Closed-loop control of the thalamocortical relay neuron's Parkinsonian state based on slow variable

Design of Closed-Loop Control Schemes Based on the GA-PID and GA-RBF-PID Algorithms for Brain Dynamic Modulation

Neurostimulation can be used to modulate brain dynamics of patients with neuropsychiatric disorders to make abnormal neural oscillations restore to normal. The control schemes proposed on the bases of neural computational models can predict the mechanism of neural oscillations induced by neurostimulation, and then make clinical decisions that are suitable for the patient's condition to ensure better treatment outcomes. The present work proposes two closed-loop control schemes based on the improved incremental proportional integral derivative (PID) algorithms to modulate brain dynamics simulated by Wendling-type coupled neural mass models. The introduction of the genetic algorithm (GA) in traditional incremental PID algorithm aims to overcome the disadvantage that the selection of control parameters depends on the designer's experience, so as to ensure control accuracy. The introduction of the radial basis function (RBF) neural network aims to improve the dynamic performance and stability of the control scheme by adaptively adjusting control parameters. The simulation results show the high accuracy of the closed-loop control schemes based on GA-PID and GA-RBF-PID algorithms for modulation of brain dynamics, and also confirm the superiority of the scheme based on the GA-RBF-PID algorithm in terms of the dynamic performance and stability. This research of making hypotheses and predictions according to model data is expected to improve and perfect the equipment of early intervention and rehabilitation treatment for neuropsychiatric disorders in the biomedical engineering field.

Intensity-Varied Closed-Loop Noise Stimulation for Oscillation Suppression in the Parkinsonian State

This work explores the effectiveness of the intensity-varied closed-loop noise stimulation on the oscillation suppression in the Parkinsonian state. Deep brain stimulation (DBS) is the standard therapy for Parkinson's disease (PD), but its effects need to be improved. The noise stimulation has compelling results in alleviating the PD state. However, in the open-loop control scheme, the noise stimulation parameters cannot be self-adjusted to adapt to the amplitude of the synchronized neuronal activities in real time. Thus, based on the delayed-feedback control algorithm, an intensity-varied closed-loop noise stimulation strategy is proposed. Based on a computational model of the basal ganglia (BG) that can present the intrinsic properties of the BG neurons and their interactions with the thalamic neurons, the proposed stimulation strategy is tested. Simulation results show that the noise stimulation suppresses the pathological beta (12-35 Hz) oscillations without any new rhythms in other bands compared with traditional high-frequency DBS. The intensity-varied closed-loop noise stimulation has a more profound role in removing the pathological beta oscillations and improving the thalamic reliability than open-loop noise stimulation, especially for different PD states. And the closed-loop noise stimulation enlarges the parameter space of the delayed-feedback control algorithm due to the randomness of noise signals. We also provide a theoretical analysis of the effective parameter domain of the delayed-feedback control algorithm by simplifying the BG model to an oscillator model. This exploration may guide a new approach to treating PD by optimizing the noise-induced improvement of the BG dysfunction.

Closing the loop of DBS using the beta oscillations in cortex

Cortical information has great importance to reflect the deep brain stimulation (DBS) effects for Parkinson's disease patients. Using cortical activities to feedback is an available closed-loop idea for DBS. Previous studies have demonstrated the pathological beta (12-35 Hz) cortical oscillations can be suppressed by appropriate DBS settings. Thus, here we propose to close the loop of DBS based on the beta oscillations in cortex. By modify the cortico-basal ganglia-thalamic neural loop model, more biologically realistic underlying the Parkinsonian phenomenon is approached. Stimulation results show the proposed closed-loop DBS strategy using cortical beta oscillation as feedback information has more profound roles in alleviating the pathological neural abnormality than the traditional open-loop DBS. Additionally, we compare the stimulation effects with subthalamic nucleus feedback strategy. It is shown that using cortical beta information as the feedback signals can further enlarge the control parameter space based on proportional-integral control structure with a lower energy expenditure. This work may pave the way to optimizing the DBS effects in a closed-loop arrangement.

A Basal Ganglia Computational Model to Explain the Paradoxical Sensorial Improvement in the Presence of Huntington's Disease

The basal ganglia (BG) represent a critical center of the nervous system for sensorial discrimination. Although it is known that Huntington's disease (HD) affects this brain area, it still remains unclear how HD patients achieve paradoxical improvement in sensorial discrimination tasks. This paper presents a computational model of the BG including the main nuclei and the typical firing properties of their neurons. The BG model has been embedded within an auditory signal detection task. We have emulated the effect that the altered levels of dopamine and the degree of HD affectation have in information processing at different layers of the BG, and how these aspects shape transient and steady states differently throughout the selection task. By extracting the independent components of the BG activity at different populations, it is evidenced that early and medium stages of HD affectation may enhance transient activity in the striatum and the substantia nigra pars reticulata. These results represent a possible explanation for the paradoxical improvement that HD patients present in discrimination task performance. Thus, this paper provides a novel understanding on how the fast dynamics of the BG network at different layers interact and enable transient states to emerge throughout the successive neuron populations.

