Failure of delayed feedback deep brain stimulation for intermittent pathological synchronization in Parkinson's disease

Disrupting abnormal neuronal oscillations with adaptive delayed feedback control

Closed-loop neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinson's disease. However, at the moment, standard stimulation protocols rely on continuous open-loop stimulation and the design of adaptive controllers is an active field of research. Delayed feedback control (DFC), a popular method used to control chaotic systems, has been proposed as a closed-loop technique for desynchronisation of neuronal populations but, so far, was only tested in computational studies. We implement DFC for the first time in neuronal populations and access its efficacy in disrupting unwanted neuronal oscillations. To analyse in detail the performance of this activity control algorithm, we used specialised in vitro platforms with high spatiotemporal monitoring/stimulating capabilities. We show that the conventional DFC in fact worsens the neuronal population oscillatory behaviour, which was never reported before. Conversely, we present an improved control algorithm, adaptive DFC (aDFC), which monitors the ongoing oscillation periodicity and self-tunes accordingly. aDFC effectively disrupts collective neuronal oscillations restoring a more physiological state. Overall, these results support aDFC as a better candidate for therapeutic closed-loop brain stimulation.

Improved control effect of pathological oscillations by using delayed feedback stimulation in neural mass model with pedunculopontine nucleus

BACKGROUND

The role of delayed feedback stimulation in the discussion of Parkinson's disease (PD) has recently received increasing attention. Stimulation of pedunculopontine nucleus (PPN) is an emerging treatment for PD. However, the effect of PPN in regulating PD is ignored, and the delayed feedback stimulation algorithm is facing some problems in parameter selection.

METHODS

On the basis of a neural mass model, we established a new network for PPN. Four types of delayed feedback stimulation schemes were designed, such as stimulating subthalamic nucleus (STN) with the local field potentials (LFPs) of STN nucleus, globus pallidus (GPe) with the LFPs of Gpe nucleus, PPN with the LFPs of Gpe nucleus, and STN with the LFPs of PPN nucleus.

RESULTS

In this study, we found that all four kinds of delayed feedback schemes are effective, suggesting that the algorithm is simple and more effective in experiments. More specifically, the other three control schemes improved the control performance and reduced the stimulation energy expenditure compared with traditional stimulating STN itself only.

CONCLUSION

PPN stimulation can affect the new network and help to suppress pathological oscillations for each neuron. We hope that our results can gain an insight into the future clinical treatment.

Dysregulation of excitatory neural firing replicates physiological and functional changes in aging visual cortex

The mammalian visual system has been the focus of countless experimental and theoretical studies designed to elucidate principles of neural computation and sensory coding. Most theoretical work has focused on networks intended to reflect developing or mature neural circuitry, in both health and disease. Few computational studies have attempted to model changes that occur in neural circuitry as an organism ages non-pathologically. In this work we contribute to closing this gap, studying how physiological changes correlated with advanced age impact the computational performance of a spiking network model of primary visual cortex (V1). Our results demonstrate that deterioration of homeostatic regulation of excitatory firing, coupled with long-term synaptic plasticity, is a sufficient mechanism to reproduce features of observed physiological and functional changes in neural activity data, specifically declines in inhibition and in selectivity to oriented stimuli. This suggests a potential causality between dysregulation of neuron firing and age-induced changes in brain physiology and functional performance. While this does not rule out deeper underlying causes or other mechanisms that could give rise to these changes, our approach opens new avenues for exploring these underlying mechanisms in greater depth and making predictions for future experiments.

Model-based optimized phase-deviation deep brain stimulation for Parkinson 's disease

High-frequency deep brain stimulation (HF-DBS) of the subthalamic nucleus (STN), globus pallidus interna (GPi) and globus pallidus externa (GPe) are often considered as effective methods for the treatment of Parkinson's disease (PD). However, the stimulation of a single nucleus by HF-DBS can cause specific physical damage, produce side effects and usually consume more electrical energy. Therefore, we use a biophysically-based model of basal ganglia-thalamic circuits to explore more effective stimulation patterns to reduce adverse effects and save energy. In this paper, we computationally investigate the combined DBS of two nuclei with the phase deviation between two stimulation waveforms (CDBS). Three different stimulation combination strategies are proposed, i.e., STN and GPe CDBS (SED), STN and GPi CDBS (SID), as well as GPi and GPe CDBS (GGD). Resultantly, it is found that anti-phase CDBS is more effective in improving parkinsonian dynamical properties, including desynchronization of neurons and the recovery of the thalamus relay ability. Detailed simulation investigation shows that anti-phase SED and GGD are superior to SID. Besides, the energy consumption can be largely reduced by SED and GGD (72.5% and 65.5%), compared to HF-DBS. These results provide new insights into the optimal stimulation parameter and target choice of PD, which may be helpful for the clinical practice.

