1. Lu R,
    2. Liang Y,
    3. Meng G,
    4. Zhou P,
    5. Svoboda K,
    6. Paninski L,
    7. Ji N
    (2020) Rapid mesoscale volumetric imaging of neural activity with synaptic resolution. Nat Methods 17:291–294. doi:10.1038/s41592-020-0760-9 pmid:32123393
    OpenUrlCrossRefPubMed
  50. ↵
    1. Ma Y,
    2. Shaik MA,
    3. Kim SH,
    4. Kozberg MG,
    5. Thibodeaux DN,
    6. Zhao HT,
    7. Yu H,
    8. Hillman EMC
    (2016a) High-speed, wide-field optical mapping (WFOM) of neural activity and brain haemodynamics: considerations and novel approaches. Philos Trans R Soc Lond B Biol Sci 371:20150360. doi:10.1098/rstb.2015.0360
    OpenUrlCrossRefPubMed
  51. ↵
    1. Ma Y,
    2. Shaik MA,
    3. Kozberg MG,
    4. Kim SH,
    5. Portes JP,
    6. Timerman D,
    7. Hillman EMC
    (2016b) Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons. Proc Natl Acad Sci USA 113:E8463–E8471. doi:10.1073/pnas.1525369113
    OpenUrlAbstract/FREE Full Text
  52. ↵
    1. Madisen L,
    2. Garner AR,
    3. Shimaoka D,
    4. Chuong AS,
    5. Klapoetke NC,
    6. Li L,
    7. van der Bourg A,
    8. Niino Y,
    9. Egolf L,
    10. Monetti C,
    11. Gu H,
    12. Mills M,
    13. Cheng A,
    14. Tasic B,
    15. Nguyen TN,
    16. Sunkin SM,
    17. Benucci A,
    18. Nagy A,
    19. Miyawaki A,
    20. Helmchen F, et al
    . (2015) Transgenic mice for intersectional targeting of neural sensors and effectors with high specificity and performance. Neuron 85:942–958. doi:10.1016/j.neuron.2015.02.022 pmid:25741722
    OpenUrlCrossRefPubMed
  53. ↵
    1. Makino H,
    2. Komiyama T
    (2015) Learning enhances the relative impact of top-down processing in the visual cortex. Nat Neurosci 18:1116–1122. doi:10.1038/nn.4061 pmid:26167904
    OpenUrlCrossRefPubMed
  54. ↵
    1. Makino H,
    2. Ren C,
    3. Liu H,
    4. Kim AN,
    5. Kondapaneni N,
    6. Liu X,
    7. Kuzum D,
    8. Komiyama T
    (2017) Transformation of cortex-wide emergent properties during motor learning. Neuron 94:880–890.e8. doi:10.1016/j.neuron.2017.04.015 pmid:28521138
    OpenUrlCrossRefPubMed
  55. ↵
    1. Mateo C,
    2. Knutsen PM,
    3. Tsai PS,
    4. Shih AY,
    5. Kleinfeld D
    (2017) Entrainment of arteriole vasomotor fluctuations by neural activity is a basis of blood-oxygenation-level-dependent “resting-state” connectivity. Neuron 96:936–948.e3. doi:10.1016/j.neuron.2017.10.012 pmid:29107517
    OpenUrlCrossRefPubMed
  56. ↵
    1. Matyas F,
    2. Sreenivasan V,
    3. Marbach F,
    4. Wacongne C,
    5. Barsy B,
    6. Mateo C,
    7. Aronoff R,
    8. Petersen CC
    (2010) Motor control by sensory cortex. Science 330:1240–1244. doi:10.1126/science.1195797 pmid:21109671
    OpenUrlAbstract/FREE Full Text
  57. ↵
    1. Mejias J,
    2. Wang XJ
    (2019) Mechanisms of distributed working memory in a large-scale model of macaque neocortex. bioRxiv 760231.
