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Parameter identification for a local field potential driven model of the parkinsonian subthalamic nucleus spike activity



Contents lists available at journal homepage: Parameter identification for a local field potential driven model of theParkinsonian subthalamic nucleus spike activity Kostis P. Michmizos Damianos Sakas , Konstantina S. Nikita a Massachusetts Institute of Technology, Cambridge, MA, USAb Evangelismos Hospital, Department of Neurosurgery, National and Kapodistrian University of Athens, Athens, Greecec National Technical University of Athens, Athens, Greece Article history: Several models, with various degrees of complexity have been proposed to model the neuronal activity Received 18 October 2011 from different parts of the human brain. We have shown before that various modeling approaches, Received in revised form 9 October 2012 including a Hammerstein–Wiener (H–W) model, can be used to predict the spike trains from a deep Accepted 10 October 2012 nucleus, the subthalamic nucleus, using the underlying local field potentials. In this article, we present,in depth, the various choices one has to make, and the limitations that they introduce, during the H–W model parameter identification process. From a segment of the recorded data, which contains information Subthalamic nucleus modeling about the spike times of a single neuron, we identify and extract the model parameters. We then use those Nonlinear modelMicroelectrode recordings parameters to numerically simulate the spike timing, the rhythm and the inter-spike intervals for the rest Parkinson's disease of the recording. To assess how well the model fits to the measured data we combine measures of spiketrain synchrony, namely the Victor–Purpura distance and the Gaussian similarity measure, with time-scale independent train distances. We show that a wise combination of metrics results in models thatpredict the spikes with temporal accuracy ranging, on average, from 53% to more than 80%, depending onthe number of the neurons' spikes recorded. The model's prediction is adequate for estimating accuratelythe spike rhythm. Quantitative results establish the model's validity as a simple yet biologically plausiblemodel of the spike activity recorded from a deep nucleus inside the human brain.
2012 Elsevier Ltd. All rights reserved.
brain are carried by volleys of spikes arriving almost simultane-ously to multiple dendrites of a neuron Understanding information flow inside the brain pathways and These volleys give rise to low frequency (below 150 Hz) how this is altered in the case of a disease is a basic endeavor postsynaptic activities in neurons, and are believed to comprise a in several different fields. Understanding information flow inside significant part of a particular class of electrophysiological signals, the central nervous system building block, the neuron, is one of namely the local field potential (LFP) the fundamental problems of neurophysiology; the development of neuromodulation surgical procedures, such as deep brain stim- A question arises about whether one can use an LFP-driven ulation (DBS) that alleviates symptoms of motor related diseases, model to accurately simulate the brain activity in terms of spike requires a thorough understanding of both how the neuron nor- timing and rhythm. That predictive relation between LFPs and mally functions and how this may best be introduced to a biophys- spikes, per se, is starting to attract great attention. Spike predic- ically accurate brain model. The work presented here takes part in tion has been firstly examined in the primary visual cortical area an attempt to bind together two of the most important hypotheses (V1) using extracellular microelectrode recordings (MERs) in anes- on neurons' language; the ‘‘ruling school of thought'' which bases thetized monkeys all descriptions of the brain on the average spike rates, treated A recent study in the primary somatosensory cortical area as functions of time, and the, now growing attention, aggregate (S1) of rats using a combination of intracellular and extracellu- dendritic activity according to which the meaningful signals in the lar MERs reveals an analogous interconnection between LFPs andspikes In addition to proving that spikes can be predicted by the area's LFP, the opposite is also sug- Correspondence to: Massachusetts Institute of Technology, Room 3-147, 77 gested: some of the local properties of the LFPs are predicted by Massachusetts Avenue, Cambridge MA 02139-4307, USA. Tel.: +1 617 253 8114, +1857 756 4599; Mob: +1 857 756 4599, +1 857 756 4599.
the spiking activity of a few or even a single neuron in the V1 E-mail address: (K.P. Michmizos).
of anesthetized monkeys 1 Tel.: +30 210 772 2285; fax: +30 210 772 2320.
Using MERs from the subthalamic nucleus (STN) of Parkinson's 0893-6080/$ – see front matter 2012 Elsevier Ltd. All rights reserved.
K.P. Michmizos et al. / Neural Networks 36 (2012) 146–156 disease (PD) patients, we have shown that the spike activity of a generation decision. This cascade model is referred to as the deep brain nucleus can also be predicted from the LFPs. Various Wiener model.
nonlinear models are used as spike predictors, including a Ham- As any current flowing through a resistor results in a voltage merstein–Wiener (H–W) model drop, the input signal (LFP and noise) is attenuated as it propagates through the dendrites towards the soma. For a dendrite to be mod- a modified LFP-driven Izhikevich model eled as a (linear) resistor, strict assumptions such as constant di- a simple bi-exponential model ameter and homogenous distribution of ion channels are required.
