Computational Stimulation of the Basal Ganglia Neurons with Cost Effective Delayed Gaussian Waveforms

Moving from phenomenological to predictive modelling: Progress and pitfalls of modelling brain stimulation in-silico

Brain stimulation is an increasingly popular neuromodulatory tool used in both clinical and research settings; however, the effects of brain stimulation, particularly those of non-invasive stimulation, are variable. This variability can be partially explained by an incomplete mechanistic understanding, coupled with a combinatorial explosion of possible stimulation parameters. Computational models constitute a useful tool to explore the vast sea of stimulation parameters and characterise their effects on brain activity. Yet the utility of modelling stimulation in-silico relies on its biophysical relevance, which needs to account for the dynamics of large and diverse neural populations and how underlying networks shape those collective dynamics. The large number of parameters to consider when constructing a model is no less than those needed to consider when planning empirical studies. This piece is centred on the application of phenomenological and biophysical models in non-invasive brain stimulation. We first introduce common forms of brain stimulation and computational models, and provide typical construction choices made when building phenomenological and biophysical models. Through the lens of four case studies, we provide an account of the questions these models can address, commonalities, and limitations across studies. We conclude by proposing future directions to fully realise the potential of computational models of brain stimulation for the design of personalized, efficient, and effective stimulation strategies.

A screening method based on analytic hierarchy process for closed-loop DBS strategies of Parkinson's disease

BACKGROUND

Closed-loop deep brain stimulation (DBS) is a research hotspot in the treatment of Parkinson's disease. However, a variety of stimulation strategies will increase the selection time and cost in animal experiments and clinical studies. Moreover, the stimulation effect is little difference between similar strategies, so the selection process will be redundant.

OBJECTIVE

The objective was to propose a comprehensive evaluation model based on analytic hierarchy process (AHP) to select the best one among similar strategies.

METHODS

Two similar strategies, namely, threshold stimulation (CDBS) and threshold stimulus after EMD feature extraction (EDBS), were used for analysis and screening. The values of Similar to Unified Parkinson's Disease Rating Scale estimates (SUE), β power and energy consumption were calculated and analysed. The stimulation threshold with the best improvement effect was selected. The weights of the indices were allocated by AHP. Finally, the weights and index values were combined, and the comprehensive scores of the two strategies were calculated using the evaluation model.

RESULTS

The optimal stimulation threshold for CDBS was 52% and for EDBS was 62%. The weights of the indices were 0.45, 0.45 and 0.1, respectively. According to comprehensive scores, different from the situation where either EDBS or CDBS can be called optimal stimulation strategies. But under the same threshold stimulation, the EDBS was better than the CDBS under the optimal level.

CONCLUSION

The evaluation model based on AHP under the optimal stimulation conditions satisfied the screening conditions between the two strategies.

Transcranial ultrasound neuromodulation induces neuronal correlation change in the rat somatosensory cortex

Objective.Transcranial focused ultrasound (tFUS) is a neuromodulation technique which has been the focus of increasing interest for noninvasive brain stimulation with high spatial specificity. Its ability to excite and inhibit neural circuits as well as to modulate perception and behavior has been demonstrated, however, we currently lack understanding of how tFUS modulates the ways neurons interact with each other. This understanding would help elucidate tFUS's mechanism of systemic neuromodulation and allow future development of therapies for treating neurological disorders.Approach.In this study, we investigate how tFUS modulates neural interaction and response to peripheral electrical limb stimulation through intracranial multi-electrode recordings in the rat somatosensory cortex. We deliver ultrasound in a pulsed pattern to induce frequency dependent plasticity in a manner similar to what is found following electrical stimulation.Main Results.We show that neural firing in response to peripheral electrical stimulation is increased after ultrasound stimulation at all frequencies, showing tFUS induced changes in excitability of individual neuronsin vivo. We demonstrate tFUS sonication repetition frequency dependent pairwise correlation changes between neurons, with both increases and decreases observed at different frequencies.Significance.These results extend previous research showing tFUS to be capable of inducing synaptic depression and demonstrate its ability to modulate network dynamics as a whole.

