NWB files for a novel Neuropixels electrophysiology dataset from transgenic mice expressing GCaMP6f, as well as additional wild type mice. This dataset was used in a comparison of electrophysiology and two-photon imaging data, which appears in Siegle, Ledochowitsch et al. (2021) eLife. For information about experimental procedures, see Siegle, Jia et al. (2021) Nature 592, 86-92 (https://www.nature.com/articles/s41586-020-03171-x). For information about file contents, see https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html Files were generated with a custom branch of the AllenSDK, available at https://github.com/jsiegle/allensdk/tree/ophys-ephys. We recommend using the same branch to interact with these files.
NWB files for a novel Neuropixels electrophysiology dataset from transgenic mice expressing GCaMP6f, as well as additional wild type mice. This dataset was used in a comparison of electrophysiology and two-photon imaging data, which appears in Siegle, Ledochowitsch et al. (2021) eLife. For information about experimental procedures, see Siegle, Jia et al. (2021) Nature 592, 86-92 (https://www.nature.com/articles/s41586-020-03171-x). For information about file contents, see https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html Files were generated with a custom branch of the AllenSDK, available at https://github.com/jsiegle/allensdk/tree/ophys-ephys. We recommend using the same branch to interact with these files.
Abstract In this paper, we propose a shifted ITO electrode structure of a LC barrier for additional sweet spots in the auto‐stereoscopic 3D display. The shifted ITO electrode is consisted of vertically inter‐digital ITO electrodes both bottom and top electrodes. They are assembled with horizontally shifted by a half pitch. We can drive each electrode according to the viewer position. It gives an effect of varying the viewing zone. By doing this, we can widen the viewing zone with head tracking technique. It can give a viewer some freedom of viewing position with an image of high quality.
Article Figures and data Abstract Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Extracellular electrophysiology and two-photon calcium imaging are widely used methods for measuring physiological activity with single-cell resolution across large populations of cortical neurons. While each of these two modalities has distinct advantages and disadvantages, neither provides complete, unbiased information about the underlying neural population. Here, we compare evoked responses in visual cortex recorded in awake mice under highly standardized conditions using either imaging of genetically expressed GCaMP6f or electrophysiology with silicon probes. Across all stimulus conditions tested, we observe a larger fraction of responsive neurons in electrophysiology and higher stimulus selectivity in calcium imaging, which was partially reconciled by applying a spikes-to-calcium forward model to the electrophysiology data. However, the forward model could only reconcile differences in responsiveness when restricted to neurons with low contamination and an event rate above a minimum threshold. This work established how the biases of these two modalities impact functional metrics that are fundamental for characterizing sensory-evoked responses. Introduction Systems neuroscience aims to explain how complex adaptive behaviors can arise from the interactions of many individual neurons. As a result, population recordings—which capture the activity of multiple neurons simultaneously—have become the foundational method for progress in this domain. Extracellular electrophysiology and calcium-dependent two-photon optical physiology are by far the most prevalent population recording techniques, due to their single-neuron resolution, ease of use, and scalability. Recent advances have made it possible to record simultaneously from thousands of neurons with electrophysiology (Jun et al., 2017; Siegle et al., 2021; Stringer et al., 2019a) or tens of thousands of neurons with calcium imaging (Sofroniew et al., 2016; Stringer et al., 2019b; Weisenburger et al., 2019). While insights gained from both methods have been invaluable to the field, it is clear that neither technique provides a completely faithful picture of the underlying neural activity. In this study, our goal is to better understand the inherent biases of each recording modality, and specifically how to appropriately compare results obtained with one method to those obtained with the other. Head-to-head comparisons of electrophysiology and imaging data are rare in the literature, but are critically important as the practical aspects of each method affect their suitability for different experimental questions. Since the expression of calcium indicators can be restricted to genetically defined cell types, imaging can easily target recordings to specific sub-populations (Madisen et al., 2015). Similarly, the use of retro- or anterograde viral transfections to drive indicator expression allows imaging to target sub-populations defined by their projection patterns (Glickfeld et al., 2013; Gradinaru