Large-scale deep tissue voltage imaging with targeted-illumination confocal microscopy.

Voltage imaging with cellular specificity has been made possible by advances in genetically encoded voltage indicators. However, the kilohertz rates required for voltage imaging lead to weak signals. Moreover, out-of-focus fluorescence and tissue scattering produce background that both undermines the signal-to-noise ratio and induces crosstalk between cells, making reliable in vivo imaging in densely labeled tissue highly challenging. We describe a microscope that combines the distinct advantages of targeted illumination and confocal gating while also maximizing signal detection efficiency. The resulting benefits in signal-to-noise ratio and crosstalk reduction are quantified experimentally and theoretically. Our microscope provides a versatile solution for enabling high-fidelity in vivo voltage imaging at large scales and penetration depths, which we demonstrate across a wide range of imaging conditions and different genetically encoded voltage indicator classes.

Statistically unbiased prediction enables accurate denoising of voltage imaging data.

Here we report SUPPORT (statistically unbiased prediction utilizing spatiotemporal information in imaging data), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatiotemporal neighboring pixels, even when its temporally adjacent frames alone do not provide useful information for statistical prediction. Such dependency is captured and used by a convolutional neural network with a spatiotemporal blind spot to accurately denoise voltage imaging data in which the existence of the action potential in a time frame cannot be inferred by the information in other frames. Through simulations and experiments, we show that SUPPORT enables precise denoising of voltage imaging data and other types of microscopy image while preserving the underlying dynamics within the scene.

Theta and gamma rhythmic coding through two spike output modes in the hippocampus during spatial navigation.

Hippocampal CA1 neurons generate single spikes and stereotyped bursts of spikes. However, it is unclear how individual neurons dynamically switch between these output modes and whether these two spiking outputs relay distinct information. We performed extracellular recordings in spatially navigating rats and cellular voltage imaging and optogenetics in awake mice. We found that spike bursts are preferentially linked to cellular and network theta rhythms (3-12 Hz) and encode an animal's position via theta phase precession, particularly as animals are entering a place field. In contrast, single spikes exhibit additional coupling to gamma rhythms (30-100 Hz), particularly as animals leave a place field. Biophysical modeling suggests that intracellular properties alone are sufficient to explain the observed input frequency-dependent spike coding. Thus, hippocampal neurons regulate the generation of bursts and single spikes according to frequency-specific network and intracellular dynamics, suggesting that these spiking modes perform distinct computations to support spatial behavior.

Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy.

Voltage imaging with cellular specificity has been made possible by the tremendous advances in genetically encoded voltage indicators (GEVIs). However, the kilohertz rates required for voltage imaging lead to weak signals. Moreover, out-of-focus fluorescence and tissue scattering produce background that both undermines signal-to-noise ratio (SNR) and induces crosstalk between cells, making reliable in vivo imaging in densely labeled tissue highly challenging. We describe a microscope that combines the distinct advantages of targeted illumination and confocal gating, while also maximizing signal detection efficiency. The resulting benefits in SNR and crosstalk reduction are quantified experimentally and theoretically. Our microscope provides a versatile solution for enabling high-fidelity in vivo voltage imaging at large scales and penetration depths, which we demonstrate across a wide range of imaging conditions and different GEVI classes.

Striatal cholinergic interneuron membrane voltage tracks locomotor rhythms in mice.

Rhythmic neural network activity has been broadly linked to behavior. However, it is unclear how membrane potentials of individual neurons track behavioral rhythms, even though many neurons exhibit pace-making properties in isolated brain circuits. To examine whether single-cell voltage rhythmicity is coupled to behavioral rhythms, we focused on delta-frequencies (1-4 Hz) that are known to occur at both the neural network and behavioral levels. We performed membrane voltage imaging of individual striatal neurons simultaneously with network-level local field potential recordings in mice during voluntary movement. We report sustained delta oscillations in the membrane potentials of many striatal neurons, particularly cholinergic interneurons, which organize spikes and network oscillations at beta-frequencies (20-40 Hz) associated with locomotion. Furthermore, the delta-frequency patterned cellular dynamics are coupled to animals' stepping cycles. Thus, delta-rhythmic cellular dynamics in cholinergic interneurons, known for their autonomous pace-making capabilities, play an important role in regulating network rhythmicity and movement patterning.

