CSD as a spatiotemporal process.

Biophysical forward models describe the transformation from currents to recorded voltages at any instant in time. In this work, we use the term “biophysical forward model” to refer to a simplified forward model based on simplifying assumptions; in particular, we do not include details such as properties of specific neurons in the forward models. Current flow across a cell membrane creates a current source or sink, and the resulting three-dimensional field potential can be derived using volume conductor theory. Because it is not possible to determine all the contributions of individual transmembrane currents from measured LFPs, the CSD at time t and spatial point s may be conceptualized as the average transmembrane current within a small area around s [2]. We let c(s, t) denote the CSD, taking it to be a continuous function, which we will estimate from the LFP data. The biophysical model maps the CSD c to the field potential, which we write as ϕ, through a spatial linear operator , so that . We use the subscript s to emphasize the spatial operation. The operator takes the form of an integral, so that the field potential at (s, t) is given by (1) for a suitable function a(s, s′) (the form of which will become clear). We write the measured LFP as and assume it is equal to ϕ plus independent noise: (2) Across repeated trials, in response to the same stimulus or producing the same behavior, the CSD will vary [15]. We use a superscript r to identify relevant quantities on trial r, for r = 1, …, N_r, and we assume the CSD functions c^(r)(s, t) are independent realizations of a stochastic process. Finally, we assume the CSD c(s′, t) is the sum of three components operating at distinct timescales, an evoked transient mean μ(s′, t), a slowly varying stationary process η₁(s′, t), and a more rapidly varying stationary process η₂(s′, t). This last process η₂ could be oscillatory. Thus, on trial r we have (3) so that c^(r)(s′, t) becomes a realization of a stochastic process, the realizations being independent across trials. Because is linear, on trial r we get (4)

Eqs (1)–(4), together with estimation of the current source density from the LFP data, define the fundamental components of the framework we have developed. By taking η₁ and η₂ to be Gaussian processes, the functions c^(r)(s′, t) in Eq (3) become Gaussian process current source densities, and we are able to infer features of them, on a trial-by-trial basis. More specifically, the free parameters in Eq (3), together with a free parameter in our forward model, make up a parameter vector θ for a log likelihood function (see Eq (15)), and the current source density on trial r is estimated using the collection of observations across the relevant grid of (s, t) values (Eq (17)). As we will show, this model can generate multiple time series similar to those observed in LFPs [1]. The specifications of the forward model in Eq (1) and the three terms on the right of Eq (3) are application-dependent. We discuss them below in the context of our two data sets.

Forward model details.

We first give an overview of a commonly-used three-dimensional forward model, which is relevant when three-dimensional LFP recordings are available. In the following section we describe additional a priori physical models that are required to adapt the generative model and forward model to LFP measurements from one-dimensional and two-dimensional electrode arrays.

Previous work on CSD estimation has established that an assumption of an isotropic, homogeneous medium with scalar conductivity ς is a reasonable approximation for cortical signals in primates [16]; see also the discussion of forward modeling assumptions in [17], Section 4.2.1. Using the quasi-static assumption, the relationship between the CSD c and the LFP ϕ at a single time point (time index suppressed for clarity of notation) is governed by the Poisson equation [7]: (5) where the spatial location s is described in terms of coordinates (x, y, z). While this appears to give a formula for computing the CSD from the LFP, it requires detailed, accurate knowledge of the LFP in three dimensions, without which it fails to accurately recover the CSD [18]. In addition, estimated second derivatives are highly influenced by noise. However, assuming an infinite volume conductor with negligible boundary conditions, the differential equation may be inverted to an integral equation which instead gives ϕ in terms of a linear integral operator on c [8]: (6)

Adapting the model.

Though our generative model and forward model have thus far been discussed in terms of general processes over three dimensions, it is common for electrodes to be arrayed along only one or two dimensions, which leaves unmeasured the effects in other dimensions. One advantage of the formulation we have described is that it can be adapted to these situations by introducing a priori assumptions about the CSD in the unmeasured directions.

