Several models, with various degrees of complexity have been proposed to model the neuronal activity from different parts of the human brain. We have shown before that various modeling approaches, ...including a Hammerstein–Wiener (H–W) model, can be used to predict the spike trains from a deep 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 about the spike times of a single neuron, we identify and extract the model parameters. We then use those parameters to numerically simulate the spike timing, the rhythm and the inter-spike intervals for the rest of the recording. To assess how well the model fits to the measured data we combine measures of spike train 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 that predict the spikes with temporal accuracy ranging, on average, from 53% to more than 80%, depending on the number of the neurons’ spikes recorded. The model’s prediction is adequate for estimating accurately the spike rhythm. Quantitative results establish the model’s validity as a simple yet biologically plausible model of the spike activity recorded from a deep nucleus inside the human brain.
The crucial engagement of the subthalamic nucleus (STN) with the neurosurgical procedure of deep brain stimulation (DBS) that alleviates medically intractable Parkinsonian tremor augments the need to ...refine our current understanding of STN. To enhance the efficacy of DBS as a result of precise targeting, STN boundaries are accurately mapped using extracellular microelectrode recordings (MERs). We utilized the intranuclear MER to acquire the local field potential (LFP) and drive an Izhikevich model of an STN neuron. Using the model as the test bed for clinically acquired data, we demonstrated that stimulation of the STN neuron produces excitatory responses that tonically increase its average firing rate and alter the pattern of its neuronal activity. We also found that the spiking rhythm increases linearly with the increase of amplitude, frequency, and duration of the DBS pulse, inside the clinical range. Our results are in agreement with the current hypothesis that DBS increases the firing rate of STN and masks its pathological bursting firing pattern.
An interesting question has been raised recently regarding the relationship between the local field potentials (LFPs) and the single unit spiking activity. In this study, we investigate whether a ...linear modification of the LFPs, acquired from microelectrode recordings inside the subthalamic nucleus (STN) of Parkinson's disease patients, can provide input to an appropriately parameterized Izhikevich model to predict the spikes of an STN neuron. We show that the model is able to predict both the exact timing and the rhythm of the recorded spikes with high accuracy in 5 out of 7 intranuclear single neuron recordings. For the rest of the models, one model shows a lower accuracy in predicting the rhythm and the second one shows a lower accuracy in predicting the timing of the spikes. Overall, the results dictate that the LFPs can reliably predict the occurrence of spikes.
The subthalamic nucleus (STN) is one of the subcortical nuclei that constitute the basal ganglia and a pivotal point of their function and dysfunction. In this paper, we use intranuclear recordings, ...acquired intraoperatively during deep brain stimulation procedure, to investigate whether it is possible to infer STN spike trains using only the underlying local field potentials (LFPs). We regard the LFPs to be the input and the spikes to be the output of a simple Hammerstein-Wiener model and we show that STN spikes can indeed be inferred from intranuclear LFPs, at least with moderate success. Our model, although not always reliable when predicting exact spike positions, shows a good accuracy in predicting the up to 1 kHz structure in STN spike trains. Hence, intranuclear LFPs can indeed hold useful information for predicting STN spiking activity.
In this paper, we discuss the use of a nonlinear cascade model to predict the subthalamic nucleus spike activity from the local field potentials recorded in the motor area of the nucleus of ...Parkinson's disease patients undergoing deep brain stimulation. We use a segment of appropriately selected and processed data recorded from five nuclei to acquire the information of the spike timing and rhythm of a single neuron and estimate the model parameters. We then use the rest of each recording to assess the model's accuracy in predicting spike timing, rhythm, and interspike intervals. We show that the cumulative distribution function (CDF) of the predicted spikes remains inside the 95% confidence interval of the CDF of the recorded spikes. By training the model appropriately, we prove its ability to provide quite accurate predictions for multiple-neuron recordings as well, and we establish its validity as a simple yet biologically plausible model of the intranuclear spike activity recorded from Parkinson's disease patients.
During the last decade, the subthalamic nucleus (STN) has drawn the attention of clinicians and researchers becoming the main target for high-frequency deep brain stimulation of the basal ganglia in ...patients suffering from movement disorders such as dystonia or Parkinson's disease. Currently, a great effort is made both in Medicine and Neurosciences for understanding and modeling in details how the STN cells work. The aim of this work is the development of a phenomenological model of an STN neuron that receives the local field potentials as its input and outputs a spike train. The model is physiologically inspired by the temporal summation observed in neurons. After estimating the model's numerical parameters and stochastic properties by fitting the available intranuclear data, we show that the model can predict with reasonable accuracy single spikes as well as the presence of bursts by following closely the recorded spiking rhythm.
Extracellular recordings in the area of the subthalamic nucleus (STN) of Parkinson's disease patients undergoing deep brain stimulation comprise fast events, Action Potentials and slower events, ...known as Local Field Potentials (LFP). The LFP is believed to represent the synchronized input into the observed area, as opposed to the spike data, which represents the output. We have shown before that there is an input-output relationship between these two components in the STN. In the present paper, we extend these observations by using LFP-driven Volterra models and the Laguerre expansion technique to estimate nonlinear dynamic models which are able to predict the recorded spiking activity. To this end, we rigorously examine the optimal model order. The improved performance of the second-order Volterra models indicates that there is a nonlinear relationship between the LFP and the spiking activity. To obtain a more compact and readily interpretable model, the most significant dynamic components of the identified Volterra models are extracted using principal dynamic mode analysis.
The subthalamic nucleus (STN) is a pivotal point of the basal ganglia function and dysfunction. Its crucial engagement with the neurosurgical procedure of deep brain stimulation (DBS) that alleviates ...medically intractable Parkinsonian tremor augments the need to refine our current understanding of it. Intranuclear recordings, received from a patient during surgery, are processed to acquire both local field potentials (LFPs) and the spiking activity of the cells inside the nucleus. Under the physiologically justified assumption that the LFPs constitute the input of the cells inside the nucleus whereas the spikes are the output, an input output system analysis of the STN is straightforward. Primary results show that a non-linear Hammerstein-Wiener model is able to simulate the STN output with a reasonable accuracy.
Deep brain stimulation (DBS) is considered a surgical treatment alternative for Parkinson disease (PD) patients with intractable tremor or for those patients who are affected by long-term ...complications of levodopa therapy such as motor fluctuations and severe dyskinesias. However, the susceptible accuracy of placement of the DBS electrode inside the brain nucleus determines the therapeutic efficacy of the method. Unlike normal cases, untreated Parkinsonian states in basal ganglia structures produce oscillations at various frequencies, the most prominent of which is a synchronization in the beta frequency band recorded in the subthalamic nucleus (STN). The actual frequency range and the strength of the beta peak vary among patients. We propose that an on-line spectral analysis of the population activity, as evidenced by microelectrode recordings (MERs) could form a neurophysiological biomarker for confirmation of the on-target placement of the electrode within the STN.
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