Stimulus representations in visual cortex shaped by spatial attention and microsaccades.

Microsaccades (MSs) are commonly associated with spatially directed attention, but how they affect visual processing is still not clear. We studied MSs in a task in which the animal was randomly cued to attend to a target stimulus and ignore distractors, and it was rewarded for detecting a color change in the target. We found that the enhancement of firing rates normally found with attention to a cued stimulus was delayed until the first MS directed towards that stimulus. Once that MS occurred, attention to the target was engaged and there were persistent effects of attention on firing rates for the remainder of the trial. These effects were found in the superficial and deep layers of V4 as well as the lateral pulvinar and IT cortex. Although the tuning curves of V4 cells do not change depending on the locus of spatial attention, we found pronounced effects of MS direction on stimulus representations that persisted for the length of the trial in V4. In intervals following a MS towards the target in the RF, stimulus decoding from population activity was substantially better than in intervals following a MS away from the target. Likewise, turning curves of cells were substantially sharper following a MS towards the target in the RF. This sharpening appeared to result from both a "refreshing" of the initial transient sensory response to stimulus onset, and a magnification of the effects of attention in this condition. MSs to the target also enhanced the neuronal response to the behaviorally relevant target color change and led to faster reaction times. These results thus reveal a major link between spatial attention, object processing and its coordination with eye movements.

Deep brain stimulation creates informational lesion through membrane depolarization in mouse hippocampus.

Deep brain stimulation (DBS) is a promising neuromodulation therapy, but the neurophysiological mechanisms of DBS remain unclear. In awake mice, we performed high-speed membrane voltage fluorescence imaging of individual hippocampal CA1 neurons during DBS delivered at 40 Hz or 140 Hz, free of electrical interference. DBS powerfully depolarized somatic membrane potentials without suppressing spike rate, especially at 140 Hz. Further, DBS paced membrane voltage and spike timing at the stimulation frequency and reduced timed spiking output in response to hippocampal network theta-rhythmic (3-12 Hz) activity patterns. To determine whether DBS directly impacts cellular processing of inputs, we optogenetically evoked theta-rhythmic membrane depolarization at the soma. We found that DBS-evoked membrane depolarization was correlated with DBS-mediated suppression of neuronal responses to optogenetic inputs. These results demonstrate that DBS produces powerful membrane depolarization that interferes with the ability of individual neurons to respond to inputs, creating an informational lesion.

Tuning Neural Synchronization: The Role of Variable Oscillation Frequencies in Neural Circuits.

Brain oscillations emerge during sensory and cognitive processes and have been classified into different frequency bands. Yet, even within the same frequency band and between nearby brain locations, the exact frequencies of brain oscillations can differ. These frequency differences (detuning) have been largely ignored and play little role in current functional theories of brain oscillations. This contrasts with the crucial role that detuning plays in synchronization theory, as originally derived in physical systems. Here, we propose that detuning is equally important to understand synchronization in biological systems. Detuning is a critical control parameter in synchronization, which is not only important in shaping phase-locking, but also in establishing preferred phase relations between oscillators. We review recent evidence that frequency differences between brain locations are ubiquitous and essential in shaping temporal neural coordination. With the rise of powerful experimental techniques to probe brain oscillations, the contributions of exact frequency and detuning across neural circuits will become increasingly clear and will play a key part in developing a new understanding of the role of oscillations in brain function.

Large-scale voltage imaging in behaving mice using targeted illumination.

Recent improvements in genetically encoded voltage indicators enabled optical imaging of action potentials and subthreshold transmembrane voltage in vivo. To perform high-speed voltage imaging of many neurons simultaneously over a large anatomical area, widefield microscopy remains an essential tool. However, the lack of optical sectioning makes widefield microscopy prone to background cross-contamination. We implemented a digital-micromirror-device-based targeted illumination strategy to restrict illumination to the cells of interest and quantified the resulting improvement both theoretically and experimentally with SomArchon expressing neurons. We found that targeted illumination increased SomArchon signal contrast, decreased photobleaching, and reduced background cross-contamination. With the use of a high-speed, large-area sCMOS camera, we routinely imaged tens of spiking neurons simultaneously over minutes in behaving mice. Thus, the targeted illumination strategy described here offers a simple solution for widefield voltage imaging of many neurons over a large field of view in behaving animals.

