Spiking Laguerre Volterra networks-predicting neuronal activity from local field potentials

Objective.Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations.Approach.Here, we fill this gap by proposing novel spiking Laguerre-Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics.Main results.The proposed networks were applied on extracellular microelectrode recordings of Parkinson's Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features.Significance.These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.

Machine learning-based high-frequency neuronal spike reconstruction from low-frequency and low-sampling-rate recordings

Recording neuronal activity using multiple electrodes has been widely used to understand the functional mechanisms of the brain. Increasing the number of electrodes allows us to decode more variety of functionalities. However, handling massive amounts of multichannel electrophysiological data is still challenging due to the limited hardware resources and unavoidable thermal tissue damage. Here, we present machine learning (ML)-based reconstruction of high-frequency neuronal spikes from subsampled low-frequency band signals. Inspired by the equivalence between high-frequency restoration and super-resolution in image processing, we applied a transformer ML model to neuronal data recorded from both in vitro cultures and in vivo male mouse brains. Even with the x8 downsampled datasets, our trained model reasonably estimated high-frequency information of spiking activity, including spike timing, waveform, and network connectivity. With our ML-based data reduction applicable to existing multichannel recording hardware while achieving neuronal signals of broad bandwidths, we expect to enable more comprehensive analysis and control of brain functions.

Impaired Timing of Speech-Related Neurons in the Subthalamic Nucleus of Parkinson Disease Patients Suffering Speech Disorders

BACKGROUND

Our previous study found degradation to subthalamic neuronal encoding of speech features in Parkinson disease (PD) patients suffering from speech disorders.

OBJECTIVE

To find how timing of speech-related neuronal firing changes in PD patients with speech disorders compared to PD patients without speech disorders.

METHODS

During the implantation of deep brain stimulator (DBS), we recorded the activity of single neurons in the subthalamic nucleus (STN) of 18 neurosurgical patients with PD while they articulated, listened to, or imagined articulation of 5 vowel sounds, each following a beep. We compared subthalamic activity of PD patients with (n = 10) vs without speech disorders.

RESULTS

In this comparison, patients with speech disorders had longer reaction times and shorter lengths of articulation. Their speech-related neuronal activity preceding speech onset (planning) was delayed relative to the beep, but the time between this activity and the emission of speech sound was similar. Notwithstanding, speech-related neuronal activity following the onset of speech (feedback) was delayed when computed relative to the onset. Only in these patients was the time lag of planning neurons significantly correlated with the reaction time. Neuronal activity in patients with speech disorders was delayed during imagined articulation of vowel sounds but earlier during speech perception.

CONCLUSION

Our findings indicate that longer reaction times in patients with speech disorders are due to STN or earlier activity of the speech control network. This is a first step in locating the source(s) of PD delays within this network and is therefore of utmost importance for future treatment of speech disorders.

Toward a closed-loop deep brain stimulation in Parkinson's disease using local field potential in parkinsonian rat model

Deep brain stimulation (DBS) is an invasive method used for treating Parkinson's disease in its advanced stages. Nowadays, the initial adjustment of DBS parameters and their automatic matching proportion to the progression of the disease is viewed as one of the research areas discussed by the researchers, which is called closed-loop DBS. Various studies were conducted regarding finding the signal(s) which reflects different symptoms of the disease. Local Field Potential (LFP) is one of the signals that is suitable for using as feedback, because it can be recorded by the same implemented electrodes for stimulation. The present study aimed to identify the distinguishing features of patients from healthy individuals using LFP signals.

METHODS

In the present study, LFP was recorded from the rats in sham and parkinsonian model groups. After evaluating the signals in the frequency domain, sixty-six features were extracted from power spectral density of LFPs. The features were classified by Support Vector Machine (SVM) to determine the ability of features for separating parkinsonian rats from healthy ones. Finally, the most effective features were selected for distinguishing between the sham and parkinsonian model groups using a genetic algorithm.