  58. ↵
    1. Mohajerani MH,
    2. McVea DA,
    3. Fingas M,
    4. Murphy TH
    (2010) Mirrored bilateral slow-wave cortical activity within local circuits revealed by fast bihemispheric voltage-sensitive dye imaging in anesthetized and awake mice. J Neurosci 30:3745–3751. doi:10.1523/JNEUROSCI.6437-09.2010 pmid:20220008
    OpenUrlAbstract/FREE Full Text
  59. ↵
    1. Montagni E,
    2. Resta F,
    3. Conti E,
    4. Scaglione A,
    5. Pasquini M,
    6. Micera S,
    7. Mascaro AL,
    8. Pavone FS
    (2019) Wide-field imaging of cortical neuronal activity with red-shifted functional indicators during motor task execution. J Phys D 52:074001. doi:10.1088/1361-6463/aaf26c
    OpenUrlCrossRef
  60. ↵
    1. Musall S,
    2. Kaufman MT,
    3. Juavinett AL,
    4. Gluf S,
    5. Churchland AK
    (2019) Single-trial neural dynamics are dominated by richly varied movements. Nat Neurosci 22:1677–1686. doi:10.1038/s41593-019-0502-4 pmid:31551604
    OpenUrlCrossRefPubMed
  61. ↵
    1. Niethard N,
    2. Hasegawa M,
    3. Itokazu T,
    4. Oyanedel CN,
    5. Born J,
    6. Sato TR
    (2016) Sleep-stage-specific regulation of cortical excitation and inhibition. Curr Biol 26:2739–2749. doi:10.1016/j.cub.2016.08.035 pmid:27693142
    OpenUrlCrossRefPubMed
    1. Nöbauer T,
    2. Skocek O,
    3. Pernía-Andrade AJ,
    4. Weilguny L,
    5. Martínez Traub F,
    6. Molodtsov MI,
    7. Vaziri A
    (2017) Video rate volumetric Ca2+ imaging across cortex using seeded iterative demixing (SID) microscopy. Nat Methods 14:811–818. doi:10.1038/nmeth.4341 pmid:28650477
    OpenUrlCrossRefPubMed
  62. ↵
    1. Oh SW,
    2. Harris JA,
    3. Ng L,
    4. Winslow B,
    5. Cain N,
    6. Mihalas S,
    7. Wang Q,
    8. Lau C,
    9. Kuan L,
    10. Henry AM,
    11. Mortrud MT,
    12. Ouellette B,
    13. Nguyen TN,
    14. Sorensen SA,
    15. Slaughterbeck CR,
    16. Wakeman W,
    17. Li Y,
    18. Feng D,
    19. Ho A,
    20. Nicholas E, et al
    . (2014) A mesoscale connectome of the mouse brain. Nature 508:207–214. doi:10.1038/nature13186 pmid:24695228
    OpenUrlCrossRefPubMed
  63. ↵
    1. Orbach HS,
    2. Cohen LB,
    3. Grinvald A
    (1985) Optical mapping of electrical activity in rat somatosensory and visual cortex. J Neurosci 5:1886–1895. pmid:4020423
    OpenUrlAbstract/FREE Full Text
    1. Ota K
    (2020) Fast scanning high optical invariant two-photon microscopy for monitoring a large neural network activity with cellular resolution. bioRxiv 201699.
  64. ↵
    1. Pal A,
    2. Tian L
    (2020) Imaging voltage and brain chemistry with genetically encoded sensors and modulators. Curr Opin Chem Biol 57:166–176. doi:10.1016/j.cbpa.2020.07.006 pmid:32823064
    OpenUrlCrossRefPubMed
  65. ↵
    1. Peters AJ,
    2. Chen SX,
    3. Komiyama T
    (2014) Emergence of reproducible spatiotemporal activity during motor learning. Nature 510:263–267. doi:10.1038/nature13235 pmid:24805237
    OpenUrlCrossRefPubMed
  66. ↵
    1. Peters AJ,
    2. Fabre JM,
    3. Steinmetz NA,
    4. Harris KD,
    5. Carandini M
    (2021) Striatal activity topographically reflects cortical activity. Nature 591:420–425. doi:10.1038/s41586-020-03166-8 pmid:33473213
    OpenUrlCrossRefPubMed
  67. ↵
    1. Piatkevich KD,
    2. Bensussen S,
    3. Tseng HA,
    4. Shroff SN,
    5. Lopez-Huerta VG,
    6. Park D,
    7. Jung EE,
    8. Shemesh OA,
    9. Straub C,
    10. Gritton HJ,
    11. Romano MF,
    12. Costa E,
    13. Sabatini BL,
    14. Fu Z,
    15. Boyden ES,
    16. Han X
    (2019) Population imaging of neural activity in awake behaving mice. Nature 574:413–417. doi:10.1038/s41586-019-1641-1 pmid:31597963
    OpenUrlCrossRefPubMed
  68. ↵
    1. Pinto L,
    2. Rajan K,
    3. DePasquale B,
    4. Thiberge SY,
    5. Tank DW,
    6. Brody CD
    (2019) Task-dependent changes in the large-scale dynamics and necessity of cortical regions. Neuron 104:810–824.e9. doi:10.1016/j.neuron.2019.08.025 pmid:31564591
    OpenUrlCrossRefPubMed
  69. ↵
    1. Pourahmadi M,
    2. Noorbaloochi S
    (2016) Multivariate time series analysis of neuroscience data: some challenges and opportunities. Curr Opin Neurobiol 37:12–15. doi:10.1016/j.conb.2015.12.006 pmid:26752736
    OpenUrlCrossRefPubMed
  70. ↵
    1. Prechtl JC,
    2. Cohen LB,
    3. Pesaran B,
    4. Mitra PP,
    5. Kleinfeld D
    (1997) Visual stimuli induce waves of electrical activity in turtle cortex. Proc Natl Acad Sci USA 94:7621–7626. doi:10.1073/pnas.94.14.7621
    OpenUrlAbstract/FREE Full Text
  71. ↵
    1. Ravotto L,
    2. Duffet L,
    3. Zhou X,
    4. Weber B,
    5. Patriarchi T
    (2020) A bright and colorful future for G-protein coupled receptor sensors. Front Cell Neurosci 14:1–9.