Empirically constrained network models for contrast-dependent modulation of gamma rhythm in V1.

Gamma oscillations are thought to play a key role in neuronal network function and neuronal communication, yet the underlying generating mechanisms have not been fully elucidated to date. At least partly, this may be due to the fact that even in simple network models of interconnected inhibitory (I) and excitatory (E) neurons, many parameters remain unknown and are set based on practical considerations or by convention. Here, we mitigate this problem by requiring PING (Pyramidal Interneuron Network Gamma) models to simultaneously satisfy a broad set of criteria for realistic behaviour based on empirical data spanning both the single unit (spikes) and local population (LFP) levels while unknown parameters are varied. By doing so, we were able to constrain the parameter ranges and select empirically valid models. The derived model constraints implied weak rather than strong PING as the generating mechanism for gamma, connectivity between E and I neurons within specific bounds, and variations of the external input to E but not I neurons. Constrained models showed valid behaviours, including gamma frequency increases with contrast and power saturation or decay at high contrasts. Using an empirically-validated model we studied the route to gamma instability at high contrasts. This involved increased heterogeneity of E neurons with increasing input triggering a breakdown of I neuron pacemaker function. Further, we illustrate the model's capacity to resolve disputes in the literature concerning gamma oscillation properties and GABA conductance proxies. We propose that the models derived in our study will be useful for other modelling studies, and that our approach to the empirical constraining of PING models can be expanded when richer empirical datasets become available. As local gamma networks are the building blocks of larger networks that aim to understand complex cognition through their interactions, there is considerable value in improving our models of these building blocks.

Context-Dependent Sensory Processing across Primary and Secondary Somatosensory Cortex.

To interpret the environment, our brain must evaluate external stimuli against internal representations from past experiences. How primary (S1) and secondary (S2) somatosensory cortices process stimuli depending on recent experiences is unclear. Using simultaneous multi-area population imaging of projection neurons and focal optogenetic inactivation, we studied mice performing a whisker-based working memory task. We find that activity reflecting a current stimulus, the recollection of a previous stimulus (cued recall), and the stimulus category are distributed across S1 and S2. Despite this overlapping representation, S2 is important for processing cued recall responses and transmitting these responses to S1. S2 network properties differ from S1, wherein S2 persistently encodes cued recall and the stimulus category under passive conditions. Although both areas encode the stimulus category, only information in S1 is important for task performance through pathways that do not necessarily include S2. These findings reveal both distributed and segregated roles for S1 and S2 in context-dependent sensory processing.

The Attentional Blink is Related to the Microsaccade Rate Signature.

The reduced detectability of a target T2 following discrimination of a preceding target T1 in the attentional blink (AB) paradigm is classically interpreted as a consequence of reduced attention to T2 due to attentional allocation to T1. Here, we investigated whether AB was related to changes in microsaccade rate (MSR). We found a pronounced MSR signature following T1 onset, characterized by MSR suppression from 200 to 328 ms and enhancement from 380 to 568 ms. Across participants, the magnitude of the MSR suppression correlated with the AB effect such that low T2 detectability corresponded to reduced MSR. However, in the same task, T1 error trials coincided with the presence of microsaccades. We discuss this apparent paradox in terms of known neurophysiological correlates of MS whereby cortical excitability is suppressed both during the microsaccade and MSR suppression, in accordance to poor T1 performance with microsaccade occurrence and poor T2 performance with microsaccade absence. Our data suggest a novel low-level mechanism contributing to AB characterized by reduced MSR, thought to cause suppressed visual cortex excitability. This opens the question of whether attention mediates T2 performance suppression independently from MSR, and if not, how attention interacts with MSR to produce the T2 performance suppression.

Within-quadrant position and orientation specificity after extensive orientation discrimination learning is related to performance gains during late learning.