RESULTS

The results indicated that the frequency domain features of LFP signals from rats have capacity of using them as a feedback for closed-loop DBS. The accuracy of the Support Vector Machine classification using all 66 features was 80.42% which increased to 84.41% using 38 features selected by genetic algorithm. The proposed method not only increase the accuracy, but it also reduce computation by decreasing the number of the effective features. The results indicate the significant capacity of the proposed method for identifying the effective high-frequency features to control the closed-loop DBS.

CONCLUSIONS

The ability of using LFP signals as feedback in closed-loop DBS was shown by extracting useful information in frequency bands below and above 100 Hz regarding LFP signals of parkinsonian rats and sham ones. Based on the results, features at frequencies above 100 Hz were more powerful and robust than below 100 Hz. The genetic algorithm was used for optimizing the classification problem.

Evidence for a task-dependent switch in subthalamo-nigral basal ganglia signaling

Basal ganglia (BG) can either facilitate or inhibit movement through excitatory and inhibitory pathways; however whether these opposing signals are dynamically regulated during healthy behavior is not known. Here, we present compelling neurophysiological evidence from three complimentary experiments in non-human primates, indicating task-specific changes in tonic BG pathway weightings during saccade behavior with different cognitive demands. First, simultaneous local field potential recording in the subthalamic nucleus (STN; BG input) and substantia nigra pars reticulata (SNr; BG output) reveals task-dependent shifts in subthalamo-nigral signals. Second, unilateral electrical stimulation of the STN, SNr, and caudate nucleus results in strikingly different saccade directionality and latency biases across the BG. Third, a simple artificial neural network representing canonical BG signaling pathways suggests that pathway weightings can be altered by cortico-BG input activation. Overall, inhibitory pathways (striato-pallidal-subthalamo-nigral) dominate during goal-driven behavior with instructed rewards, while facilitatory pathways (striato-nigral and subthalamo-pallidal-nigral) dominate during unconstrained (free reward) conditions.

Basal ganglia can both facilitate or inhibit movement through excitatory and inhibitory pathways; however whether these opposing signals are dynamically regulated during behavior is not known. Here the authors use multinucleus LFP recordings and electrical microstimulation in monkeys performing saccade based tasks to show task specific changes in the tonic weighting of these pathways.

Closed-loop firing rate regulation of two interacting excitatory and inhibitory neural populations of the basal ganglia

This paper develops a new closed-loop firing rate regulation strategy for a population of neurons in the subthalamic nucleus, derived using a model-based analysis of the basal ganglia. The system is described using a firing rate model, in order to analyse the generation of beta-band oscillations. On this system, a proportional regulation of the firing rate reduces the gain of the subthalamo-pallidal loop in the parkinsonian case, thus impeding pathological oscillation generation. A filter with a well-chosen frequency is added to this proportional scheme, in order to avoid a potential instability of the feedback loop due to actuation and measurement delays. Our main result is a set of conditions on the parameters of the stimulation strategy that guarantee both its stability and a prescribed delay margin. A discussion on the applicability of the proposed method and a complete set of mathematical proofs is included.

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.

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

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.

Complexity and multifractality of neuronal noise in mouse and human hippocampal epileptiform dynamics

Fractal methods offer an invaluable means of investigating turbulent nonlinearity in non-stationary biomedical recordings from the brain. Here, we investigate properties of complexity (i.e. the correlation dimension, maximum Lyapunov exponent, 1/f(γ) noise and approximate entropy) and multifractality in background neuronal noise-like activity underlying epileptiform transitions recorded at the intracellular and local network scales from two in vitro models: the whole-intact mouse hippocampus and lesional human hippocampal slices. Our results show evidence for reduced dynamical complexity and multifractal signal features following transition to the ictal epileptiform state. These findings suggest that pathological breakdown in multifractal complexity coincides with loss of signal variability or heterogeneity, consistent with an unhealthy ictal state that is far from the equilibrium of turbulent yet healthy fractal dynamics in the brain. Thus, it appears that background noise-like activity successfully captures complex and multifractal signal features that may, at least in part, be used to classify and identify brain state transitions in the healthy and epileptic brain, offering potential promise for therapeutic neuromodulatory strategies for afflicted patients suffering from epilepsy and other related neurological disorders.

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.