et al., 2010). The ability to identify genetically or projection-defined cell populations in electrophysiology experiments is far more limited (Economo et al., 2018; Jia et al., 2019; Lima et al., 2009). Both techniques have been adapted for chronic recordings, but imaging offers the ability to reliably return to the same neurons over many days without the need to implant bulky hardware (Peters et al., 2014). Furthermore, because imaging captures structural, in addition to functional, data, individual neurons can be precisely registered to tissue volumes from electron microscopy (Bock et al., 2011; Lee et al., 2016), in vitro brain slices (Ko et al., 2011), and potentially other ex vivo techniques such as in situ RNA profiling (Chen et al., 2015). In contrast, the sources of extracellular spike waveforms are very difficult to localize with sufficient precision to enable direct cross-modal registration. Inherent differences in the spatial sampling properties of electrophysiology and imaging are widely recognized, and influence what information can be gained from each method (Figure 1A). Multi-photon imaging typically yields data in a single plane tangential to the cortical surface, and is limited to depths of <1 mm due to a combination of light scattering and absorption in tissue. While multi-plane (Yang et al., 2016) and deep structure (Ouzounov et al., 2017) imaging are both areas of active research, imaging of most subcortical structures requires physical destruction of more superficial tissues (Dombeck et al., 2010; Feinberg and Meister, 2015; Skocek et al., 2018). Extracellular electrophysiology, on the other hand, utilizes microelectrodes embedded in the tissue, and thus dense recordings are easiest to perform along a straight line, normal to the cortical surface, in order to minimize per-channel tissue displacement. Linear probes provide simultaneous access to neurons in both cortex and subcortical structures, but make it difficult to sample many neurons from the same cortical layer. Figure 1 Download asset Open asset Overview of the ephys and imaging datasets. (A) Illustration of the orthogonal spatial sampling profiles of the two modalities. Black and white area represents a typical imaging plane (at approximately 250 µm below the brain surface), while tan circles represent the inferred locations of cortical neurons recorded with a Neuropixels probe (area is proportional to overall amplitude). (B) Comparison of temporal dynamics between the modalities. Top: a heatmap of ΔF/F values for 100 neurons simultaneously imaged in V1 during the presentation of a 30 s movie clip. Bottom: raster plot for 100 neurons simultaneously recorded with a Neuropixels probe in V1 in a different mouse viewing the same movie. Inset: Close-up of one sample of the imaging heatmap, plotted on the same timescale as 990 samples from 15 electrodes recorded during the equivalent interval from the ephys experiment. (C) Steps in the two data generation pipelines. Following habituation, mice proceed to either two-photon imaging or Neuropixels electrophysiology. (D) Side-by-side comparison of the rigs used for ephys (left) and imaging (right). (E) Schematic of the stimulus set used for both modalities. The ephys stimuli are shown continuously for a single session, while the imaging stimuli are shown over the course of three separate sessions. (F) Histogram of neurons recorded in each area and layer, grouped by mouse genotype. The temporal resolutions of these two methodologies also differ in critical ways (Figure 1B). Imaging is limited by the dwell time required to capture enough photons to distinguish physiological changes in fluorescence from noise (Svoboda and Yasuda, 2006), and the kinetics of calcium-dependent indicators additionally constrain the ability to temporally localize neural activity (Chen et al., 2013). While kilohertz-scale imaging has been achieved (Kazemipour et al., 2019; Zhang et al., 2019), most studies are based on data sampled at frame rates between 1 and 30 Hz. In contrast, extracellular electrophysiology requires sampling rates of 20 kHz or higher, in order to capture the action potential waveform shape that is essential for accurate spike sorting. High sampling rates allow extracellular electrophysiology to pin-point neural activity in time with sub-millisecond resolution, enabling analyses of fine-timescale synchronization across simultaneously recorded neural populations. The fact that electrophysiology can measure action potentials—what we believe to be the fundamental currency of neuronal communication and causation—bestows upon it a more basic ontological status than on calcium imaging, which captures an indirect measure of a neuron's spike train. To date, there has been no comprehensive attempt to characterize how the choice of recording modality affects the inferred functional properties of neurons in sensory cortex. Our limited understanding of how scientific conclusions may be skewed by the recording modality represents the weakest link in the chain of information integration across the techniques available to neurophysiologists