Robust desynchronization of Parkinson's disease pathological oscillations by frequency modulation of delayed feedback deep brain stimulation

The hyperkinetic symptoms of Parkinson's Disease (PD) are associated with the ensembles of interacting oscillators that cause excess or abnormal synchronous behavior within the Basal Ganglia (BG) circuitry. Delayed feedback stimulation is a closed loop technique shown to suppress this synchronous oscillatory activity. Deep Brain Stimulation (DBS) via delayed feedback is known to destabilize the complex intermittent synchronous states. Computational models of the BG network are often introduced to investigate the effect of delayed feedback high frequency stimulation on partially synchronized dynamics. In this study, we develop a reduced order model of four interacting nuclei of the BG as well as considering the Thalamo-Cortical local effects on the oscillatory dynamics. This model is able to capture the emergence of 34 Hz beta band oscillations seen in the Local Field Potential (LFP) recordings of the PD state. Train of high frequency pulses in a delayed feedback stimulation has shown deficiencies such as strengthening the synchronization in case of highly fluctuating neuronal activities, increasing the energy consumed as well as the incapability of activating all neurons in a large-scale network. To overcome these drawbacks, we propose a new feedback control variable based on the filtered and linearly delayed LFP recordings. The proposed control variable is then used to modulate the frequency of the stimulation signal rather than its amplitude. In strongly coupled networks, oscillations reoccur as soon as the amplitude of the stimulus signal declines. Therefore, we show that maintaining a fixed amplitude and modulating the frequency might ameliorate the desynchronization process, increase the battery lifespan and activate substantial regions of the administered DBS electrode. The charge balanced stimulus pulse itself is embedded with a delay period between its charges to grant robust desynchronization with lower amplitudes needed. The efficiency of the proposed Frequency Adjustment Stimulation (FAS) protocol in a delayed feedback method might contribute to further investigation of DBS modulations aspired to address a wide range of abnormal oscillatory behavior observed in neurological disorders.

Pulsatile desynchronizing delayed feedback for closed-loop deep brain stimulation

High-frequency (HF) deep brain stimulation (DBS) is the gold standard for the treatment of medically refractory movement disorders like Parkinson’s disease, essential tremor, and dystonia, with a significant potential for application to other neurological diseases. The standard setup of HF DBS utilizes an open-loop stimulation protocol, where a permanent HF electrical pulse train is administered to the brain target areas irrespectively of the ongoing neuronal dynamics. Recent experimental and clinical studies demonstrate that a closed-loop, adaptive DBS might be superior to the open-loop setup. We here combine the notion of the adaptive high-frequency stimulation approach, that aims at delivering stimulation adapted to the extent of appropriately detected biomarkers, with specifically desynchronizing stimulation protocols. To this end, we extend the delayed feedback stimulation methods, which are intrinsically closed-loop techniques and specifically designed to desynchronize abnormal neuronal synchronization, to pulsatile electrical brain stimulation. We show that permanent pulsatile high-frequency stimulation subjected to an amplitude modulation by linear or nonlinear delayed feedback methods can effectively and robustly desynchronize a STN-GPe network of model neurons and suggest this approach for desynchronizing closed-loop DBS.