One-dimensional recordings are typically taken along a linear probe inserted perpendicular to the surface of the cortex, so we will index the one-dimensional spatial locations by a single coordinate z representing depth along the probe. We use an a priori physical model in which the CSD is assumed constant in the dimensions perpendicular to the linear probe on a cylinder of radius R around the probe and zero elsewhere [10]; previous work has shown deviations from this shape do not have a large impact on the results [11, 19]. Additionally, as linear probes are typically inserted to penetrate the thickness of the cortex, we assume the CSD is nonzero only on an interval a ≤ z ≤ b representing the thickness of the cortex. These assumptions lead to the following a priori physical model that describes the variation of the CSD in the z direction through a function g(z, t) that varies over time and a single spatial dimension: (7) In Eq (7), is an indicator function that evaluates to 1 if the argument is true and 0 otherwise, and we have used the convention that x = y = 0 corresponds to the probe location. Under this a priori physical model, as shown in S1 Text and previously derived in [10], the three-dimensional forward model of Eq (6) reduces to (8) Thus, under this a priori physical model, the one-dimensional LFP is the result of applying a linear operator to g, where the weighting function a(z, z′; R) (see Eq (1)) decreases with distance z − z′ but also depends on the cylinder radius R. In this case, our prior model on the CSD specifies the space of functions g(z, t) with one spatial and one temporal dimension.

Two-dimensional LFP measurements are commonly collected using microelectrode arrays such as a Utah array or Neuropixels probe [12, 20]. In the Neuropixels data we will analyze, the two-dimensional spatial locations represent depth and width along the probe, so we index the spatial location s by coordinates y for width and z for depth. Unlike with Utah arrays, for Neuropixels, sources behind the face housing the electrode contacts are unlikely to make contributions to the measured LFPs [21], so we assume they don’t. We also assume zero charge in a small area in front of the probe face, which avoids singularities in the forward model. Therefore, the a priori physical model will assume that the CSD is constant in front of the face of the probe in the unmeasured x direction for some distance R after a space of τ; that is, using the convention that x = 0 is the face of the probe and positive x are in front of the probe, we assume (9) We note that this forward model is similar to previously derived models for multielectrode arrays [22, 23]. In this case, the three-dimensional forward model of Eq (6) reduces to (see S1 Text for derivation) (10) The prior in this case specifies the space of functions g(y, z, t) with two spatial dimensions and one temporal dimension. We note that the form of the forward model is similar to existing two-dimensional forward models for slightly different measuring devices such as Utah arrays [11], but with some differences due to the a priori physical model we have assumed for the Neuropixels probe.

CSD prior model.

In Eq (4), the noise ϵ is modeled as zero-mean Gaussian, independent over space and time, with variance σ² (though extensions to spatially or temporally varying noise variances are easily included without complicating the computational framework). Details of the transient mean μ^(r) = μ^(r)(s′, t) differ depending on the application, so specific choices will be discussed in context; in the remainder of this section, we will assume μ^(r) is known and that it absorbs the non-stationary activity. The η processes are then modeled as independent zero-mean Gaussian processes that are stationary in time and space, with realizations being independent across trials. To complete the model specification, we assume the spatiotemporal covariance for the sum of the slow and fast processes is separable in space and time. That is, the covariance at (s, t) and (s′, t′) decomposes as (11) Compared with a non-separable covariance structure, separability greatly reduces the number of free parameters in the model and yields large savings in computation time. As we show in our simulation and real-data results, it remains possible to fit spatially distinct sources that have different profiles in time. While in principle, processes at multiple time scales may not be identifiable, we distinguish between the slow and fast processes using priors on the lengthscale parameters.

One of the advantages of our generative model framework is that various spatial and temporal covariance functions can be used to include different prior beliefs about the underlying process, including its smoothness in space and time or periodicity; an overview of commonly-used covariance functions is given in [25], Ch. 4. In our simulations and data analysis, we use the following covariance function structure. The spatial covariance function is a unit variance squared exponential, or SE, with one lengthscale for each of the D spatial dimensions: The SE covariance function reflects the prior belief that the CSD is smooth over space with the lengthscale ℓ_s,d governing the frequency of variation along spatial dimension d. The slow-timescale temporal covariance function is also SE, with representing the marginal variance of the slow-timescale process and ℓ_t,2 representing the frequency of variation. The fast-timescale exponential covariance function can capture rougher, faster variations, and has marginal variance and lengthscale ℓ_t,1. Note that the spatial covariance function has unit variance to avoid identifiability issues with the marginal temporal variances.