and a Volterra model Yet dendrites are complicated structures and the observed volt- age attenuation, due to known and unknown dendritic processes, The development of brain models, either using biophysically is far from being regarded as linear. The nonlinear voltage drop accurate Hodgkin–Huxley-type neuron models or computationally was introduced to the model with a second nonlinear static pro- efficient integrate-and-fire neuron models, usually remains on cess before the Wiener system. The overall system was a Hammer- a large-scale level. Consequently, in most of the experimentally stein–Wiener model to be mathematically presented in Section verified models of the basal ganglia (BG) (e.g., Processing and analysis of the MERs to be used in simulations is presented in Section the STN, which is of interest in this study, is a peripheral structurewith its neurons modeled very simplistically. In fact, only few 2.1. Data acquisition computational models of the STN neuron itself currently existand are mainly based on anatomical data Data were taken from recordings partially presented elsewhere Although the STN is believed to possess a pivotal role in BG Extracellular MERs, recorded from three unmedicated, awake PD patients, during DBS implantation, were included in the present study. Recordings were acquired during pe- its underlying networking riods in which the patient lay down immobile in the operational ta- mechanisms are still partly unknown ble, prior to DBS permanent lead implantation. MERs began above The extraordinary rich and varied range of firing patterns the estimated upper border of the STN and allowed for a complete that the STN cells are able to produce have sampling of the neuronal activity of the STN. They were obtained at not been faithfully modeled yet.
0.5 mm (or fewer) steps through the STN to the substantia nigra us- Given the importance of the STN and the possible use of the ing the LeadpointTM Software, Medtronics Functional Diagnostics LFP-driven models as test beds for current hypotheses on the effect S/A. For each electrode depth, 10 s recordings, digitized at 24 kHz, of DBS on the cellular level we now were simultaneously acquired from 5 electrodes positioned in a seek to formalize the evidential modeling paradigm to a concise cross (Ben-Gun) formation. The distance between any peripheral identification scheme for the selection of the optimal parameters electrode and the central one was 2 mm. After visual inspection for for MER-specific H–W models. Herein we propose a prediction- excluding MERs with artifacts or without the typical discharge pat- error minimization method to estimate a set of H–W models tern of an STN neuron that predict the recorded spikes from the LFPs acquired from the the signals were low-pass filtered, using an FIR least squares same or adjacent electrodes. To select the best possible model, we filter with a cut off frequency of 6 kHz, order of 1300 and peak to propose a two-step validation procedure: an exploratory analysis peak (p–p) rippling equal to 8 × 10−9 db. The filtered signal was based on time-independent metrics is followed by a confirmatory then downsampled to 12 kHz for computational convenience.
analysis. The latter uses single-valued metrics derived from time- A filter bank with 2 filters was applied to each MER to acquire dependent measures that estimate the degree of synchrony the LFP and spike signals. To acquire the LFP signal, VL, an FIR between spike trains. By looking at the distribution of the spike equiripple LP filter with a pass-band of [0 170] Hz and a stop- rhythm residuals, we were able to show that the proposed band of [0.22 12] kHz was used. To acquire the spike signal, Vs, an identification procedure results in optimal models.
FIR equiripple band-pass (BP) filter with stop-bands of [0 430] Hzand [1.55 6] kHz and a pass-band of [0.48 1.5] kHz was used. Bothfilters had an order of 2351 samples, a p–p rippling of less than 2. Materials and methods
2 × 10−7 db and a constant over all frequencies phase delay of0.615 rad. Spikes were identified from the Vs, using amplitude Although being regarded as a ‘‘black-box'' model, the H–W thresholding The absolute value of the threshold model has some physiological analogies between its structures was typically chosen equal to 3SD of the distribution of all the sam- and central parts of a neuron. The biological foundation of the ples in the noisy spike signal, excluding the 1 ms spike segments.
model is discussed in detail in Briefly, Qualitatively, this means that a V there are evidences that LFPs recorded inside the STN reflect s peak was considered as a spike if its amplitude was almost 1.5 the noise level of the recorded sig- synchronized aggregate activity of local STN neurons. In other nal (95% confidence interval). This value assured that the peak sur- words, a significant component of the recorded LFP arrives from passed the noise level enough for an observer to be able to visually a narrow STN area and hence can be used as a predictor for the verify the existence of a spike, but did not eliminate the possibil- STN spikes. Our hypothesis is further supported by experimental ity of ‘‘hiding'' some spikes inside the noise signal, especially for findings in cortical neurons according to which a large part of multi-neuron MERs. As a final processing step, V the LFP signal can be reconstructed from just a single neuron s was binarized by placing a 1 at the timing of each detected spike, defined by its max- spike recording For modeling the LFP signal processes that take place inside the cell's body and particularlyin the axon hillock, a linear time invariant (LTI) system was used.
Conversely, generation of spikes is a strongly non-linear process 2.2. The H–W model as a neuron model since an abrupt change in the membrane potential precedes theopening or closing of ion channels. Thus, the LTI system was The mathematical presentation of a lumped parameters H–W followed by a nonlinear static module used to model the spike model follows. If k = n · 1t, n ∈ N and 1t is the sampling period,


K.P. Michmizos et al. / Neural Networks 36 (2012) 146–156 such a H–W model is represented in Z space by v(k) = f (u(k)) z−1 + · · · + γ znb v(k) = v(k) a′ + β′z−1 + · · · + γ ′znf y(k) = h(w(k)) where u ∈ is the input (the LFP and the background noise) to the entry module of the model. Input u is passed through thenonlinear mapping f (u) to give the input v ∈ dynamic block (Eq. In Eq. parameters α, β, . . , γ , and α′, β′, . . , γ ′ are the polynomial coefficients for the numerator, B,and the denominator, F , of the linear dynamic block, respectively,nb and nf being the order of B and F , respectively. In Eq. w ∈ R is the output of the linear block which is passed through thenonlinear mapping h(w) to give the output y p R of the model.