Parkinsonism Differently Affects the Single Neuronal Activity in the Primary and Supplementary Motor Areas in Monkeys: An Investigation in Linear and Nonlinear Domains

The changes in neuronal firing activity in the primary motor cortex (M1) and supplementary motor area (SMA) were compared in monkeys rendered parkinsonian by treatment with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. The neuronal dynamic was characterized using mathematical tools defined in different frameworks (rate, oscillations or complex patterns). Then, and for each cortical area, multivariate and discriminate analyses were further performed on these features to identify those important to differentiate between the normal and the pathological neuronal activity. Our results show a different order in the importance of the features to discriminate the pathological state in each cortical area which suggests that the M1 and the SMA exhibit dissimilarities in their neuronal alterations induced by parkinsonism. Our findings highlight the need for multiple mathematical frameworks to best characterize the pathological neuronal activity related to parkinsonism. Future translational studies are warranted to investigate the causal relationships between cortical region-specificities, dominant pathological hallmarks and symptoms.

Noise-Induced Improvement of the Parkinsonian State: A Computational Study

The benefit of noise in improving the basal ganglia (BG) dysfunctions, especially Parkinsonian state, is explored in this paper. High frequency (≥ 100 Hz) deep brain stimulation (DBS), as a clinical effective stimulation method, has compelling and fantastic results in alleviating the motor symptoms of Parkinson's disease (PD). However, the mechanism of DBS is still unclear. And the selection of the DBS waveform parameters faces great challenges to further optimize the stimulation effects and to reduce its energy expenditure. Considering that the desynchronization of the BG neuronal activities is benefited from the forced high frequency regular spikes driven by standard high frequency DBS, we expect to explore a novel stimulation method that has capability of restoring the BG physiological firing patterns without introducing artificial high-frequency fires. In this paper, a colored noise stimulation is used as a neuromodulation method to disrupt the firing patterns of the pathological neuronal activities. A computational model of the BG that exhibits the intrinsic properties of the BG neurons and their interactions with the thalamic (Th) cells is employed. Based on the model, we investigate the effects of noise stimulation and explore the impacts of the noise stimulation parameters on both relay reliability of the Th neurons and energy expenditure of the stimulation. By comparison, it can be found that noise stimulation does not entrain the network to an artificial high-frequency firing state, but induces the pathological increased synchronous activities back to a normal physiological level. Moreover, besides the capability of restoring the neuronal state, the benefits of the noise also include its balanced waveform to avert potential tissue or electrode damage and its ability to reduce the energy expenditure to 50% less than that of the standard DBS, when the noise stimulation has low frequency (≤ 100 Hz) and appropriate intensity. Thus, the exploration of the optimal noise-induced improvement of the BG dysfunction is of great significance in treating symptoms of neurological disorders such as PD.

Nonlinear predictive control for adaptive adjustments of deep brain stimulation parameters in basal ganglia-thalamic network

The efficacy of deep brain stimulation (DBS) for Parkinson's disease (PD) depends in part on the post-operative programming of stimulation parameters. Closed-loop stimulation is one method to realize the frequent adjustment of stimulation parameters. This paper introduced the nonlinear predictive control method into the online adjustment of DBS amplitude and frequency. This approach was tested in a computational model of basal ganglia-thalamic network. The autoregressive Volterra model was used to identify the process model based on physiological data. Simulation results illustrated the efficiency of closed-loop stimulation methods (amplitude adjustment and frequency adjustment) in improving the relay reliability of thalamic neurons compared with the PD state. Besides, compared with the 130Hz constant DBS the closed-loop stimulation methods can significantly reduce the energy consumption. Through the analysis of inter-spike-intervals (ISIs) distribution of basal ganglia neurons, the evoked network activity by the closed-loop frequency adjustment stimulation was closer to the normal state.

Closed-Loop Modulation of the Pathological Disorders of the Basal Ganglia Network

A generalized predictive closed-loop control strategy to improve the basal ganglia activity patterns in Parkinson's disease (PD) is explored in this paper. Based on system identification, an input-output model is established to reveal the relationship between external stimulation and neuronal responses. The model contributes to the implementation of the generalized predictive control (GPC) algorithm that generates the optimal stimulation waveform to modulate the activities of neuronal nuclei. By analyzing the roles of two critical control parameters within the GPC law, optimal closed-loop control that has the capability of restoring the normal relay reliability of the thalamus with the least stimulation energy expenditure can be achieved. In comparison with open-loop deep brain stimulation and traditional static control schemes, the generalized predictive closed-loop control strategy can optimize the stimulation waveform without requiring any particular knowledge of the physiological properties of the system. This type of closed-loop control strategy generates an adaptive stimulation waveform with low energy expenditure with the potential to improve the treatments for PD.