Denoting by K_{s,t;s′,t′} the spatiotemporal covariance matrix of the combined fast and slow CSD processes evaluated for vectors of spatial and temporal locations s, s′, t, and t′, the covariance matrix has the following structure: Exploiting the covariance matrix structure allows faster matrix inversion that would be possible on a general spatiotemporal covariance matrix [26].

Joint distribution of CSD and LFP.

We have now specified the generative model framework for the CSD in addition to forward models relating the CSD to the LFP for one-, two-, and three-dimensional recordings. Before discussing how the CSD can be predicted using this model or how to fit the model, we give an overview of what the generative model and forward model imply about the joint distribution of the latent CSD and the observed LFPs.

Assuming we are working with LFPs measured in one or two spatial dimensions, our generative model provides the form of g(s, t) describing the spatial and temporal variation of the CSD in the measured dimensions. The generative CSD model gives g as a Gaussian process with mean function μ and covariance function , which we denote as Much as in the finite-dimensional case, the application of a linear operator to a Gaussian process results in another process that is jointly Gaussian with the original process. We write the bilinear operator corresponding to , when applied to k^s, as , so that the marginal noiseless LFP process is given by See Eq (S.7) in S1 Text

The joint Gaussian process governing the CSD and LFP processes implies that if we have noisy observations of at a discrete set of spatial locations s and temporal locations t, then the joint distribution of the vectorized observations, denoted , and the vectorized CSD process observed at an arbitrary set of locations s′ and t′, denoted g_s′,t′, is multivariate Gaussian: (12) where . Note that Eq (12) refers to the joint distribution of CSD and LFP processes on a single trial, for notational clarity; to complete the model across trials, we assume the trials are independently and identically distributed. The linear operator notation indicates application of the linear operator jointly to both inputs of the covariance function used to compute K_s,s, while and indicate application of the linear operator to only the first or second inputs, respectively. Details of these calculations are given in S1 Text. The joint distribution over discretely observed LFPs and the latent CSD process at any spatial and temporal coordinates of interest is used for tuning model parameters and for prediction of the CSD.

Estimation of model parameters.

Using properties of multivariate Gaussian distributions, Eq (12) implies that the marginal density of the observations for trial r, defined as (13) is available in closed form: (14) Taking all N_r trials to be independent and identically distributed, we obtain the following log marginal likelihood function for the model parameters : (15) where depends implicitly on the covariance hyperparameters and on the forward model hyperparameter R. Note that direct inversion of Σ is costly and potentially unstable, but can be improved by exploiting the Kronecker product structure (S1 Text).

To estimate the model parameters, we use the log marginal posterior, which combines the log marginal likelihood with prior information about the model parameters: (16) While a fully Bayesian approach such as MCMC could be used to approximate the full posterior, we instead use maximum a posteriori (MAP) estimation by maximizing the logarithm of Eq (16) with respect to θ; in this case, the use of priors can be seen as a form of regularization on the log marginal likelihood that discourages unrealistic or unidentifiable parameter values. In our implementation, we use inverse Gamma priors for the lengthscales in which the prior hyperparameters are chosen so that the 1% and 99% quantiles will fall at specific values, typically corresponding to the minimum and maximum observed distances in space or time [27]. We also use a similar inverse Gamma prior for the forward model parameter R, and use broad half-Normal priors for the marginal variances and the noise variance [27].

Predicting the CSD.

Given a fixed mean function and fixed values for model parameters for the covariance functions and forward model, predictions conditional on the observed values of can be made for any s′, t′ for either ϕ or g; typically, we will be mostly interested in predicting the latent CSD described by g. Using properties of multivariate Gaussians, Eq (12) yields the following distribution for the CSD conditioned on the observed LFPs: The conditional mean, which we use to predict g_s′,t′ given , is (17) We will use the acronym GPCSD to refer to the process of using the Gaussian process conditional mean to predict the CSD at some set of spatial locations after tuning model parameters.