    OpenUrl
  72. ↵
    1. Rubinov M,
    2. Sporns O
    (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069. doi:10.1016/j.neuroimage.2009.10.003 pmid:19819337
    OpenUrlCrossRefPubMed
  73. ↵
    1. Rynes M,
    2. Surinach D,
    3. Laroque M,
    4. Linn S,
    5. Dominguez J,
    6. Ghanbari L,
    7. Kodandaramaiah S
    (2021) Miniaturized device for whole cortex mesoscale imaging in freely behaving mice. Nat Methods. Advance online publication. Retrieved April 5, 2021. doi: 10.1038/s41592-021-01104-8. doi:10.1117/12.2550395
    OpenUrlCrossRef
  74. ↵
    1. Sabatini BL,
    2. Tian L
    (2020) Imaging neurotransmitter and neuromodulator dynamics in vivo with genetically encoded indicators. Neuron 108:17–32. doi:10.1016/j.neuron.2020.09.036 pmid:33058762
    OpenUrlCrossRefPubMed
  75. ↵
    1. Salkoff DB,
    2. Zagha E,
    3. McCarthy E,
    4. McCormick DA
    (2020) Movement and performance explain widespread cortical activity in a visual detection task. Cereb Cortex 30:421–437. doi:10.1093/cercor/bhz206 pmid:31711133
    OpenUrlCrossRefPubMed
  76. ↵
    1. Saxena S,
    2. Kinsella I,
    3. Musall S,
    4. Kim SH,
    5. Meszaros J,
    6. Thibodeaux DN,
    7. Kim C,
    8. Cunningham J,
    9. Hillman EM,
    10. Churchland A,
    11. Paninski L
    (2020) Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data. PLoS Comput Biol 16:e1007791. doi:10.1371/journal.pcbi.1007791 pmid:32282806
    OpenUrlCrossRefPubMed
    1. Schlegel F,
    2. Sych Y,
    3. Schroeter A,
    4. Stobart J,
    5. Weber B,
    6. Helmchen F,
    7. Rudin M
    (2018) Fiber-optic implant for simultaneous fluorescence-based calcium recordings and BOLD fMRI in mice. Nat Protoc 13:840–855. doi:10.1038/nprot.2018.003 pmid:29599439
    OpenUrlCrossRefPubMed
    1. Schwalm M,
    2. Schmid F,
    3. Wachsmuth L,
    4. Backhaus H,
    5. Kronfeld A,
    6. Jury FA,
    7. Prouvot PH,
    8. Fois C,
    9. Albers F,
    10. Van Alst T,
    11. Faber C,
    12. Stroh A
    (2017) Cortex-wide BOLD fMRI activity reflects locally-recorded slow oscillation-associated calcium waves. Elife 6:e27602. doi:10.7554/eLife.27602
    OpenUrlCrossRefPubMed
  77. ↵
    1. Scott BB,
    2. Thiberge SY,
    3. Guo C,
    4. Tervo DG,
    5. Brody CD,
    6. Karpova AY,
    7. Tank DW
    (2018) Imaging cortical dynamics in GCaMP transgenic rats with a head-mounted widefield macroscope. Neuron 100:1045–1058.e5. doi:10.1016/j.neuron.2018.09.050 pmid:30482694
    OpenUrlCrossRefPubMed
  78. ↵
    1. Shemesh OA,
    2. Linghu C,
    3. Piatkevich KD,
    4. Goodwin D,
    5. Celiker OT,
    6. Gritton HJ,
    7. Romano MF,
    8. Gao R,
    9. Yu CC,
    10. Tseng HA,
    11. Bensussen S,
    12. Narayan S,
    13. Yang CT,
    14. Freifeld L,
    15. Siciliano CA,
    16. Gupta I,
    17. Wang J,
    18. Pak N,
    19. Yoon YG,
    20. Ullmann JF, et al
    . (2020) Precision calcium imaging of dense neural populations via a cell-body-targeted calcium indicator. Neuron 107:470–486.e11. doi:10.1016/j.neuron.2020.05.029 pmid:32592656
    OpenUrlCrossRefPubMed
  79. ↵
    1. Shimaoka D,
    2. Steinmetz NA,
    3. Harris KD,
    4. Carandini M
    (2019) The impact of bilateral ongoing activity on evoked responses in mouse cortex. Elife 8:e43533. doi:10.7554/eLife.43533