2.3. Identification of the H–W model Fig. 1. Schematic representation of the identification steps for a MER-specific
model.
Recording-specific models were identified using an iterative w, included a percentage, p, of the samples before the time of the prediction-error minimization method. The nonlinear functions peak and 1 − p of the samples after it. Selection of p and w will f and h and the LTI were adjusted to the data to minimize the be discussed in Section Each of the training pairs was visu- simulation error signal, expressed as ally inspected to avoid the presence of a second spike in the train- ing pair. In single-neuron MERs, selection of the LFP-spike training (y (u, t) − ϕ(t))2dt pairs was random and a model of a single neuron was estimated. In the multi neuron category, we applied K -means clustering via prin- where ϕ was a scalar function representing the binary spike signal cipal component analysis to relate each spike to a specific neuron.
over time, t. The integral was discretized using the Gaussian Then the pairs were chosen in analogy to the percentage of each quadrature formula and Eq. was formulated neuron's spikes present in the recording. For example, for a MER in as a least squares problem: which 2 different neurons were firing with almost equal rhythm, the training data would be composed of half pairs that belong to F (u) = min f (u) = the first neuron followed by the other half pairs belonging to the i) − ϕ(ti))2 second neuron. In other words, in multiple-neuron MERs, a modelwas estimated, as if there was a single neuron firing all the spikes where y and ϕ included the weights of the quadrature scheme and that we recorded from the 2 -or more- neurons. The steps for pa- the interpolating function, x, is a polynomial of degree 2m − 1 or rameter estimation are summarized in less. The residual ∥F (u)∥ was likely to be small at the optimum, Typically, model orders and delays were selected by trial and if we had set realistically achievable target trajectories. Follow- error until a model that produced a satisfactory fit to the data was ing initialization, the optimum model parameters (Eqs. found. A satisfactory model predicted at least 50% of the recorded were estimated using a prediction-error minimization method as spikes in a 1 s segment of the validation data, without fabricating follows. If we denote the m-by-n Jacobian matrix of F (u) as J(u), more than 10% of non-existent spikes. Piecewise linear functions, the gradient vector of f (u) as G(u), the Hessian matrix of f (u) as known to provide the means to represent any nonlinear function, H(u), and the Hessian matrix of each Fi(u) as Hi(u), we have were used to approximate both f and h. The number of linear G(u) = 2J(u)T F (u) segments was kept equal to 10. Thus, only nb and nf needed to beestimated a priori. The ranges of their values were [0, 3] and [1, 3], H(u) = 2J(u)T J(u) + 2Q (u), respectively. Consequently, 12 H–W models were identified, using where Q (u) = m F the same LFP-spike training pairs.
i(u)Hi(u). The matrix Q (u) has the prop- erty that when the residual ∥F (u)∥ tends to zero as u Two iterative searching algorithms were used to estimate the k approaches the solution, then Q (u) also tends to zero hold Thus model parameters (Eqs. under the assumption that the when ∥F (u)∥ was small at the solution, a Gauss–Newton direction parameters remained constant over the time of the recording. Pa- could be used for the optimization procedure rameters were initially set to 0 and a maximum of 50 iterations Hence, a search direction, d of the adaptive Gauss–Newton prediction-error minimization k, was obtained at each iteration, k, that was a solution of the linear least-squares problem: method with no robustifi-cation, were used to train the model (Eqs. The iterative dk = min ∥J(uk) − F (uk)∥2.
search was terminated when the expected improvement of the pa- rameter values was less than 0.01%. When the second-order termQ (u) is significant, the Gauss–Newton method does not result into 2.4. Parameter estimation process a valuable model. This was the case when the model was overpa-rameterized or the data was not informative enough and the as- Each model was trained in a predefined MER segment with sumption of invertibility for input and output nonlinear functions a process that depended on the number of different neurons did not hold (see This situation was identified when recorded. The first 0.5 s for each [VL, VS] pair was detained for either dk went below e−15 or when the condition number of J(uk) model training and the remaining 9.5 s of the data were used was below e−10 and the Levenberg–Marquardt method for validation. For each spike present in the first 0.5 s of the VS, was chosen as the iterative searching al- a window was applied to both VL and VS. The window, of length gorithm. The condition number of a matrix is the ratio of the largest


K.P. Michmizos et al. / Neural Networks 36 (2012) 146–156 singular value to the smallest. The estimated model parameters foreach LFP-spike pair were becoming the initial values for the nextrepetition of the identification procedure (model refinement).
2.5. Validation metrics of spike train synchrony To quantify the performance of our proposed models, we sought to characterize the agreement between y and ϕ by meansof the timing of individual spikes. Even in single-neuron MERs,recorded STN neurons exhibited a highly variable firing patternranging from bursting to pacemaker mode whereas spikes fromirregularly firing neurons were also present. Since no particularcoding pattern could be assumed, general measures suited foran exploratory analysis were followed by a set of redundantsingle-valued metrics for a confirmatory analysis. Specifically,we combined parameter-free and time-independent approacheswith the most widely used time-scale dependent measures. These Fig. 2. Schematic representation of the single-valued metrics derived from the V–P
measures were complementary in nature and spanned throughout distance (normalized spike distance metric). The estimated V–P distance betweenpredicted and recorded spikes (thick line) is compared to that between a random the entire spectrum of measuring spike synchrony, from spike prediction with the same spike rate (dotted line). For each prediction, the temporal coincidence detectors to rate comparison.
resolution in which we found the Spike Coincidence (SC) and Spike Rate (SR) points General parameter-free and time-independent metrics inclu- and the Prediction Area (PA) determine the quality of the prediction. The V–P ded (a) the plot of the empirical cumulative distribution function distance for random prediction was averaged over 30 V–P distances estimated fromrandom spike trains.