The structure of the covariance function also permits separate prediction of the fast- and slow-timescale components. Let . Then the conditional mean decomposes as where the second term corresponds to the fast-timescale prediction and the third term corresponds to the slow-timescale prediction.

Performance on simple CSD dipole configuration.

The first set of simulation results demonstrates the ability of the GPCSD method to recover simple dipole-like CSD patterns even in the presence of noise, and shows qualitative differences between GPCSD, tCSD, and kCSD. We generated a simple CSD template comprised of two positive and two negative Gaussian-shaped bumps across one spatial and one temporal dimension, then passed it through the forward model to obtain a noiseless LFP profile; noisy versions of the LFPs were also generated by adding white noise. The GPCSD method was applied with the mean function μ assumed to be zero and standard priors used for all estimated parameters, which included the forward model parameter R. We note that only a single realization of the spatiotemporal LFP was used for estimation, demonstrating that GPCSD can be applied to single-trial data without repetitions. Traditional CSD was applied directly to the observed LFPs at each time point, while kCSD was applied with the forward model parameter R set to the ground-truth value and the other tuning parameters (basis width and noise variance) chosen by cross-validation over a two-dimensional grid of values using the Python toolbox kCSD for 1D CSD estimation [10, 11]. Because different CSD methods recover the CSD pattern up to some multiplicative constant, the true CSD and the CSD predictions were rescaled to have maximum absolute value equal to 1, similar to [28].

Fig 1A and 1B show the ground-truth CSD, noiseless LFP, and CSD predictions for tCSD, GPCSD, and kCSD, with the CSD predicted from the noiseless LFP in the top row (A) and from the noisy LFP in the bottom row (B). In both cases, GPCSD reconstructed the ground truth accurately, though there were some small-amplitude artifacts due to the use of a stationary covariance function on a nonstationary ground-truth pattern. Much more severe artifacts were present in the tCSD predictions, even in the noiseless case, and the performance of both tCSD and kCSD clearly degraded when white noise was added to the LFP while GPCSD recovered the pattern free of noise.

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Fig 1. Comparing GPCSD to other methods on simulated CSD dipole.

(A) From left to right, ground-truth noiseless LFP, ground-truth CSD pattern, traditional CSD (tCSD) prediction, GPCSD prediction, and kernel CSD (kCSD) prediction. (B) Same as top row, but with noise added to the ground-truth LFP before CSD prediction. GPCSD accurately recovers the pattern compared to tCSD, and appears more robust to LFP noise than either tCSD or kCSD.

https://doi.org/10.1371/journal.pcbi.1009601.g001

Quantifying performance on repeated trials.

To quantify the accuracy of GPCSD relative to the other methods, we generated multiple realizations of spatially one-dimensional CSDs from zero-mean spatiotemporal Gaussian process models, then passed these CSDs through the forward model to obtain LFPs. The generated LFP trials were split into training and testing sets, each of size 50; the training set was used for selecting GPCSD and kCSD model parameters and the test set was used to evaluate the performance of each method in reconstructing the true CSD. We again used the Python toolbox kCSD for 1D CSD estimation [10, 11]. Selected kCSD and GPCSD parameters are listed in Methods and materials. Because tCSD can only estimate the CSD at the interior electrode positions, we compared methods based on CSD predictions at these locations only. For each test set trial, we computed the mean squared error (MSE) across all predicted space-time points for each trial. The MSEs averaged across trials were 7.38 × 10⁻⁵, 4.64 × 10⁻⁵, and 0.046 for GPCSD, kCSD, and tCSD, respectively. Paired t-tests indicated that GPCSD performed significantly better than tCSD (p << 0.0001) but there was weaker evidence that kCSD outperformed GPCSD (p = 0.0003). We note that if the GPCSD hyperparameters were fixed to their true values, the paired t-test for the difference in mean MSE between kCSD and GPCSD failed to detect a difference (t = 0.071, p = 0.94), suggesting that in this simulation, kCSD and GPCSD performed very similarly, but that results may depend on GPCSD hyperparameter optimization. In S1 Fig, we also show that the spatial distribution of error was different for kCSD and GPCSD, with kCSD tending to have higher errors at the edge relative to GPCSD and GPCSD tending to have higher errors in the center of the array than kCSD. Future work could explore this issue further, similar to [29]. Additionally, we show in S1 Text section “Additional simulation results” that GPCSD performed well in a similar simulation study but with the Gaussian process model mis-specified relative to the generating model.