    OpenUrlCrossRefPubMed
    1. Sofroniew NJ,
    2. Flickinger D,
    3. King J,
    4. Svoboda K
    (2016) A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging. Elife 5:e14472. doi:10.7554/eLife.14472
    OpenUrlCrossRefPubMed
    1. Song A,
    2. Charles AS,
    3. Koay SA,
    4. Gauthier JL,
    5. Thiberge SY,
    6. Pillow JW,
    7. Tank DW
    (2017) Volumetric two-photon imaging of neurons using stereoscopy (vTwINS). Nat Methods 14:420–426. doi:10.1038/nmeth.4226 pmid:28319111
    OpenUrlCrossRefPubMed
  80. ↵
    1. Sreenivasan V,
    2. Kyriakatos A,
    3. Mateo C,
    4. Jaeger D,
    5. Petersen CC
    (2017) Parallel pathways from whisker and visual sensory cortices to distinct frontal regions of mouse neocortex. Neurophotonics 4:031203. doi:10.1117/1.NPh.4.3.031203 pmid:27921067
    OpenUrlCrossRefPubMed
  81. ↵
    1. Steinmetz NA,
    2. Zatka-Haas P,
    3. Carandini M,
    4. Harris KD
    (2019) Distributed coding of choice, action and engagement across the mouse brain. Nature 576:266–273. doi:10.1038/s41586-019-1787-x pmid:31776518
    OpenUrlCrossRefPubMed
  82. ↵
    1. Stern M,
    2. Shea-Brown E,
    3. Witten D
    (2020) Inferring the spiking rate of a population of neurons from wide-field calcium imaging. bioRxiv 930040.
    1. Stirman JN,
    2. Smith IT,
    3. Kudenov MW,
    4. Smith SL
    (2016) Wide field-of-view, multi-region two-photon imaging of neuronal activity. Nat Biotechnol 34:857–870. doi:10.1038/nbt.3594
    OpenUrlCrossRefPubMed
  83. ↵
    1. Stringer C,
    2. Pachitariu M,
    3. Steinmetz N,
    4. Reddy CB,
    5. Carandini M,
    6. Harris KD
    (2019) Spontaneous behaviors drive multidimensional, brainwide activity. Science 364:255. doi:10.1126/science.aav7893 pmid:31000656
    OpenUrlCrossRefPubMed
  84. ↵
    1. Sun F,
    2. Zhou J,
    3. Dai B,
    4. Qian T,
    5. Zeng J,
    6. Li X,
    7. Zhuo Y,
    8. Zhang Y,
    9. Wang Y,
    10. Qian C,
    11. Tan K,
    12. Feng J,
    13. Dong H,
    14. Lin D,
    15. Cui G,
    16. Li Y
    (2020) Next-generation GRAB sensors for monitoring dopaminergic activity in vivo. Nat Methods 17:1156–1166. doi:10.1038/s41592-020-00981-9 pmid:33087905
    OpenUrlCrossRefPubMed
  85. ↵
    1. Sun XR,
    2. Badura A,
    3. Pacheco DA,
    4. Lynch LA,
    5. Schneider ER,
    6. Taylor MP,
    7. Hogue IB,
    8. Enquist LW,
    9. Murthy M,
    10. Wang SS
    (2013) Fast GCaMPs for improved tracking of neuronal activity. Nat Commun 4:2170. doi:10.1038/ncomms3170 pmid:23863808
    OpenUrlCrossRefPubMed
  86. ↵
    1. Tasic B,
    2. Yao Z,
    3. Graybuck LT,
    4. Smith KA,
    5. Nguyen TN,
    6. Bertagnolli D,
    7. Goldy J,
    8. Garren E,
    9. Economo MN,
    10. Viswanathan S,
    11. Penn O,
    12. Bakken T,
    13. Menon V,
    14. Miller J,
    15. Fong O,
    16. Hirokawa KE,
    17. Lathia K,
    18. Rimorin C,
    19. Tieu M,
    20. Larsen R, et al
    . (2018) Shared and distinct transcriptomic cell types across neocortical areas. Nature 563:72–78. doi:10.1038/s41586-018-0654-5 pmid:30382198
    OpenUrlCrossRefPubMed
  87. ↵
    1. Tian L,
    2. Hires SA,
    3. Mao T,
    4. Huber D,
    5. Chiappe ME,