today. To address this, we took advantage of two recently collected large-scale datasets that sampled neural activity in mouse visual cortex using either two-photon calcium imaging (de Vries et al., 2020) or dense extracellular electrophysiology (Siegle et al., 2021). These datasets were collected using standardized pipelines, such that the surgical methods, experimental steps, and physical geometry of the recording rigs were matched as closely as possible (Figure 1C,D). The overall similarity of these Allen Brain Observatory pipelines eliminates many of the potential confounding factors that arise when comparing results from imaging and electrophysiology experiments. We note that this is not an attempt at calibration against ground truth data, but rather an attempt to reconcile results across two uniquely comprehensive datasets collected under highly standardized conditions. Our comparison focused on metrics that capture three fundamental features of neural responses to environmental stimuli: (1) responsiveness, (2) preference (i.e. the stimulus condition that maximizes the peak response), and (3) selectivity (i.e. sharpness of tuning). Responsiveness metrics characterize whether or not a particular stimulus type (e.g. drifting gratings) reproducibly elicits increased activity. For responsive neurons, preference metrics (e.g. preferred temporal frequency) determine which stimulus condition (out of a finite set) elicits the largest response, and serve as an indicator of a neuron's functional specialization—for example, whether it responds preferentially to slow- or fast-moving stimuli. Lastly, selectivity metrics (e.g. orientation selectivity, lifetime sparseness) characterize a neuron's ability to distinguish between particular exemplars within a stimulus class. All three of these features must be measured accurately in order to understand how stimuli are represented by individual neurons. We find that preference metrics are largely invariant across modalities. However, in this dataset, electrophysiology suggests that neurons show a higher degree of responsiveness, while imaging suggests that responsive neurons show a higher degree of selectivity. In the absence of steps taken to mitigate these differences, the two modalities will yield mutually incompatible conclusions about basic neural response properties. These differences could be reduced by lowering the amplitude threshold for valid ΔF/F events, applying a spikes-to-calcium forward model to the electrophysiology data (Deneux et al., 2016), or sub-selection of neurons based either on event rate or by contamination level (the likelihood that signal from other neurons is misattributed to the neurons under consideration). This reconciliation reveals the respective biases of these two recording modalities, namely that extracellular electrophysiology predominantly captures the activity of highly active units while missing or merging low-firing-rate units, while calcium-indicator binding dynamics sparsify neural responses and supralinearly amplify spike bursts. Results We compared the visual responses measured in the Allen Brain Observatory Visual Coding ('imaging') and Allen Brain Observatory Neuropixels ('ephys') datasets, publicly available through brain-map.org and the AllenSDK Python package. These datasets consist of recordings from neurons in six cortical visual areas (as well as subcortical areas in the Neuropixels dataset) in the awake, head-fixed mouse in response to a battery of passively viewed visual stimuli. For both datasets, the same drifting gratings, static gratings, natural scenes, and natural movie stimuli were shown (Figure 1E). These stimuli were presented in a single 3 hr recording session for the ephys dataset. For the imaging dataset, these stimuli were divided across three separate 1 hr imaging sessions from the same group of neurons. In both ephys and imaging experiments, mice were free to run on a rotating disc, the motion of which was continuously recorded. The imaging dataset was collected using genetically encoded GCaMP6f (Chen et al., 2013) under the control of specific Cre driver lines. These Cre drivers limit the calcium indicator expression to specific neuronal populations, including different excitatory and inhibitory populations found in specific cortical layers (see de Vries et al., 2020 for details). The ephys dataset also made use of transgenic mice in addition to wild-type mice. These transgenic mice expressed either channelrhodopsin in specific inhibitory populations for identification using optotagging (see Siegle et al., 2021 for details), or GCaMP6f in specific excitatory or inhibitory populations (see Materials and methods). Unlike in the imaging dataset, however, these transgenic tools did not determine which neurons could be recorded. We limited our comparative analysis to putative excitatory neurons from five cortical visual areas (V1, LM, AL, PM, and AM). In the case of the imaging data, we only included data from 10 excitatory Cre lines, while for ephys we limited our analysis to regular-spiking units by setting a threshold on the waveform duration (>0.4 ms). After this filtering step, we were left with 41,578 neurons from 170 mice in imaging, and 11,030 neurons from 52 mice in ephys. The total number of cells for each genotype, layer, and area is shown in Figure 1F. Calculating response magnitudes for both modalities In order to directly compare results from ephys and imaging, we first calculated the magnitude of each neuron's response to individual trials, which were defined as the interval over which a stimulus was present on the screen. We computed a variety of metrics based on these response magnitudes, and compared the overall distributions of those metrics for all the neurons in each visual area. The methods for measuring these responses necessarily differ between modalities, as explained below. For the ephys dataset, stimulus-evoked responses were computed using the spike times identified by Kilosort2 (Pachitariu et al., 2016a; Stringer et al., 2019a). Kilosort2 uses information in the extracellularly recorded voltage traces to find templates that fit the spike waveform shapes of all the units in the dataset, and assigns a template to each spike. The process of 'spike sorting'—regardless of the underlying algorithm—does not perfectly recover the true underlying spike times, and has the potential to miss spikes (false negatives) or assign spikes (or noise waveforms) to the wrong unit (false positives). The magnitude of the response for a given trial was determined by counting the total number of spikes (including false positives and excluding false negatives) that occured during the stimulation interval. This spike-rate–based analysis is the de facto standard for analyzing electrophysiology data, but it washes out information about bursting or other within-trial dynamics. For example, a trial that includes a four-spike burst will have the same apparent magnitude as a trial with four isolated spikes (Figure 2A). Figure 2 with 4 supplements see all Download asset Open asset Baseline metric comparison. (A) Steps involved in computing response magnitudes for units in the ephys dataset. (B) Same as A, but for the imaging dataset. (C) Drifting gratings spike rasters for an example ephys neuron. Each raster represents 2 s of spikes in response to 15 presentations of a drifting grating at one orientation and temporal frequency. Inset: spike raster for the neuron's preferred condition, with each trial's response magnitude shown on the right, and compared to the 95th percentile of the spontaneous distribution. Responsiveness (purple), preference (red), and selectivity (brown) metrics are indicated. (D) Same as C, but for an example imaged neuron. (E) Fraction of neurons deemed responsive to each of four stimulus types, using the same responsiveness metric for both ephys (gray) and imaging (green). Numbers above each pair of bars represent the Jensen–Shannon distance between the full distribution of response reliabilities for each stimulus/area combination. (F) Distribution of preferred temporal frequencies for all neurons in five different areas. The value D represents the Jensen–Shannon distance between the ephys and imaging distributions. (G) Distributions of lifetime sparseness in response to a drifting grating stimulus for all neurons in five different areas. The value D represents the Jensen–Shannon distance between the ephys and imaging distributions. Methods for determining response magnitudes for neurons in imaging datasets are less standardized, and deserve careful consideration. The most commonly used approach involves averaging the continuous, baseline-normalized fluorescence signal over the trial interval. This method relies on information that is closer to the raw data. However, it suffers the severe drawback that, due to the long decay time of calcium indicators, activity from one trial can contaminate the fluorescence trace during the next trial, especially when relatively short (<1 s) inter-stimulus intervals are used. To surmount this problem, one can attempt to determine the onset of abrupt changes in fluorescence and analyze these extracted 'events,' rather than the continuous trace. There are a variety of algorithms available for this purpose, including non-negative deconvolution (Vogelstein et al., 2010; Friedrich et al., 2017), approaches that model calcium binding kinetics (Deneux et al., 2016; Greenberg et al., 2018), and methods based on machine learning (Theis et al., 2016; Berens et al., 2018; Rupprecht et al., 2021). For our initial comparison, we extracted events using the same method we applied to our previous analysis of the large-scale imaging dataset (de Vries et al., 2020). This algorithm finds event times by reframing ℓ0-regularized deconvolution as a change point detection problem that has a mathematically guaranteed, globally optimal 'exact' solution (hereafter, 'exact ℓ0'; Jewell and Witten, 2018; Jewell et al., 2018). The algorithm includes a sparsity constraint (λ) that is calibrated to each neuron's overall noise level. For the most part, the events that are detected from the 2P imaging data do not represent individual spikes, but rather are heavily biased