Application to auditory LFPs from laminar probes.

The auditory LFP data was collected from two simultaneously recording laminar probes inserted 3mm apart in primate primary auditory cortex (A1). The first probe was located centrally in A1 and the second probe was located more medially and closer to the boundary of A1 with the medio-lateral belt, so we will refer to the probes as the lateral and medial probes, respectively. The medial probe had lower response threshold, shorter multi-unit activity (MUA) latencies, and overall stronger current sinks and sources than the lateral probe. The recordings contained 2,509 trials in response to a collection of 11 pure tones with varying inter-stimulus intervals (ISIs) and fundamental frequencies on each trial; see Methods and materials for a full description of the data. For each probe, we estimated putative cortical depth based on examination of the LFP and spiking activity. For interpretation of the results, each electrode contact was assigned to one of three depth levels (superficial, medium, or deep). First, we investigated post-stimulus spectral power and phase coupling based on GPCSD predictions, assuming a zero-mean Gaussian process, from the steady LFP activity (activity during each trial but with the average evoked LFP response subtracted out prior to GPCSD modeling). Second, to study correlations in transient activity, we estimated the CSD evoked response by first predicting the CSD during the trial using a fixed GPCSD model with hyperparameters estimated using the baseline pre-trial data, then taking the average across trials to obtain an evoked CSD pattern. We used spatially and temporally localized CSD components of the evoked response to detect correlated trial-to-trial variation in evoked response timing.

Auditory steady activity. To analyze steady activity, we first subtracted the LFP average evoked response (mean across trials) from each trial, then used the baseline period (100 ms before tone onset until tone onset) to estimate GPCSD model parameters for a zero-mean process separately for each probe. We then predicted both the CSD and noiseless LFP at the original electrode positions and at time points from tone onset to 500ms after tone onset.

We computed a periodogram for each trial separately for the fast and slow timescales, then averaged the resulting periodograms across trials. Fig 2A shows the trial-averaged periodograms for each timescale (solid: fast timescale, dashed: slow timescale) for both the CSD and LFP at six different depths along the lateral probe (results were similar for the medial probe). Dotted horizontal lines indicate boundaries between superficial, medium, and deep layers. For the fast timescale processes, the CSD and LFP periodograms were similar, but there were discernible differences, with the CSD periodograms exhibiting greater variation across depths. There were clear peaks around 10 Hz at several depths, with CSD seeming to be relatively stronger in the middle depth, around 1300 microns, and again in a deeper layer, around 1900 microns. Fig 2B displays power (relative to maximum) for the 10Hz frequency band for CSD (red) and LFP (blue). Again, while the CSD and LFP 10 Hz power plots remain similar, the distinctions across depths are more clearly visible in the CSD plot. Fig 2C shows example time courses for a single trial and a single electrode in the middle cortical layers for both CSD and LFP, decomposed into slow and fast components. While the temporal properties of the slow and fast components appear similar in both CSD and LFP, there are some differences in the values due to the spatial deconvolution of the CSD.

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Fig 2. Spatial, spectral, and temporal properties of LFP and GPCSD.

(A) Periodograms (averaged across trials) showing power spectra at six cortical depths along the lateral probe for CSD (left) and LFP (right), computed for estimated fast-timescale processes (solid) and slow-timescale processes (dashed). Horizontal grey dotted lines indicate approximate boundaries between superficial, medium, and deep cortical layers. (B) Relative 10Hz power for CSD (red) and LFP (blue) as a function of cortical depth. (C) Time courses for a single trial for an electrode in the middle cortical layers, broken into slow, fast, and total, for CSD (left) and LFP (right). While the temporal components were similar in LFP and CSD, they did exhibit differences due to the different spatial properties of the CSD.