    6. Chalasani SH,
    7. Petreanu L,
    8. Akerboom J,
    9. McKinney SA,
    10. Schreiter ER,
    11. Bargmann CI,
    12. Jayaraman V,
    13. Svoboda K,
    14. Looger LL
    (2009) Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat Methods 6:875–881. doi:10.1038/nmeth.1398 pmid:19898485
    OpenUrlCrossRefPubMed
  88. ↵
    1. Valley MT,
    2. Moore MG,
    3. Zhuang J,
    4. Mesa N,
    5. Castelli D,
    6. Sullivan D,
    7. Reimers M,
    8. Waters J
    (2020) Separation of hemodynamic signals from GCaMP fluorescence measured with wide-field imaging. J Neurophysiol 123:356–366. doi:10.1152/jn.00304.2019 pmid:31747332
    OpenUrlCrossRefPubMed
  89. ↵
    1. Vanni MP,
    2. Murphy TH
    (2014) Mesoscale transcranial spontaneous activity mapping in GCaMP3 transgenic mice reveals extensive reciprocal connections between areas of somatomotor cortex. J Neurosci 34:15931–15946. doi:10.1523/JNEUROSCI.1818-14.2014 pmid:25429135
    OpenUrlAbstract/FREE Full Text
  90. ↵
    1. Wan J,
    2. Peng W,
    3. Li X,
    4. Qian T,
    5. Song K,
    6. Zeng J,
    7. Deng F,
    8. Hao S,
    9. Feng J,
    10. Zhang P,
    11. Zhang Y,
    12. Zou J,
    13. Pan S,
    14. Zhu JJ,
    15. Jing M,
    16. Xu M,
    17. Li Y
    (2021) A genetically encoded GRAB sensor for measuring serotonin dynamics in vivo. Nat Neurosci. Advance online publication. Retrieved April 5, 2021. doi: 10.1038/s41593-021-00823-7.
    OpenUrlCrossRef
  91. ↵
    1. Wang Q,
    2. Ding SL,
    3. Li Y,
    4. Royall J,
    5. Feng D,
    6. Lesnar P,
    7. Graddis N,
    8. Naeemi M,
    9. Facer B,
    10. Ho A,
    11. Dolbeare T,
    12. Blanchard B,
    13. Dee N,
    14. Wakeman W,
    15. Hirokawa KE,
    16. Szafer A,
    17. Sunkin SM,
    18. Oh SW,
    19. Bernard A,
    20. Phillips JW, et al
    . (2020) The Allen mouse brain common coordinate framework: a 3D reference atlas. Cell 181:936–953.e20. doi:10.1016/j.cell.2020.04.007 pmid:32386544
    OpenUrlCrossRefPubMed
    1. Weisenburger S,
    2. Tejera F,
    3. Demas J,
    4. Chen B,
    5. Manley J,
    6. Sparks FT,
    7. Martínez Traub F,
    8. Daigle T,
    9. Zeng H,
    10. Losonczy A,
    11. Vaziri A
    (2019) Volumetric Ca2+ imaging in the mouse brain using hybrid multiplexed sculpted light microscopy. Cell 177:1050–1066.e14. doi:10.1016/j.cell.2019.03.011 pmid:30982596
    OpenUrlCrossRefPubMed
  92. ↵
    1. Wekselblatt JB,
    2. Flister ED,
    3. Piscopo DM,
    4. Niell CM
    (2016) Large-scale imaging of cortical dynamics during sensory perception and behavior. J Neurophysiol 115:2852–2866. doi:10.1152/jn.01056.2015 pmid:26912600
    OpenUrlCrossRefPubMed
  93. ↵
    1. Williamson RC,
    2. Doiron B,
    3. Smith MA,
    4. Yu BM
    (2019) Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction. Curr Opin Neurobiol 55:40–47. doi:10.1016/j.conb.2018.12.009 pmid:30677702
    OpenUrlCrossRefPubMed
  94. ↵
    1. Wu Z,
    2. Cui Y,
    3. Wang H,
    4. Song K,
    5. Yuan Z,
    6. Dong A,
    7. Wu H,
    8. Wan Y,
    9. Pan S,
    10. Peng W,
    11. Jing M,
    12. Xu M,
    13. Luo M,
    14. Li Y
    (2020) A GRAB sensor reveals activity-dependent non-vesicular somatodendritic adenosine release. bioRxiv 075564.