Synchronized Beta-Band Oscillations in a Model of the Globus Pallidus-Subthalamic Nucleus Network under External Input

Hypokinetic symptoms of Parkinson's disease are usually associated with excessively strong oscillations and synchrony in the beta frequency band. The origin of this synchronized oscillatory dynamics is being debated. Cortical circuits may be a critical source of excessive beta in Parkinson's disease. However, subthalamo-pallidal circuits were also suggested to be a substantial component in generation and/or maintenance of Parkinsonian beta activity. Here we study how the subthalamo-pallidal circuits interact with input signals in the beta frequency band, representing cortical input. We use conductance-based models of the subthalamo-pallidal network and two types of input signals: artificially-generated inputs and input signals obtained from recordings in Parkinsonian patients. The resulting model network dynamics is compared with the dynamics of the experimental recordings from patient's basal ganglia. Our results indicate that the subthalamo-pallidal model network exhibits multiple resonances in response to inputs in the beta band. For a relatively broad range of network parameters, there is always a certain input strength, which will induce patterns of synchrony similar to the experimentally observed ones. This ability of the subthalamo-pallidal network to exhibit realistic patterns of synchronous oscillatory activity under broad conditions may indicate that these basal ganglia circuits are directly involved in the expression of Parkinsonian synchronized beta oscillations. Thus, Parkinsonian synchronized beta oscillations may be promoted by the simultaneous action of both cortical (or some other) and subthalamo-pallidal network mechanisms. Hence, these mechanisms are not necessarily mutually exclusive.

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.

Improving desynchronization of Parkinsonian neuronal network via triplet-structure coordinated reset stimulation

We investigate how the triplet-structure coordinated reset stimulations (CRS), which acts on the GPe, STN and GPi within the basal ganglia-thalamocortical motor circuit, can destabilize the strong synchronous state and improve the reliability of thalamic relay in the parkinsonian network. It is shown that compared with the permanent (1:0 ON-OFF) CRS or the classic deep brain stimulation paradigm, the periodic m:n ON-OFF CRS (i.e., m ON-cycles stimulation followed by n OFF-cycles stimulation) can significantly desynchronize the neuronal network of Parkinson's disease, and evidently improve the fidelity of thalamic relay. In addition, the CRS-induced desynchronization can be greatly enhanced when the STN subpopulation within the pathologic network is subjected to the synaptic plasticity. Furthermore, the desynchronization and reliability can also be further improved as the closed-loop CRS strategy is introduced. The obtained results can be helpful for us to understand the pathophysiology mechanism of Parkinson's disease, even though the feasibility of CRS still needs to be explored in clinic.

Designing a deep brain stimulator to suppress pathological neuronal synchrony

Some of neuropathologies are believed to be related to abnormal synchronization of neurons. In the line of therapy, designing effective deep brain stimulators to suppress the pathological synchrony among neuronal ensembles is a challenge of high clinical relevance. The stimulation should be able to disrupt the synchrony in the presence of latencies due to imperfect knowledge about parameters of a neuronal ensemble and stimulation impacts on the ensemble. We propose an adaptive desynchronizing deep brain stimulator capable of dealing with these uncertainties. We analyze the collective behavior of the stimulated neuronal ensemble and show that, using the designed stimulator, the resulting asynchronous state is stable. Simulation results reveal the efficiency of the proposed technique.

Cortical information flow in Parkinson's disease: a composite network/field model

The basal ganglia play a crucial role in the execution of movements, as demonstrated by the severe motor deficits that accompany Parkinson's disease (PD). Since motor commands originate in the cortex, an important question is how the basal ganglia influence cortical information flow, and how this influence becomes pathological in PD. To explore this, we developed a composite neuronal network/neural field model. The network model consisted of 4950 spiking neurons, divided into 15 excitatory and inhibitory cell populations in the thalamus and cortex. The field model consisted of the cortex, thalamus, striatum, subthalamic nucleus, and globus pallidus. Both models have been separately validated in previous work. Three field models were used: one with basal ganglia parameters based on data from healthy individuals, one based on data from individuals with PD, and one purely thalamocortical model. Spikes generated by these field models were then used to drive the network model. Compared to the network driven by the healthy model, the PD-driven network had lower firing rates, a shift in spectral power toward lower frequencies, and higher probability of bursting; each of these findings is consistent with empirical data on PD. In the healthy model, we found strong Granger causality between cortical layers in the beta and low gamma frequency bands, but this causality was largely absent in the PD model. In particular, the reduction in Granger causality from the main “input” layer of the cortex (layer 4) to the main “output” layer (layer 5) was pronounced. This may account for symptoms of PD that seem to reflect deficits in information flow, such as bradykinesia. In general, these results demonstrate that the brain's large-scale oscillatory environment, represented here by the field model, strongly influences the information processing that occurs within its subnetworks. Hence, it may be preferable to drive spiking network models with physiologically realistic inputs rather than pure white noise.