(CDF) of ϕ against the CDF of y (b) a sorted interspike interval (ISI) plot for ϕ and y interval for which two spikes can be considered to be related by and (c) the rhythm jitter; otherwise they better are deleted or inserted. The similarity estimation in fixed size bins. For maximizing the CDF's temporal measure uses a smoothing process with a controllable temporal resolution, we defined the coincidence window as the minimum size, τ = 1 sample. If the model accurately describes the observed Both y and ϕ are convolved with a Gaussian function of a spiking data, then the empirical and model CDFs should roughly standard deviation ˆ σ , which essentially determines the temporal coincide, and the plot follows a 45° line. If the model fails to ac- resolution used in the comparison. A large value means low count for some aspect of the spiking behavior, then that lack of fit temporal resolution and a small value means high temporal is reflected in the plot as a significant deviation from the 45° line.
resolution. In this study, ˆ σ varied from 0.5 to 6.7 ms. After For the ISI method, the predicted sorted ISIs were plotted against smoothing the binary signals to continuous signals, we estimated those acquired by ϕ. These plots were used for visualizing which the correlation coefficient, r, as a function of ˆ σ. For each recording, ISIs of ϕ were well captured by the model and which were not.
r was averaged over 9 trials of simulations lasting 1 s each.
Such comparisons are especially useful in testing the ability of the The selection of an optimal time-scale for the time-dependent model to predict characteristics of an STN neuron such as the onset metrics is not a trivial task as it results from the interplay of and the recession of a bursting spike train. To compare y's rhythm, many different factors such as the distribution of the spikes in Ry, with ϕ's rhythm, Rϕ, a bin width was estimated using Freed- the MERs and the degree of similarity between the predicted and man Diaconis rule For all MERs, we recorded spikes (in analogy to Herein, we calculated the h = 2 IQR(x) , where h is the bin width, IQR is the propose single-valued metrics derived from the V–P metric that inter-quartile range of the data x and n is the number of data sam- ‘‘integrate'' the quality of a prediction. For each real recording, ples and we selected the median width (50 ms) as a universal bin we estimate the V–P distance between the real spikes and those width. Although these methods are preferable in applications to of the prediction, V–Ppredict, as well as between the real spikes real data for which there is no validated knowledge about the rele- and those of a random spike train, V–Prandom. The random spike vant time scales, they require visual validation. More importantly, train had the same number of spikes and duration with those of they do not allow for the functional characterization and precision the real spike train with its spikes uniformly distributed across analysis offered by the time-dependent methods.
the timespan of the real recording. The intersections between Consequently, to give an objective and comparable estimate V–Ppredict and V–Prandom defined the range of the time scales for of neuronal variability, we used 2 time-scale depended measures which the prediction could not be regarded as random For of spike train (dis)similarity, namely the Victor–Purpura (V–P) each V–P metric, there are two time-scale related points: a Spike distance and the Schreiber et al. similarity measure. One of Coincidence (SC) point below which any spike costs less if it is the main arguments for using such measures is their potential deleted/inserted than shifted and a Spike Rate (SR) point above insight into the precision of the neuronal code which the V–P metric is as low as possible Yet, a poor The V–P distance prediction can still result to a very small (large) SC (SR) point; e.g.
between two spike trains is when a neuron predicts to fire randomly with a rhythm equal to calculated by minimizing the cost of transforming one spike train the average rhythm of the real recording. That is why we propose into the other by means of 3 basic operations: (a) spike deletion the Prediction Area (PA) between the two V–P metric curves as (cost 1); (b) spike addition (cost 1); and (c) spike shift by an interval an estimator of the overall quality of the prediction In δt (cost q δt ). The cost per time unit, q, sets the time scale of the particular, we calculated the ratio of the two areas below the analysis. As q → 0 (no cost) the distance is equal to the difference curves, RoA = Areapredict/Arearandom. An accurate prediction will in the number of spikes. As q → ∞, it is cheaper to delete and increase the area between the two curves and hence will make create spikes than to shift a spike and so the distance approaches RoA < 1. Please note that an RoA also gives an indication of a bad the number of non-coincident spikes. Thus, by increasing the cost, rate prediction: when the average predicted rhythm is not equal to the V–P distance gradually transforms from a rate distance to a the real one, the lower asymptote of V–Ppredict is y = x, with x > 0 temporal distance. Roughly speaking, 2/q is the time-difference and that can make RoA > 1.




K.P. Michmizos et al. / Neural Networks 36 (2012) 146–156 ed LFP (SDU) -0.2 Fig. 3. Spike–LFP relationship for electrodes positioned inside the STN of 2 patients (a) STA-LFP. Amplitude is in standard deviation (SD) units. Superimposed (thick grey
lines) are the STAs computed for all LFPs from the same nucleus. (b) PSD estimate via Welch's method for the LFP on its left. (c) STA-LFP from a different patient. SD of the
resulting STA was calculated as in B. Superimposed (thick grey lines) are the STAs computed as in A. (d) Same as B.