towards indicating short bouts of high firing rate, for example bursting (Huang et al., 2021). There is, however, rich information contained in the amplitudes of these events, which have a non-linear—albeit on average monotonic—relationship with the underlying number of true spikes within a window. Therefore, in our population imaging dataset, we calculated the trial response magnitude by summing the amplitudes of events that occurred during the stimulation interval (Figure 2B). In example trials for the same hypothetical neuron recorded with both modalities (Figure 2A,B), the response magnitudes are equivalent from the perspective of electrophysiology. However, from the perspective of imaging, the trial that includes a spike burst (which results in a large influx of calcium) may have an order-of-magnitude larger response than a trial that only includes isolated spikes. Baseline metric comparison A comparison between individual neurons highlights the effect of differences in response magnitude calculation on visual physiology. A spike raster from a neuron in V1 recorded with electrophysiology (Figure 2C) appears much denser than the corresponding event raster for a separate neuron that was imaged in the same area (Figure 2D). For each neuron, we computed responsiveness, preference, and selectivity metrics. We consider both neurons to be responsive to the drifting gratings stimulus class because they have a significant response (p < 0.05, compared to a distribution of activity taken during the epoch of spontaneous activity) on at least 25% of the trials of the preferred condition (the grating direction and temporal frequency that elicited the largest mean response) (de Vries et al., 2020). Since these neurons were deemed responsive according to this criterion, their function was further characterized in terms of their preferred stimulus condition and their selectivity (a measure of tuning curve sharpness). We use lifetime sparseness (Vinje and Gallant, 2000) as our primary selectivity metric, because it is a general metric that is applicable to every stimulus type. It reflects the distribution of responses of a neuron across some stimulus space (e.g. natural scenes or drifting gratings), equaling 0 if the neuron responds equivalently to all stimulus conditions, and one if the neuron only responds to a single condition. Across all areas and mouse lines, lifetime sparseness is highly correlated with more traditional selectivity metrics, such as drifting gratings orientation selectivity (R = 0.8 for ephys, 0.79 for imaging; Pearson correlation), static gratings orientation selectivity (R = 0.79 for ephys, 0.69 for imaging), and natural scenes image selectivity (R = 0.85 for ephys, 0.95 for imaging). For our initial analysis, we sought to compare the results from ephys and imaging as they are typically analyzed in the literature, prior to any attempt at reconciliation. We will refer to these comparisons as 'baseline comparisons' in order to distinguish them from subsequent comparisons made after applying one or more transformations to the imaging and/or ephys datasets. We pooled responsiveness, preference, and selectivity metrics for all of the neurons in a given visual area across experiments, and quantified the disparity between the imaging and ephys distributions using Jensen–Shannon distance. This is the square root of the Jensen–Shannon divergence, which is a method of measuring the disparity between two probability distributions that is symmetric and always has a finite value (Lin, 1991). Jensen–Shannon distance is equal to 0 for perfectly overlapping distributions, and one for completely non-overlapping distributions, and falls in between these values for partially overlapping distributions. Across all areas and stimuli, the fraction of responsive neurons was higher in the ephys dataset than the imaging dataset (Figure 2E). To quantify the difference between modalities, we computed the Jensen–Shannon distance for the distributions of response reliabilities, rather than the fraction of responsive neurons at the 25% threshold level. This is done to ensure that our results are not too sensitive to the specific responsiveness threshold we have chosen. We found tuning preferences to be consistent between the two modalities, including preferred temporal frequency (Figure 2F), preferred direction (Figure 2—figure supplement 1A), preferred orientation (Figure 2—figure supplement 1B), and preferred spatial frequency (Figure 2—figure supplement 1C). This was based on the qualitative similarity of their overall distributions, as well as their low values of Jensen–Shannon distance. Selectivity metrics, such as lifetime sparseness (Figure 2G), orientation selectivity (Figure 2—figure supplement 1D), and direction selectivity (Figure 2—figure supplement 1E), were consistently higher in imaging than ephys. Controlling for laminar sampling bias and running behavior To control for potential high-level variations across the imaging and ephys experimental preparations, we first examined the effect of laminar sampling bias. For example, the ephys dataset contained