The last decade has seen the emergence of new views about the mechanisms underlying specificity (or, conversely, generalization) of visual skill learning. Here, we trained participants at orientation discrimination paradigm at a peripheral position to induce position and orientation specificity and to test its underlying mechanisms. Specifically, we aimed to test whether the within-quadrant spatial gradient of generalization is determined by cortical magnification, which would show that retinotopic plasticity contributes to learning and specificity. Additionally, we aimed to test whether late parts of the learning relate differently to specificity compared to early parts. This is relevant in the context of double training papers, which suggest that rule-based mechanisms of specificity in fast, early learning also would apply to late, slower learning. Our data showed partial but significant position and orientation specificity within quadrants. Interestingly, specificity was greatest for those participants who had continued to show threshold decreases during the last five sessions of training (late, asymptotic learning). Performance gains during early learning were less related to specificity. A trend for skill to spread over larger distances towards periphery than towards central vision suggested contributions to transfer of early visual areas showing cortical magnification of central vision. Control experiments however did not support this hypothesis. In summary, our study demonstrates significant specificity after extensive perceptual learning, and indicates that asymptotic learning recruits specific mechanisms that promote specificity, and that may not be recruited yet in early parts of the learning. The contributions of different mechanisms to early and late learning suggests that following these different learning periods, generalization relies on different principles and is subjected to different limits.

Enhanced Neural Processing by Covert Attention only during Microsaccades Directed toward the Attended Stimulus.

Attention can be "covertly" directed without eye movements; yet, even during fixation, there are continuous microsaccades (MSs). In areas V4 and IT of macaques, we found that firing rates and stimulus representations were enhanced by attention but only following a MS toward the attended stimulus. The onset of neural attentional modulations was tightly coupled to the MS onset. The results reveal a major link between the effects of covert attention on cortical visual processing and the overt movement of the eyes.

Microsaccade-rhythmic modulation of neural synchronization and coding within and across cortical areas V1 and V2.

Primates sample their visual environment actively through saccades and microsaccades (MSs). Saccadic eye movements not only modulate neural spike rates but might also affect temporal correlations (synchrony) among neurons. Neural synchrony plays a role in neural coding and modulates information transfer between cortical areas. The question arises of how eye movements shape neural synchrony within and across cortical areas and how it affects visual processing. Through local field recordings in macaque early visual cortex while monitoring eye position and through neural network simulations, we find 2 distinct synchrony regimes in early visual cortex that are embedded in a 3- to 4-Hz MS-related rhythm during visual fixation. In the period shortly after an MS ("transient period"), synchrony was high within and between cortical areas. In the subsequent period ("sustained period"), overall synchrony dropped and became selective to stimulus properties. Only mutually connected neurons with similar stimulus responses exhibited sustained narrow-band gamma synchrony (25-80 Hz), both within and across cortical areas. Recordings in macaque V1 and V2 matched the model predictions. Furthermore, our modeling provides predictions on how (micro)saccade-modulated gamma synchrony in V1 shapes V2 receptive fields (RFs). We suggest that the rhythmic alternation between synchronization regimes represents a basic repeating sampling strategy of the visual system.

A quantitative theory of gamma synchronization in macaque V1.

Gamma-band synchronization coordinates brief periods of excitability in oscillating neuronal populations to optimize information transmission during sensation and cognition. Commonly, a stable, shared frequency over time is considered a condition for functional neural synchronization. Here, we demonstrate the opposite: instantaneous frequency modulations are critical to regulate phase relations and synchronization. In monkey visual area V1, nearby local populations driven by different visual stimulation showed different gamma frequencies. When similar enough, these frequencies continually attracted and repulsed each other, which enabled preferred phase relations to be maintained in periods of minimized frequency difference. Crucially, the precise dynamics of frequencies and phases across a wide range of stimulus conditions was predicted from a physics theory that describes how weakly coupled oscillators influence each other's phase relations. Hence, the fundamental mathematical principle of synchronization through instantaneous frequency modulations applies to gamma in V1 and is likely generalizable to other brain regions and rhythms.

Discovering recurring patterns in electrophysiological recordings.

