Can we infer subthalamic nucleus spike trains from intranuclear local field potentials?

Evaluation of Spike Sorting Algorithms: Application to Human Subthalamic Nucleus Recordings and Simulations

An important prerequisite for the analysis of spike synchrony in extracellular recordings is the extraction of single-unit activity from the multi-unit signal. To identify single units, potential spikes are separated with respect to their potential neuronal origins ('spike sorting'). However, different sorting algorithms yield inconsistent unit assignments, which seriously influences subsequent spike train analyses. We aim to identify the best sorting algorithm for subthalamic nucleus recordings of patients with Parkinson's disease (experimental data ED). Therefore, we apply various prevalent algorithms offered by the 'Plexon Offline Sorter' and evaluate the sorting results. Since this evaluation leaves us unsure about the best algorithm, we apply all methods again to artificial data (AD) with known ground truth. AD consists of pairs of single units with different shape similarity embedded in the background noise of the ED. The sorting evaluation depicts a significant influence of the respective methods on the single unit assignments. We find a high variability in the sortings obtained by different algorithms that increases with single units shape similarity. We also find significant differences in the resulting firing characteristics. We conclude that Valley-Seeking algorithms produce the most accurate result if the exclusion of artifacts as unsorted events is important. If the latter is less important ('clean' data) the K-Means algorithm is a better option. Our results strongly argue for the need of standardized validation procedures based on ground truth data. The recipe suggested here is simple enough to become a standard procedure.

GPU technology as a platform for accelerating physiological systems modeling based on Laguerre-Volterra networks

The use of a GPGPU programming paradigm (running CUDA-enabled algorithms on GPU cards) in biomedical engineering and biology-related applications have shown promising results. GPU acceleration can be used to speedup computation-intensive models, such as the mathematical modeling of biological systems, which often requires the use of nonlinear modeling approaches with a large number of free parameters. In this context, we developed a CUDA-enabled version of a model which implements a nonlinear identification approach that combines basis expansions and polynomial-type networks, termed Laguerre-Volterra networks and can be used in diverse biological applications. The proposed software implementation uses the GPGPU programming paradigm to take advantage of the inherent parallel characteristics of the aforementioned modeling approach to execute the calculations on the GPU card of the host computer system. The initial results of the GPU-based model presented in this work, show performance improvements over the original MATLAB model.

Local field potential driven Izhikevich model predicts a subthalamic nucleus neuron activity

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.

Addition of deep brain stimulation signal to a local field potential driven Izhikevich model masks the pathological firing pattern of an STN neuron

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.