3. Results
and 0.3, respectively. A typical value of w = 120 samples andp = 0.5 was used throughout the simulations in this study. For Computer simulations were performed to evaluate the identi- the LFPs, power spectral density (PSD) was also estimated using fication methodology and the validation metrics. Thirty one MERs, the Welch's method. LFPs were divided into 8 sections with 50% acquired from the sensorimotor areas of 5 STN (3 patients), were overlap, each section was windowed with a Hamming window and the modified periodograms were averaged. PSDs of the MERsunder study revealed that there is a characteristic – for PD – beta 3.1. Spike triggered average of the STN-LFP band peak and (d)) in the LFPs. Such a peak was absent inrecordings acquired from outside the STN borders (p < 0.05).
Local neuronal synchronization was determined using the spike-triggered average (STA) of the LFP. The relationship between 3.2. Single neuron spike prediction STN spike activity and the underlying LFPs is visualized in The processing of MERs, acquired from 2 subjects, is plotted. Data Validation results after each step of the identification process in upper figures received from Subject 2, left STN, central electrode, are shown in for a single neuron MER. Data received from 1 mm below the theoretical target. Data in bottom figures received Subject 3, left STN, lateral electrode, 1.5 mm below theoretical tar- from Subject 3, left STN, lateral electrode, 1.5 mm below theoretical get. Seven LFP-spike pairs, with w = 120 samples and p = 0.5, target. For each spike, we obtained the LFP segment for a short time were recruited as the training data. Models were validated with re- window (±0.15 s lag) around that spike and then we averaged over spect to the mean rhythm mean squared error, MSE, and 2 metrics, those LFP segments. For significance levels, ISIs were randomized namely the r and the RoA (see Section A 3rd order polyno- and the SD of the resulting STA was calculated. Any components mial was fitted to the data in a least squares sense and overlaid on of the LFP that were not consistently phase locked to the spikes each plot. The general trend of the parameterization of the LTI sys- averaged out and are not visible in the STA. If spike times had tem, for each MER, could then be assumed. In this example, models no temporal relation to the LFP activity of surrounding neurons, with nb < nf resulted in a poor spike prediction. On the contrary, fluctuations in the LFP would average out during STA compilation, models with M2 , n resulting in a flat STA. The STA-LFP fluctuation indicates that spike ≤ 2 predicted the spike activity with great times have a reliable temporal relation to the LFP. In and accuracy (MSE < 0.75, r ≥ 0.8 and RoAs ≤ 0.6). Other models, (c), one notes a sharp negativity at spike position (at zero lag) e.g. M3, took advantage of the refinement process and stabilized and a prominent upswing for negative and positive time lags, i.e., their performance near a constant value for each metric. This was right before and after a spike occurred. The negative spike in the also observed in most of the models in which nb + nf ≥ 3. That STA as a response to spikes was common among electrodes and being said, an over-parameterized model resulted in a poor prog- patients. The presence of the zero-lag peak in all recordings is an nosis (e.g. M3 and M3). Thus, to avoid overfitting the model to the indication of a linear relation between spikes and LFPs, since the training data, we kept the smallest possible parameter values that STA is equivalent to computing the cross-correlation between the gave good results. In the case presented here, one of the M2 models binary output and the input (LFP) (for a proof see was selected as the optimal model.
The STA fluctuation Four of the sequentially estimated M2 models, shown in duration (<0.02 s) and the zero lag STA peak restricted w to be are presented in A visual comparison of the prediction less than 240 samples and p equal to 0.5, respectively. But in for one of them (the ‘‘third'' M2) reveals the model's ability to practice, w and p were found sufficient to be as low as 90 samples predict the overall structure of the spike train The


K.P. Michmizos et al. / Neural Networks 36 (2012) 146–156 Fig. 4. Identification procedure for single neuron MER. M b
symbolizes a H–W model with a linear component of n b zeros and (nf + 1) poles. For each model, the same 7 training LFP-spike pairs were used, in the same order.
Fig. 5. Modeling example (a)–(c) 4M2 models, identified for the first, second, third and fifth training pairs. (d) The raw MER (up) and the second M2 model's prediction
(bottom). (e) The general, time-independent validation metrics: Rhythm estimation (up), CDF plot (middle) and sorted ISI plot (bottom). (f) The time-dependent metrics:Correlation coefficient of smoothed versions of y and ϕ (up) and V–P distance between y and ϕ with respect to time scale (bottom).
exact onsets and offsets of the bursts were somewhat inaccurate in model's ability to detect the presence of a spike after an extended the prediction. Nonetheless, even smaller bursts and single spikes absence of spikes is somehow limited. This means that the model were closely predicted, although no clear mark in the LFP signal might not be totally accurate in predicting single spikes, especially could be seen with the naked eye. There were also occasions when the neuron is firing after a quiescent epoch. However, this where spikes were simply missed (7.10–7.20 s) or fabricated was not always the case, since 12 of 16 single-neuron models (3.25–3.35 s). The model was able to predict the presence of both predicted the ISI accurately (MSE-ISI was lower than 0.65 ms).
lonely spikes and bursts with great accuracy. The great accuracy The RoA was estimated equal to 0.58 and r approached of the model was reflected in the general, time-independent the asymptote of y = 0.8 at a time resolution of 4 ms; the V–P comparison using the estimated rhythms, the CDFs and the ISIs metric remained very low (<0.2) until a temporal resolution of The CDF plot showed that the model displayed a lack 6 ms and the SC point was found at a temporal resolution of 1 ms of fit only for the last second. Nevertheless, model values lie in (12 samples).