https://doi.org/10.1371/journal.pcbi.1009601.g002

Next, we assessed phase coupling for oscillations centered at 10 Hz by using a bandpass filter centered at 10 Hz along with the Hilbert transform to extract instantaneous phases. In both the CSD and LFP, the mean pairwise PLV across all electrode locations (both within-probes and between-probes) appeared to increase monotonically from before the stimulus until 100 ms after stimulus, then remained high and nearly constant until about 300 ms after stimulus, so we selected a time point during the later period (250 ms after stimulus) to investigate phase coupling. We used torus graphs to construct a multivariate phase coupling graph describing connectivity among all 48 nodes (24 from each probe). The overall test of the null hypothesis of no edges between the two probes was significant for both the LFP and CSD phases (p < 0.0001). Fig 3A shows connectivity matrices for within- and between-probe connections, with edges colored by edgewise log₁₀ p-value. Horizontal dashed lines indicate the separation between superficial, medium, and deep cortical layers. Within-probe, most connections tended to be the near the diagonal, particularly for the LFP, where there was strong evidence for edges mostly along the diagonal. By inspection of the lower right quadrant of Fig 3A, we also note that the medial probe CSD indicates a large number of strong connections from its deep to its superficial layers. Across-probes, the torus graph based on LFP had very few edges, while CSD torus graph had many edges, with a noticeable spatial structure to the edge pattern. Fig 3B shows the between-probe connectivity matrix in graphical form, with edges shown for edgewise p < 0.01 (Bonferroni corrected for the number of edges tested). Most connections between probes occurred between the same depths on each probe, but there was also very strong evidence for phase locking between the lateral probe deep layers and the medial probe superficial layers. While this result, taken in the context of deep-to-superficial edges within the medial probe, may suggest cross-layer connections between the medial and lateral areas, we note that the underdetermined nature of CSD estimation implies that the activity assigned to the deepest layers could potentially be coming from a deeper brain structure. Fig 3C shows a simplified version of the graph, taking only the center electrode in each depth range as the superficial (S), medium (M), and deep (D) nodes. In this graph, the edges are colored by the lower bound of a 95% bootstrap confidence interval on the partial PLV value, a statistic that falls between 0 and 1 and, much like PLV, quantifies the strength of the phase coupling. The strongest edge connected lateral probe deep layers to medial probe superficial layers.

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Fig 3. Phase coupling graphs in auditory CSD and LFP.

(A) Results of edgewise torus graph phase coupling inference both within- and across-probe for CSD (left) and LFP (right); depth along each probe is increasing left to right and top to bottom, and dashed lines indicate approximate boundaries between superficial, medium, and deep cortical layers. Colored entries correspond to edges, with color representing the log₁₀ of the p-value. Within-probe, the LFP had edges primarily along the diagonal, while the CSD contained more edges, including some connections across superficial, medium, and deep layers. Between probes, the CSD torus graph contained a noticeable set of edges, primarily along the diagonal, while the LFP torus graph had very few edges between probes. (B) Graph showing significant between-probe CSD torus graph edges, with lateral probe nodes ordered by depth in the left column and medial probe nodes ordered by depth in the right column; dashed lines indicate approximate boundaries between superficial, medium, and deep cortical layers. Many of the cross-probe connections occurred near the same depth on both probes, though there appear to be some edges connecting lateral probe deep layers to medial probe superficial layers. (C) Simplified graph between superficial (S), medium (M), and deep (D) cortical layers. Edge color corresponds to a 95% bootstrap confidence interval lower bound for the partial PLV value (reflecting coupling strength, which falls between 0 and 1). The strongest cross-probe connection was between the deep layers of the lateral probe and the superficial layers of the medial probe.

https://doi.org/10.1371/journal.pcbi.1009601.g003

Auditory transient activity. In the steady activity analysis, we assumed that a single evoked response (estimated by the mean across trials) was shared across all trials and that any residual variation was not part of an evoked response. However, it is more likely that the transient evoked response varies from trial to trial, and this variation could indicate a different type of neural communication than the steady activity. While previous work has demonstrated that trial-to-trial time lags in LFPs can be recovered on a coarse spatial scale based on steady state recordings [31], here we investigate trial-to-trial time lag variation in transient, nonstationary activity resolved to a fine spatial scale through the estimated CSD. With the steady activity GPCSD model parameters fixed to the values estimated from the pre-stimulus baseline data, we first estimated a CSD evoked response function shared across all trials, then separated it into multiple CSD evoked components to investigate trial-to-trial timing and variation of specific evoked components localized in space and time.