BACKGROUND: Fourier-based techniques are used abundantly in the analysis of electrophysiological data. However, these techniques are of limited value when the signal of interest is non-sinusoidal or non-periodic. NEW METHOD: We present sliding window matching (SWM): a new data-driven method for discovering recurring temporal patterns in electrophysiological data. SWM is effective in detecting recurring but unknown patterns even when they appear non-periodically. RESULTS: To demonstrate this, we used SWM on oscillations in local field potential (LFP) recordings from the rat hippocampus and monkey V1. The application of SWM yielded two interesting findings. We could show that rat hippocampal theta and monkey V1 gamma oscillations were both skewed (i.e. asymmetric in time), rather than being sinusoidal. Furthermore, gamma oscillations in monkey V1 were skewed differently in the superficial compared to the deeper cortical layers. Second, we used SWM to analyze responses evoked by stimuli or microsaccades even when the onset timing of stimulus or microsaccades was unknown. COMPARISON WITH EXISTING METHODS: We first validated the method on simulated datasets, and we checked that for recordings with a sufficiently low noise level the SWM results were consistent with results from the widely used phase alignment (PA) method. CONCLUSIONS: We conclude that the proposed method has wide applicability in the exploration of noisy time series data where the onset times of particular events are unknown by the experimenter such as in resting state and sleep recordings.

Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value Approaches.

Synchronization or phase-locking between oscillating neuronal groups is considered to be important for coordination of information among cortical networks. Spectral coherence is a commonly used approach to quantify phase locking between neural signals. We systematically explored the validity of spectral coherence measures for quantifying synchronization among neural oscillators. To that aim, we simulated coupled oscillatory signals that exhibited synchronization dynamics using an abstract phase-oscillator model as well as interacting gamma-generating spiking neural networks. We found that, within a large parameter range, the spectral coherence measure deviated substantially from the expected phase-locking. Moreover, spectral coherence did not converge to the expected value with increasing signal-to-noise ratio. We found that spectral coherence particularly failed when oscillators were in the partially (intermittent) synchronized state, which we expect to be the most likely state for neural synchronization. The failure was due to the fast frequency and amplitude changes induced by synchronization forces. We then investigated whether spectral coherence reflected the information flow among networks measured by transfer entropy (TE) of spike trains. We found that spectral coherence failed to robustly reflect changes in synchrony-mediated information flow between neural networks in many instances. As an alternative approach we explored a phase-locking value (PLV) method based on the reconstruction of the instantaneous phase. As one approach for reconstructing instantaneous phase, we used the Hilbert Transform (HT) preceded by Singular Spectrum Decomposition (SSD) of the signal. PLV estimates have broad applicability as they do not rely on stationarity, and, unlike spectral coherence, they enable more accurate estimations of oscillatory synchronization across a wide range of different synchronization regimes, and better tracking of synchronization-mediated information flow among networks.

Input-dependent frequency modulation of cortical gamma oscillations shapes spatial synchronization and enables phase coding.

Fine-scale temporal organization of cortical activity in the gamma range (∼25-80Hz) may play a significant role in information processing, for example by neural grouping ('binding') and phase coding. Recent experimental studies have shown that the precise frequency of gamma oscillations varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common frequency. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent frequency modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different oscillation frequencies at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and frequency differences, and quantified the stimulus-related information represented by gamma phase and frequency. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled oscillators, where input drive determines the intrinsic (natural) frequency of oscillators. The gamma phase-locking, the precise phase relation and the emergent (measurable) frequencies were determined by two principal factors: the detuning (intrinsic frequency difference, i.e. local input difference) and the coupling strength. In addition to frequency coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower oscillation phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma frequencies could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity.

Robust gamma coherence between macaque V1 and V2 by dynamic frequency matching.

Current theories propose that coherence of oscillatory brain activity in the gamma band (30-80 Hz) constitutes an avenue for communication among remote neural populations. However, reports documenting stimulus dependency and time variability of gamma frequency suggest that distant neuronal populations may, at any one time, operate at different frequencies precluding synchronization. To test this idea, we recorded from macaque V1 and V2 simultaneously while presenting gratings of varying contrast. Although gamma frequency increased with stimulus contrast in V1 and V2 (by ∼25 Hz), V1-V2 gamma coherence was maintained for all contrasts. Moreover, while gamma frequency fluctuated by ∼15 Hz during constant contrast stimulation, this fluctuation was highly correlated between V1 and V2. The strongest coherence connections showed a layer-specific pattern, matching feedforward anatomical connectivity. Hence, gamma coherence among remote populations can occur despite large stimulus-induced and time-dependent changes in gamma frequency, allowing communication through coherence to operate without a stimulus independent, fixed-frequency gamma channel.