more neurons in layer 5, due to the presence of large, highly active cells in this layer. The imaging dataset, on the other hand, had more neurons in layer 4 due to the preponderance of layer 4 Cre lines included in the dataset (Figure 2—figure supplement 2A). After resampling each dataset to match layer distributions (Figure 2—figure supplement 2B, see Materials and methods for details), we saw very little change in the overall distributions of responsiveness, preference, and selectivity metrics (Figure 2—figure supplement 2C–E), indicating that laminar sampling biases are likely not a key cause of the differences we observed between the modalities. We next sought to quantify the influence of behavioral differences on our comparison. As running and other motor behavior can influence visually evoked responses (Niell and Stryker, 2010; Stringer et al., 2019a; Vinck et al., 2015; de Vries et al., 2020), could modality-specific behavioral differences contribute to the discrepancies in the response metrics? In our datasets, mice tend to spend a larger fraction of time running in the ephys experiments, perhaps because of the longer experiment duration, which may be further confounded by genotype-specific differences in running behavior (Figure 2—figure supplement 3A). Within each modality, running had a similar impact on visual response metrics. On average, units in ephys and neurons in imaging have slightly lower responsiveness during periods of running versus non-running (Figure 2—figure supplement 3B), but slightly higher selectivity (Figure 2—figure supplement 3C). To control for the effect of running, we sub-sampled our imaging experiments in order to match the overall distribution of running fraction to the ephys data (Figure 2—figure supplement 4A). This transformation had a negligible impact on responsiveness, selectivity, and preference metrics (Figure 2—figure supplement 4B–D). From this analysis we conclude that, at least for the datasets examined here, behavioral differences do not account for the differences in functional properties inferred from imaging and ephys. Impact of event detection on functional metrics We sought to determine whether our approach to extracting events from the 2P data could explain between-modality differences in responsiveness and selectivity. Prior work has shown that scientific conclusions can depend on both the method of event extraction and the chosen parameters (Evans et al., 2019). However, the impact of different algorithms on functional metrics has yet to be assessed in a systematic way. To address this shortcoming, we first compared two event detection algorithms, exact ℓ0 and unpenalized non-negative deconvolution (NND), another event extraction method that performs well on a ground truth dataset of simultaneous two-photon imaging and loose patch recordings from primary visual cortex (Huang et al., 2021). Care was taken to ensure that the characteristics of the ground truth imaging data matched those of our large-scale population recordings in terms of their imaging resolution, frame rate, and noise levels, which implicitly accounted for differences in laser power across experiments. The correlation between the ground truth firing rate and the overall event amplitude within a given time bin is a common way of assessing event extraction performance (Theis et al., 2016; Berens et al., 2018; Rupprecht et al., 2021). Both algorithms performed equally well in terms of their ability to predict the instantaneous firing rate (for 100 ms bins, exact ℓ0 r = 0.48 ± 0.23; NND r = 0.50 ± 0.24; p = 0.1, Wilcoxon signed-rank test). However, this metric does not capture all of the relevant features of the event time series. In particular, it ignores the rate of false positive events that are detected in the absence of a true underlying spike (Figure 3A). We found that the exact ℓ0 method, which includes a built-in sparsity constraint, had a low rate of false positives (8 ± 2% in 100 ms bins, N = 32 ground truth recordings), whereas NND had a much higher rate (21 ± 4%; p = 8e-7, Wilcoxon signed-rank test). Small-amplitude false positive events have very little impact on the overall correlation between the ground truth spike rate and the extracted events, so parameter optimization does not typically penalize such events. However, we reasoned that the summation of many false positive events could have a noticeable impact on response magnitudes averaged over trials. Because these events always have a positive sign, they cannot be canceled out by low-amplitude negative deflections of similar magnitude, as would occur when analyzing ΔF/F directly. Figure 3 Download asset Open asset Impact of calcium event detection on functional metrics. (A) GCaMP6f fluorescence trace, spike times, and detected events for one example neuron with simultaneous imaging and electrophysiology. Events are either detected via exact ℓ0-regularized deconvolution (Jewell and Witten, 2018) or unpenalized non-negative deconvolution (Friedrich et al., 2017). Each 250 ms bin is classified as a 'hit,' 'miss,' 'false positive,' or 'true positive' by