To estimate the latent CSD mean function shared across all trials, we first estimated the CSD timecourses on each trial using the GPCSD model with parameters estimated from the baseline period, then took the mean across trials to obtain an evoked CSD. To assess trial-to-trial variation, we separated the fitted CSD mean function into multiple CSD components, non-overlapping in space and time, by applying image segmentation techniques (see Methods and materials). The center of mass of the absolute value of each component was used as a marker of the peak time and spatial location of the component. Fig 4 shows the estimated evoked response function for each probe along with the components identified by the segmentation algorithm. The evoked responses and components were similar in time and space between the two probes, but there were some differences in the spatial and temporal profiles. It appears that the medial probe evoked responses started slightly earlier than the lateral probe responses, which is also consistent with the trial-averaged multi-unit spiking activity. Both probes exhibited a current inversion near a depth of 1000 microns that appeared to persist even as the activity fluctuated between positive and negative current over time.

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Fig 4. Estimated CSD evoked response components.

Left to right: Trial-averaged multi-unit activity (MUA) relative to baseline, estimated CSD evoked response, components returned by the image segmentation (colors correspond to arbitrary cluster number). Top row corresponds to the lateral probe and bottom row to the medial probe. The evoked responses for both probes have similar features but slightly different spatial and temporal properties. In particular, both the MUA and CSD evoked responses indicate that the evoked response begins earlier in the medial probe than the lateral probe.

https://doi.org/10.1371/journal.pcbi.1009601.g004

Given the CSD evoked components, we then estimated a per-trial time shift for each component by maximizing the marginal likelihood of the data for each trial conditional on the estimated component shapes and estimated ongoing activity Gaussian process covariance function. The estimated per-trial shifts for all components in both probes had across-trial means at 0 ms and across-trial standard deviations between 1ms and 3ms, depending on the component. To determine components for which the shifts were related across trials, we computed correlations (across trials) between the point estimates of the shifts of each component from both of the probes. Fig 5A shows the components for the lateral and medial probes, with edges connecting the centers of mass of two components when there was significant correlation in the per-component shifts (p < 0.001, corrected; based on Fisher z-transform on the correlation coefficient). Within-probe, it appears that many of the early evoked components (occurring before 60 ms) had correlated time shifts, indicating that time variation in early evoked components was related across trials. Across probes, the component time shifts for most of the early evoked components were related to components near the same depth along the probe, though there were some connections from early lateral probe components to later medial probe components. To quantify lagged relationships between probes in the earliest evoked responses (occurring before 40ms), we display kernel density estimates of the across-trial peak times for the responses in each probe in Fig 5B along with dashed horizontal lines indicating depth boundaries (separating superficial, medium, and deep layers). This figure indicates that across depth, medial probe evoked responses tended to precede lateral probe evoked responses; the differences in the mean peak times for evoked components at similar depths were also significantly different from zero (p < 0.001 corrected). In addition, it appears that in both probes the superficial and deep layers had earlier peak times than the medium layers. The earliest responses in A1 are typically observed in thalamo-recipient layers 4 and deep 3. It appears these responses are somewhat identified in the earliest responses occurring just above the dashed line separating superficial and medium layers.

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Fig 5. Per-trial shifts and correlations in CSD evoked response components.

(A) Spatiotemporal plot of evoked response components for the lateral probe (left) and the medial probe (right), colored to show separate components. Black circles represent centers of mass of each component, and an edge between two black circles indicates significant correlation in the per-trial shifts of the two components (darker, thicker edges indicate larger correlation values). Within each probe, the pattern of connections suggests that many components of the early evoked response (occurring before 60 ms) have related time shifts on a trial-to-trial basis; the lateral probe also has correlated time shifts in the later evoked components. Between probes, there are connections between early evoked components at similar depths, with some evidence of shift correlations between lateral probe early components and medial probe later components. (B) Kernel density estimates of the peak times, across trials, of the evoked components in each probe with dashed lines marking putative cortical depth boundaries (separating superficial, medium, and deep layers). The medial probe responses generally precede the lateral probe responses across depths (confirmed by pairwise testing on the difference in means, p < 0.001 corrected). In addition, the ordering of responses across depths appears similar in each probe, with the earliest responses occurring in the superficial and deep layers, followed by the medium depth layers.

https://doi.org/10.1371/journal.pcbi.1009601.g005

Application to visual LFPs from Neuropixels probes.