A neural mass model of basal ganglia nuclei simulates pathological beta rhythm in Parkinson's disease

An increase in beta oscillations within the basal ganglia nuclei has been shown to be associated with movement disorder, such as Parkinson's disease. The motor cortex and an excitatory-inhibitory neuronal network composed of the subthalamic nucleus (STN) and the external globus pallidus (GPe) are thought to play an important role in the generation of these oscillations. In this paper, we propose a neuron mass model of the basal ganglia on the population level that reproduces the Parkinsonian oscillations in a reciprocal excitatory-inhibitory network. Moreover, it is shown that the generation and frequency of these pathological beta oscillations are varied by the coupling strength and the intrinsic characteristics of the basal ganglia. Simulation results reveal that increase of the coupling strength induces the generation of the beta oscillation, as well as enhances the oscillation frequency. However, for the intrinsic properties of each nucleus in the excitatory-inhibitory network, the STN primarily influences the generation of the beta oscillation while the GPe mainly determines its frequency. Interestingly, describing function analysis applied on this model theoretically explains the mechanism of pathological beta oscillations.

Closed-Loop Control of Tremor-Predominant Parkinsonian State Based on Parameter Estimation

A significant feature of Parkinson's disease (PD) is the inability of the thalamus to respond faithfully to sensorimotor information from the cerebral cortex. This may be the result of abnormal oscillations in the basal ganglia (BG). Deep brain stimulation (DBS) is regarded as an effective method to modulate these pathological brain rhythmic activities. However, the selection of DBS parameters is challenging because the mechanism is not well understood. This work proposes the design of a closed-loop control strategy to automatically adjust the parameters of a DBS waveform based on a computational model. By estimating the synaptic input from BG to the thalamic neuron model as feedback variable, we designed and compared various control algorithms to counteract the effects of pathological oscillatory inputs. We then obtained optimal DBS parameters to modulate the tremor-predominant Parkinsonian state. We showed that even a simple proportional controller provides higher fidelity of thalamic relay of sensorimotor information and lower energy expenditure, as compared with classical open-loop DBS. Integral action further enhances DBS performance. Additionally, a positive bias voltage further improves the relay ability of the thalamus with decreased stimulation energy expenditure. These findings were conducive to the development of a more effective DBS to further improve the treatment of the PD.

Neuronal Entropy-Rate Feature of Entopeduncular Nucleus in Rat Model of Parkinson's Disease

The function of the nigro-striatal pathway on neuronal entropy in the basal ganglia (BG) output nucleus, i.e. the entopeduncular nucleus (EPN) was investigated in the unilaterally 6-hyroxydopamine (6-OHDA)-lesioned rat model of Parkinson's disease (PD). In both control subjects and subjects with 6-OHDA lesion of dopamine (DA) the nigro-striatal pathway, a histological hallmark for parkinsonism, neuronal entropy in EPN was maximal in neurons with firing rates ranging between 15 and 25 Hz. In 6-OHDA lesioned rats, neuronal entropy in the EPN was specifically higher in neurons with firing rates above 25 Hz. Our data establishes that the nigro-striatal pathway controls neuronal entropy in motor circuitry and that the parkinsonian condition is associated with abnormal relationship between firing rate and neuronal entropy in BG output nuclei. The neuronal firing rates and entropy relationship provide putative relevant electrophysiological information to investigate the sensory-motor processing in normal condition and conditions such as movement disorders.

Computer-Aided Diagnosis of Parkinson's Disease Using Enhanced Probabilistic Neural Network

Early and accurate diagnosis of Parkinson's disease (PD) remains challenging. Neuropathological studies using brain bank specimens have estimated that a large percentages of clinical diagnoses of PD may be incorrect especially in the early stages. In this paper, a comprehensive computer model is presented for the diagnosis of PD based on motor, non-motor, and neuroimaging features using the recently-developed enhanced probabilistic neural network (EPNN). The model is tested for differentiating PD patients from those with scans without evidence of dopaminergic deficit (SWEDDs) using the Parkinson's Progression Markers Initiative (PPMI) database, an observational, multi-center study designed to identify PD biomarkers for diagnosis and disease progression. The results are compared to four other commonly-used machine learning algorithms: the probabilistic neural network (PNN), support vector machine (SVM), k-nearest neighbors (k-NN) algorithm, and classification tree (CT). The EPNN had the highest classification accuracy at 92.5% followed by the PNN (91.6%), k-NN (90.8%) and CT (90.2%). The EPNN exhibited an accuracy of 98.6% when classifying healthy control (HC) versus PD, higher than any previous studies.