  95. ↵
    1. Xiao D,
    2. Vanni MP,
    3. Mitelut CC,
    4. Chan AW,
    5. LeDue JM,
    6. Xie Y,
    7. Chen AC,
    8. Swindale NV,
    9. Murphy TH
    (2017) Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons. Elife 6:e19976. doi:10.7554/eLife.19976
    OpenUrlCrossRefPubMed
  96. ↵
    1. Xie Y,
    2. Chan AW,
    3. McGirr A,
    4. Xue S,
    5. Xiao D,
    6. Zeng H,
    7. Murphy TH
    (2016) Resolution of high-frequency mesoscale intracortical maps using the genetically encoded glutamate sensor iGluSnFR. J Neurosci 36:1261–1272. doi:10.1523/JNEUROSCI.2744-15.2016 pmid:26818514
    OpenUrlAbstract/FREE Full Text
  97. ↵
    1. Yang W,
    2. Carrillo-Reid L,
    3. Bando Y,
    4. Peterka DS,
    5. Yuste R
    (2018) Simultaneous two-photon imaging and two-photon optogenetics of cortical circuits in three dimensions. Elife 7:e32671. doi:10.7554/eLife.32671
    OpenUrlCrossRefPubMed
    1. Yang Y,
    2. Liu N,
    3. He Y,
    4. Liu Y,
    5. Ge L,
    6. Zou L,
    7. Song S,
    8. Xiong W,
    9. Liu X
    (2018) Improved calcium sensor GCaMP-X overcomes the calcium channel perturbations induced by the calmodulin in GCaMP. Nat Commun 9:1504. doi:10.1038/s41467-018-03719-6 pmid:29666364
    OpenUrlCrossRefPubMed
  98. ↵
    1. Yizhar O,
    2. Fenno LE,
    3. Davidson TJ,
    4. Mogri M,
    5. Deisseroth K
    (2011) Optogenetics in neural systems. Neuron 71:9–34. doi:10.1016/j.neuron.2011.06.004 pmid:21745635
    OpenUrlCrossRefPubMed
    1. Yu CH,
    2. Stirman JN,
    3. Yu Y,
    4. Hira R,
    5. Smith SL
    (2020) Diesel2p mesoscope with dual independent scan engines for flexible capture of dynamics in distributed neural circuitry. bioRxiv 305508.
  99. ↵
    1. Zatka-Haas P,
    2. Steinmetz NA,
    3. Carandini M,
    4. Harris KD
    (2020) A perceptual decision requires sensory but not action coding in mouse cortex. bioRxiv 501627.
  100. ↵
    1. Zhuang J,
    2. Ng L,
    3. Williams D,
    4. Valley M,
    5. Li Y,
    6. Garrett M,
    7. Waters J
    (2017) An extended retinotopic map of mouse cortex. Elife 6:e18372. doi:10.7554/eLife.18372
    OpenUrlCrossRefPubMed
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The Journal of Neuroscience: 41 (19)
Journal of Neuroscience
Vol. 41, Issue 19
12 May 2021
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Characterizing Cortex-Wide Dynamics with Wide-Field Calcium Imaging
Chi Ren, Takaki Komiyama
Journal of Neuroscience 12 May 2021, 41 (19) 4160-4168; DOI: 10.1523/JNEUROSCI.3003-20.2021

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Characterizing Cortex-Wide Dynamics with Wide-Field Calcium Imaging
Chi Ren, Takaki Komiyama
Journal of Neuroscience 12 May 2021, 41 (19) 4160-4168; DOI: 10.1523/JNEUROSCI.3003-20.2021
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Keywords

  • decision making
  • large-scale cortical dynamics
  • learning
  • multimodal recordings
  • sensorimotor integration
  • wide-field calcium imaging

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