comparing the presence/absence of detected events with the true underlying spike times. (B) Correlation between binned event amplitude and ground truth firing rate after filtering NND events at different threshold levels relative to each neuron's noise level (σ). The average across neurons is shown as colored dots. (C) Same as B, but for the average response amplitude within a 100 ms interval (relative to the amplitude with no thresholding applied). (D) Same as B, but for the probability of detecting a false positive event in a 100 ms interval. (E) Event times in response to 600 drifting gratings presentations for one example neuron, sorted by grating direction (ignoring differences in temporal frequency). Dots representing individual events are scaled by the amplitude of the associated event. Each column represents the results of a different event detection method. The resulting tuning curves and measured global orientation selectivity index (gOSI) are shown
Abstract Identification of the structural connections between neurons is a prerequisite to understanding brain function. We developed a pipeline to systematically map brain-wide monosynaptic inputs to specific neuronal populations using Cre-driver mouse lines and the recombinant rabies tracing system. We first improved the rabies virus tracing strategy to accurately identify starter cells and to efficiently quantify presynaptic inputs. We then mapped brain-wide presynaptic inputs to different excitatory and inhibitory neuron subclasses in the primary visual cortex and seven higher visual areas. Our results reveal quantitative target-, layer- and cell-class-specific differences in the retrograde connectomes, despite similar global input patterns to different neuronal populations in the same anatomical area. The retrograde connectivity we define is consistent with the presence of the ventral and dorsal visual information processing streams and reveals further subnetworks within the dorsal stream. The hierarchical organization of the entire visual cortex can be derived from intracortical feedforward and feedback pathways mediated by upper- and lower-layer input neurons, respectively. This study expands our knowledge of the brain-wide inputs regulating visual areas and demonstrates that our improved rabies virus tracing strategy can be used to scale up the effort in dissecting connectivity of genetically defined cell populations in the whole mouse brain.
Abstract Identification of the structural connections between neurons is a prerequisite to understanding brain function. We developed a pipeline to systematically map brain-wide monosynaptic inputs to specific neuronal populations using Cre-driver mouse lines and the recombinant rabies tracing system. We first improved the rabies virus tracing strategy to accurately identify starter cells and to efficiently quantify presynaptic inputs. We then mapped brain-wide presynaptic inputs to different excitatory and inhibitory neuron subclasses in the primary visual cortex and seven higher visual areas. Our results reveal quantitative target-, layer- and cell-class-specific differences in the retrograde connectomes, despite similar global input patterns to different neuronal populations in the same anatomical area. The retrograde connectivity we define is consistent with the presence of the ventral and dorsal visual information processing streams and reveals further subnetworks within the dorsal stream. The hierarchical organization of the entire visual cortex can be derived from intracortical feedforward and feedback pathways mediated by upper- and lower-layer input neurons, respectively. This study expands our knowledge of the brain-wide inputs regulating visual areas and demonstrates that our improved rabies virus tracing strategy can be used to scale up the effort in dissecting connectivity of genetically defined cell populations in the whole mouse brain.
Extracellular electrophysiology and two-photon calcium imaging are widely used methods for measuring physiological activity with single-cell resolution across large populations of cortical neurons. While each of these two modalities has distinct advantages and disadvantages, neither provides complete, unbiased information about the underlying neural population. Here, we compare evoked responses in visual cortex recorded in awake mice under highly standardized conditions using either imaging of genetically expressed GCaMP6f or electrophysiology with silicon probes. Across all stimulus conditions tested, we observe a larger fraction of responsive neurons in electrophysiology and higher stimulus selectivity in calcium imaging, which was partially reconciled by applying a spikes-to-calcium forward model to the electrophysiology data. However, the forward model could only reconcile differences in responsiveness when restricted to neurons with low contamination and an event rate above a minimum threshold. This work established how the biases of these two modalities impact functional metrics that are fundamental for characterizing sensory-evoked responses.
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