the border of the 95% confidence bounds. The maximum distance The accuracy of the model to predict the spikes using LFPs between model values and the 45° line was 7 spikes (at 7.55 s). A recorded from the same electrode was also proven true when using similar insufficiency of the optimum models was found in 4 of 16 LFPs acquired from adjacent electrodes. In the V–P distance single-neuron models. An observable small discrepancy between for predictions for two spike trains is shown. The predictions were the large values of ISIs means that that the particular made by optimal M2 models driven by LFPs from posterior and K.P. Michmizos et al. / Neural Networks 36 (2012) 146–156 Fig. 6. Prediction of spikes using LFPs acquired from adjacent electrodes for (a) 1-neuron, and (b) 2-neuron MERs. Spikes received from central electrode for both cases. For
the prediction, the LFPs from Central, Posterior and Anterior electrodes were used. (left). Periodogram estimation for each of the LFPs used. (right) The V–P distance metric
with respect to the time scale, for each of the predictions.
anterior electrodes whereas spikes were acquired from the central electrode. The raw recordings from adjacent microelectrodes were Single-neuron models estimated from MERs received from five nuclei (threepatients). Numerator vector B = [b spike-free as the microelectrodes were not close enough to any 1 , b2, . . , bk] and denominator vector F [f1, f2, . . , fk] represent the polynomial coefficients of the numerator and the neuron. All LFPs were acquired inside the STN. For both cases, denominator of the LTI system, respectively.
the posterior electrode was placed inside the sensorimotor area Hemisphere Recording LTI module [Numerator] (beta band peak present) but the anterior electrode was not (as identifier [Denumerator] indicated by the presence of alpha rhythm and the absence of beta band energy). The first order statistics of the impedances [0, 1] [1, −0.649] of the microelectrodes used in this study are µ = 1447 k and σ = 52 k. Hence the interfering input (i.e. the input for which the [1, −1.764, 0.781] microelectrodes were unintentionally responsive) was not altered.
−0.5 (Posterior) [0, 1] [1, −0.956] Yet, when the LFP recording electrode is 2 mm away from the spike [0, 1] [1, −0.451] [0, 1] [1, −0.987] recording electrode, the modifying input is expected to increase.
The accuracy of the prediction was lowered moderately when LFPs [0, 1] [1, −0.068] [0, 1] [1, 0.866] from other electrodes were used, compared to the accuracy of −1.0 (Posterior) [0, 1] [1, −0.949] the model estimated from the same electrode Specifically, [0, 1] [1, −0.279] for the first prediction, the RoA increased from 0.80 (center LFP- [0, 1] [1, −0.871] driven model) to 0.89 and 0.90 for posterior and anterior LFP- [0, −0.952, 1] [1, 0.859] driven models, respectively. For the second prediction, the RoA [0, 0.962, 1] [1] increased from 0.87 (center-LFP driven model) to 0.91 and 0.92 for [0, −0.999, 1] [1] posterior and anterior LFP-driven models, respectively.
−1.5 (Posterior) [0, −40.970, 1] [1] Cumulative results with models estimated from single-neuron [0, −0.523, 1] [1] MERs help to establish the validity of the identification procedure.
[0, −1.075, 1] [1, −0.979] Each model was estimated and validated separately Themodel's predictability was a function of both the quality of MER By looking at the SC and SR points, we verified that models (observability of the spikes) and the validation metrics used.
estimated from single neuron MERs predicted the spike rhythmbetter than those estimated from 2-neuron recordings. The last 3.3. Multi-neuron spikes prediction were slightly better compared to the models estimated from3-neuron recordings. The average SC point for single neuron The identification scheme was also tested and found to be predictions was found at 0.57 ms (±0.12 ms) whereas that for effective in MERs exhibiting spikes received from an ‘‘area'' of double and triple neuron MER predictions was found at 0.17 ms neurons. A typical 2-neuron MER might have high amplitude (±0.24 ms) and 0.05 ms (±0.17 ms), respectively. The average SR spikes received from a burster neuron very close to the electrode point for single neuron predictions was found at 5.0 s (±0.5 s), and low amplitude spikes received from a farther apart neuron. A whereas that for double and triple neuron MER predictions was comparison between the V–P distance for optimal single-neuron found at 1.0 s (±0.5 s) and 0.8 s (±0.3 s), respectively. The rhythm and multiple neuron models is shown in For a temporal predictability of the models can be verified by looking at the V–P resolution ranging between 2.9 ms and 112 ms, the V–P distance distance for large time scales (>1 s). All (16) single-neuron, 7 out in single-neuron MERs predictions was significantly lower than of 10 2-neuron and 4 out of 5 3-neuron models predicted the the V–P distance for 2 or 3-neuron MERs (two-sample t-test, p < rhythm very accurately (V–P distance <0.1). The remaining models predicted the rhythm with V–P distance = 0.2 (±0.07).
K.P. Michmizos et al. / Neural Networks 36 (2012) 146–156 approaches in which simple linear interaction between LFP andspike signals is tested (e.g., with means of coherence or correlationthe approach used in our study exploited thepattern of the LFP in a nonlinear way to infer spike activity.