The visual cortex data set was obtained from an experiment in which six Neuropixels probes were simultaneously inserted into the left hemisphere of a mouse brain [13]. The probes were targeted to the retinotopic centers of primary visual cortex (V1) and five higher-order visual areas (AM, PM, LM, AL, and RL), and extended down into portions of the hippocampus, thalamus, and midbrain. For each probe, the lower boundary of cortex was identified based on a decrease in the density of detected units approximately 800 μm below the brain surface. We analyzed the LFP data from 150 trials in which a 250 ms flash stimulus was presented to the right visual hemifield. In this data set, we were interested in investigating relationships between V1 and higher-order visual regions [32]; as an example, we analyze phase connectivity between V1 and LM. We first applied the GPCSD method (assuming a zero-mean Gaussian process) to the cortical electrodes from each of the probes to infer the latent CSDs corresponding to steady activity (with the average evoked response subtracted from the LFPs), then used torus graphs [30] to determine phase connectivity. We chose to predict the CSD at locations along the center of the probe that would correspond to the center of each putative cortical layer, depthwise. The Neuropixels probe recording locations, putative cortical visual layer boundaries, and CSD prediction locations are shown in Fig 6A.

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Fig 6. Phase coupling in Neuropixels data.

(A) Neuropixels probe LFP electrode locations (circles) for V1 (left) and LM (right), colored by putative region (VIS: visual cortex, CA: Cornu Ammonis, DG: dentate gyrus, TH: thalamus, N-L: no label). Putative cortical layer boundaries are overlaid on the red visual area electrodes with layer numbers indicated along the right side. Along the center of the probe are the locations we chose for CSD estimation (yellow diamonds), with one location centered in each cortical layer. (B) Torus graph phase coupling graphs based on theta oscillations at two time points relative to the stimulus. Edges shown for torus graph p < 0.0001. Edge color indicates the lower bound of a 95% bootstrap confidence interval on the partial PLV value. It appears the strongest edges were between V1 and LM at similar cortical depths, with some evidence that edges were stronger at 70ms than at 0ms. (C) Similar to B, but for beta oscillations; dashed edges indicate edges with weaker evidence (torus graph p < 0.001). Similar to theta band, the connection patterns were mostly across similar layers but appeared slightly stronger at 70ms compared to 0ms.

https://doi.org/10.1371/journal.pcbi.1009601.g006

For phase coupling analysis, we selected two frequency bands of interest, theta band (centered at 5 Hz) and beta band (centered at 22 Hz). Similar to the torus graph analysis of steady auditory potentials and CSDs, the predicted CSDs were filtered using Butterworth bandpass filters with plus or minus 2 Hz width and the instantaneous phases were extracted for each trial using the Hilbert transform. We chose two time points of interest, 0 ms (stimulus onset) and 70 ms (during the stimulus) to estimate torus graphs. Fig 6B and 6C show the torus graph results, with edges shown for edgewise p < 0.0001 (solid) and p < 0.001 (dashed). To quantify edge strength, we computed the partial PLV statistic, which depends on the torus graph parameters and, like PLV, falls in the range [0, 1]. Furthermore, we recomputed the partial PLV statistic based on fitted torus graphs across 1,000 bootstrap resamplings of the trials to measure uncertainty in the partial PLV statistic. The edges in Fig 6B and 6C are colored by the lower bound of the 95% bootstrap confidence interval. In both frequency bands, the primary connectivity between V1 and LM was between layers at similar depths (e.g., layer 5 to layer 5). For both the theta band (Fig 6B) and beta band (Fig 6C), the connections were similar across time points but appeared stronger after stimulus compared to stimulus onset [33].