Modeling sequence and quasi-uniform assumption in computational neurostimulation

Computational neurostimulation aims to develop mathematical constructs that link the application of neuromodulation with changes in behavior and cognition. This process is critical but daunting for technical challenges and scientific unknowns. The overarching goal of this review is to address how this complex task can be made tractable. We describe a framework of sequential modeling steps to achieve this: (1) current flow models, (2) cell polarization models, (3) network and information processing models, and (4) models of the neuroscientific correlates of behavior. Each step is explained with a specific emphasis on the assumptions underpinning underlying sequential implementation. We explain the further implementation of the quasi-uniform assumption to overcome technical limitations and unknowns. We specifically focus on examples in electrical stimulation, such as transcranial direct current stimulation. Our approach and conclusions are broadly applied to immediate and ongoing efforts to deploy computational neurostimulation.

Adaptive control of Parkinson's state based on a nonlinear computational model with unknown parameters

The objective here is to explore the use of adaptive input-output feedback linearization method to achieve an improved deep brain stimulation (DBS) algorithm for closed-loop control of Parkinson's state. The control law is based on a highly nonlinear computational model of Parkinson's disease (PD) with unknown parameters. The restoration of thalamic relay reliability is formulated as the desired outcome of the adaptive control methodology, and the DBS waveform is the control input. The control input is adjusted in real time according to estimates of unknown parameters as well as the feedback signal. Simulation results show that the proposed adaptive control algorithm succeeds in restoring the relay reliability of the thalamus, and at the same time achieves accurate estimation of unknown parameters. Our findings point to the potential value of adaptive control approach that could be used to regulate DBS waveform in more effective treatment of PD.

ADAPTATION-DEPENDENT SYNCHRONIZATION TRANSITIONS AND BURST GENERATIONS IN ELECTRICALLY COUPLED NEURAL NETWORKS

A typical feature of neurons is their ability to encode neural information dynamically through spike frequency adaptation (SFA). Previous studies of SFA on neuronal synchronization were mainly concentrated on the correlated firing between neuron pairs, while the synchronization of neuron populations in the presence of SFA is still unclear. In this study, the influence of SFA on the population synchronization of neurons was numerically explored in electrically coupled networks, with regular, small-world, and random connectivity, respectively. The simulation results indicate that cross-correlation indices decrease significantly when the neurons have adaptation compared with those of nonadapting neurons, similar to previous experimental observations. However, the synchronous activity of population neurons exhibits a rather complex adaptation-dependent manner. Specifically, synchronization strength of neuron populations changes nonmonotonically, depending on the degree of adaptation. In addition, single neurons in the networks can switch from regular spiking to bursting with the increase of adaptation degree. Furthermore, the connection probability among neurons exhibits significant influence on the population synchronous activity, but has little effect on the burst generation of single neurons. Accordingly, the results may suggest that synchronous activity and burst firing of population neurons are both adaptation-dependent.

Brain–Computer Interface after Nervous System Injury

Brain–computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous system injury. BCI has been and will continue to be implemented into rehabilitation practices for more interactive and speedy neurological recovery. The most exciting BCI technology is evolving to provide therapeutic benefits by inducing cortical reorganization via neuronal plasticity. This article presents a state-of-the-art review of BCI technology used after nervous system injuries, specifically: amyotrophic lateral sclerosis, Parkinson’s disease, spinal cord injury, stroke, and disorders of consciousness. Also presented is transcending, innovative research involving new treatment of neurological disorders.

Impact of delays on the synchronization transitions of modular neuronal networks with hybrid synapses

The combined effects of the information transmission delay and the ratio of the electrical and chemical synapses on the synchronization transitions in the hybrid modular neuronal network are investigated in this paper. Numerical results show that the synchronization of neuron activities can be either promoted or destroyed as the information transmission delay increases, irrespective of the probability of electrical synapses in the hybrid-synaptic network. Interestingly, when the number of the electrical synapses exceeds a certain level, further increasing its proportion can obviously enhance the spatiotemporal synchronization transitions. Moreover, the coupling strength has a significant effect on the synchronization transition. The dominated type of the synapse always has a more profound effect on the emergency of the synchronous behaviors. Furthermore, the results of the modular neuronal network structures demonstrate that excessive partitioning of the modular network may result in the dramatic detriment of neuronal synchronization. Considering that information transmission delays are inevitable in intra- and inter-neuronal networks communication, the obtained results may have important implications for the exploration of the synchronization mechanism underlying several neural system diseases such as Parkinson's Disease.