A reasonable argument against our proposed modeling ap- proach is that spikes could be mainly predicted by a low-frequencycomponent remaining in the LFP after low-pass filtering the spik-ing signal. Firstly, if the spike predictions were due to low-frequency ‘‘spike-remains'', one could not use the LFPs fromelectrode-j to predict the spikes from an electrode-i (i ̸= j), asshown in Secondly, other models that, unlike the H–Wmodel, are not data-driven (e.g. the Izhikevich model and the bi-exponential model were also successful in predicting the spikes fromLFPs acquired from MERs in the same data set. These models do Cumulative V–P distance for single-neuron and multi-neuron models.
not rely on the input–output relationship as the H–W model does.
Normalized spike distance metric is shown. The grey area indicates the time Thirdly, other researchers resolution for which there is a significantly different amplitude between V–Pdistance metric for single neuron models and double (triple) multi neuron models.
have tried a different approach to prove that the pre- Black lines indicate average V–P distance.
diction is not due to a spike artifact: they excluded the 1 ms areaaround a spike and they interpolated the LFP with a sample-signal 3.4. Model residuals distribution found close to each spike. They concluded that model performancewas superior when spikes were not removed. As our signals' energy By looking at the probability density function of the residuals, was located in the >100 Hz area (and hence our prediction relied one can tell whether the assumptions made were reasonable and on the fast changes in the LFP signal) and we aimed for the best if the choice of model was appropriate. Residuals can be thought possible model, we did not follow this approach.
of as elements of variation unexplained by the fitted model. If Whether the spike prediction is due to physiological mech- we ensure that a model is able to simulate the neuronal spike anisms or to pathophysiological changes in the STN cells mer- rhythm with a reasonable accuracy, the residual rhythm Ry Rϕ its further clarification. Spikes are predicted by cortical LFPs in must follow a Gaussian distribution. In cumulative results, healthy animal models From a neurophysiolog- acquired for all our estimated models of 1-, 2 and 3-neuron MERs ical point of view, the STN is a ‘‘close'' nucleus (please see Section are shown. On each probability histogram, we superimpose the 2.A from Hence, it seems reasonable to say corresponding theoretical distribution having first order statistics that the general formulation is related to the physiology of the STN that were estimated from the raw residuals. Gaussian distribution neurons and not to PD. Whether our current modeling scheme can of residuals was common among recordings and nuclei for bin be expanded to other neurons (e.g. in Vim and SNr) requires more widths <50 ms. This is theoretically justified since it is known that clinical data than those we currently have.
a superposition of Gaussian distribution signals results in a signal The good predictability of our models may be, at least partly, having the same (Gaussian) distribution.
attributed to the existence of coincidence-detector neurons foundinside the STN By comparing the STA-LFPs, one can deduce if two neurons respond to the same inputwith the same response (spike). No two STA-LFPs were identical, Our simulation results confirm that the proposed identification yet they all had a common pattern: a deep low oscillation very scheme, guided by a combination of spike-timing validation close to when the spike occurred followed and preceded by high- metrics, succeeded in finding a data-driven model of the neural rising waves (see Such STA-LFP oscillations were visible only area inside the STN for all 31 MERs in our data set. Unlike other in the electrode from which we acquired the spikes. Oscillations Fig. 8. Cumulative density estimation for residuals of the estimation of the spike rhythm for all models. The theoretical Gaussian distribution with mean and SD equal to
the mean and SD of the residual signal, respectively, is superimposed in each graph.
K.P. Michmizos et al. / Neural Networks 36 (2012) 146–156 were almost identical in frequency but not in amplitude, across and the ISI plot with a single-valued metric. That being said, an MERs. The amplitude variation might depend on both the accurate CDF plot and a somewhat accurate ISI plot could still be electrode–neuron distance and the neuron characteristics. If it was accompanied with a bad (close to 1) RoA value, resulting from a only due to the electrode–neuron distance, the STA-amplitude poor prognosis for the model. That was the case when the predicted would follow the spikes' amplitude which was not the case in our spike rhythm was constant and similar to the mean true spike data set. Hence it seems safe to assume that the LFP-oscillation rhythm. On the other hand, a good (less than 1) RoA value could would increase in amplitude under the presence of two neurons point out an accurate prediction but failed to describe a model with firing simultaneously. Since the LFP signal-to-noise ratio would be mediocre performance, since it missed the information of when the higher, one would expect a better prediction for the neural activity.
model failed to predict correctly the spikes.
Our analysis of the STA-LFP also revealed a deviation of the STA In addition, spike timing accuracy of the models' prediction falls fluctuation duration compared to STAs in previous studies. The with the number of neurons recorded. Training of models with elevated power at beta frequencies (13–35 Hz) of the LFPs received single neuron MERs resulted in very accurate spike predictions.
inside the dorsolateral (motor) area of the Parkinsonian STN at Note that a value of Roa < 0.6 (observed in means, on rest (for a discussion see average, that 90% of the recorded spikes were predicted by the possibly explain those differences in the STA-LFP fluctuation model (with a ±25 sample distance) and at the same time 90% duration, compared to previous STA-LFP analyses. In addition, high of the predicted spikes were also existent in the raw data (with frequency oscillations (>150 Hz), only present in patients with a ±25 samples distance); this is what we define as a 90% accuracy for the model. The means the RoA for models trained and validated were also observable in all MERs (p < 0.01) with single neuron MERs (16 in total) was 0.70; that corresponds (see and (d)). Consequently, the low frequencies (<2 Hz) to models with 80% accuracy, on average. Results are in agreement did not dominate the signal; rather the slow oscillations were with two, unrelated to each other, modeling approaches that modulated in the high amplitude beta (or higher) oscillations.
exploit the LFPs to predict the STN spikes (see Hence, the spectral characteristics of the STN-LFP signals were For multi-neuron MERs (15 different from the ones estimated from the S1 in total), the mean RoA increases to 0.8 for 2-neuron MERs (>61% and the V1 cortical areas. This may explain accuracy) and 0.85 for 3-neuron MERs (>53% accuracy). However, the difference in fluctuation duration between the estimated STA this significant decrease of temporal accuracy in spike predictionmay not only be due to a mediocre model performance. In multi- and (c)) and other STA-LFPs already presented (e.g., Figs.
neuron MERs, some of the spikes of the farthest recorded neuron 1 and 2 of and Fig. 2 of Our are unavoidably covered by the noise level. In a visual observation hypothesis is further supported by studies estimating the STA from of a multi-neuron MER, one could recognize spikes inside the noise high-pass filtered versions of S1 LFPs (see Fig. 4 of level; although their amplitude was smaller than the noise level, its shape was clearly observable. That was not the case for single By training the H–W models with small LFP segments around neuron MERs where even a significant decrease of the amplitude each spike, we aimed at capturing the high frequency component of a spike (usually as a consequence of a bursting firing of that of the LFP. We chose to train the data with small segments of LFPs neuron) would still result in a spike amplitude that would be well- because (a) most of the energy of our signals was concentrated in distinguishable from the noise level. Indeed, for most (13 out of the >100 Hz area and in the β-band area and (b) mul- 15) of the multi-neuron MERs, the resulted model's residuals had tiple small segments allow the refinement of models with spike a larger predicted rhythm than the real rhythm. This argument prediction reaching a plateau fast Oscillations with fre- is further supported by the estimated PDF of the residuals for quencies equal or lower than β-band were not captured by the 3-neuron recordings the number of misdetected spikes in window. That was done on purpose as (a) β-energy was always the right lobe of the Gaussian (over-prediction) was significantly present during the recording (the β-band activity was a prerequi- larger compared to the one in the left lobe. This means that the site to include a MER in our data set) and (b) low frequency com- 3-neuron models either over-predicted the recorded activity or ponents are much more likely traveling from farther neurons (just they predicted some of the spikes that corresponded to recorded like one can only hear the low frequencies of a song played in a spikes lost inside the MER noise.
radio player far away; also see Consequently, millisecond precision, shown to be used for By restricting ourselves to high frequencies, we minimize the mod- temporal coding was not totally safe ifying noise arriving from farther neurons (the spiking activity of to be inferred from LFPs in multi-neuron MERs. But even in those which we do not record) and we focus on the synaptic activity of recordings, we can still assume that, irrespective of whether they the nearest to the electrode neuron(s).
are important for coding, temporal aspects of neural spiking used However, the strengths of modeling are tempered by the for rate coding or coding on spike timing on the scale of 50–150 ms necessary simplifications made in any reasonable model. In our can be moderately well inferred from the LFPs. This is very case, the model does not explicitly take into consideration the crucial since electrophysiological research in the last 25 years has existence of glutamatergic synapses between STN neurons, which provided evidence that individual impulses in the impulse trains have been strongly suggested from each neural cell may sometimes be quite precisely timed even if still debated. But still, the feedback and hence of great importance. For instance, impulse trains from from an STN neuron to one that was being recorded, was implicitly single neurons may show above-chance recurrences of exactly embedded into the model through the LFPs.
timed sequences of several impulses Not a single validation metric was found adequate to fully Moreover, when impulse trains from different neurons recorded characterize the quality of a prediction. For example, the metric at the same time are compared, there may be above chance r, by itself, was not always informative of the quality of the repetitions of exactly timed impulse patterns shared between the prediction. The r depended heavily on the rhythm since a binary different trains. Furthermore, there is increasing evidence that signal with low number of spikes per second has large ‘‘0'' areas. In such exact timing of impulses correlates with behavior these signals, even if the spikes were heavily misdetected, r would or with sensory analysis and there still be large. Such a problem was often revealed by estimating the is increasing acceptance that exactly timed patterns of impulses ISIs That is why, to decide on the optimal model, one may be of general significance in the information processing had to consider a combination of validation metrics such as a CDF accomplished by the brain.
K.P. Michmizos et al. / Neural Networks 36 (2012) 146–156 Ultimately, the model may represent a first step in realizing model able to predict the spikes for a specific electrode, nucleus neural prostheses to replace damaged subregions of the basal or patient was found. Rather, we estimated a MER-specific model.
ganglia that could initiate and control a movement using a brain Hence, the strong dependence of the model parameters (and computer interface. This is in analogy to various central nervous accordingly of spike predictability) on electrode position suggests system neural prostheses currently under development and that the accuracy of such predictions does depend on the recording application. An example is a research study that aims to replace a site inside the STN. Our proposed modeling identification scheme brain region (hippocampus) with a cognitive prosthesis might enable the design of time-adaptive DBS stimulation that can That being said, achieving a clinically viable model that can be tailored to the needs of each individual PD patient. However to predict the electrophysiological function of a region – the motor reach this goal and test the DBS effects on the firing activity, the area in our case – of STN requires more than the H–W model shown model input must be modified accordingly.
here. The model presented in this paper is meant primarily todemonstrate the methodology and the feasibility for capturing thespike timings and the rhythm of a small ensemble of STN neurons.
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