Real-Time Generation of Hyperbolic Neuronal Spiking Patterns

Neuronal spikes are referred to as the electric activity of neurons (in terms of voltage) in response to various biological events such as the sodium and calcium ionic current channels in the brain. Currently, both biological models as well as mathematical models of neuronal spiking patterns have been introduced in the literature. However, very little attempt has been made to run these models in real-time. With applications ranging from hardware neuromorphic circuit designs, artificial intelligence (AI) architectures, to deep brain stimulation, real-time generation of these models is of particular interest in the brain-inspired computing/architecture and neuro-modulation/stimulation research communities. This paper proposes the development of a framework for generating the hyperbolic based single neuronal spiking patterns in real-time. Simulation results confirm that the generated spikes resemble the existing models of neuronal spiking patterns, with additional real-time run capability as well as the ability to change the parameters on the fly. Clinical relevance-Real-time models of neuronal spiking patterns have significant clinical relevance with respect to applications of neuromorphic/AI chips for medical image processing/computer vision, as well as clinical neuroscience, neuromodulation and neurostimulation such as deep brain stimulation for modulating the abnormal effects of neurological diseases.

Synchronization of Fractional Order Neurons in Presence of Noise

The firing rate of some biological neurons such as neocortical pyramidal neurons is consistent with fractional order derivative, and the fractional-order neuron models depict the firing rate of neurons more accurately than other integer order neuron models do. For this reason, first, the dynamical characteristics of fractional order Hindmarsh Rose (HR) neuron are investigated, here and then a two coupled neuronal system based on Hindmarsh Rose neuron is presented. The results show several differences in the dynamical cha.racteristics of integer order and fractional order Hindmarsh Rose neuron model. The integer order model shows only one type of firing characteristics when the parameter of the model remained the same. The fractional-order model depicts several dynamical behaviors even for the same parameters as the order of the fractional operator is varied with the same parameter values. The firing frequency increases as the order of the fractional operator decreases. The fractional-order is therefore key in determining the firing characteristics of biological neuron models. A linearized model of HR neuron is also given for hardware resource minimizations and to implement this neuronal network on a large scale. A synchronized system of two fractional-order fractional Hindmarsh-Rose (HR) neurons in the presence of noise is also presented. The dynamical characteristics of the modified coupled neuron are determined by the parameters of the neuron model and the coupling function. The robustness of the network in the presence of noise is verified by both amplitude and phase synchronization techniques. A simplification of the coupling function is also presented to reduce the hardware cost. The synchronization results show that the model can produce the desired behavior with acceptable error.

A review of computational modeling and deep brain stimulation: applications to Parkinson's disease

Biophysical computational models are complementary to experiments and theories, providing powerful tools for the study of neurological diseases. The focus of this review is the dynamic modeling and control strategies of Parkinson's disease (PD). In previous studies, the development of parkinsonian network dynamics modeling has made great progress. Modeling mainly focuses on the cortex-thalamus-basal ganglia (CTBG) circuit and its sub-circuits, which helps to explore the dynamic behavior of the parkinsonian network, such as synchronization. Deep brain stimulation (DBS) is an effective strategy for the treatment of PD. At present, many studies are based on the side effects of the DBS. However, the translation from modeling results to clinical disease mitigation therapy still faces huge challenges. Here, we introduce the progress of DBS improvement. Its specific purpose is to develop novel DBS treatment methods, optimize the treatment effect of DBS for each patient, and focus on the study in closed-loop DBS. Our goal is to review the inspiration and insights gained by combining the system theory with these computational models to analyze neurodynamics and optimize DBS treatment.

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.

Is the efficacy of repetitive transcranial magnetic stimulation influenced by baseline severity of fatigue symptom in patients with myalgic encephalomyelitis

Objectives: Recently, repetitive transcranial magnetic stimulation (rTMS) has been therapeutically applied for patients with myalgic encephalomyelitis (ME). However, it is still unclear which clinical factors could influence the efficacy of rTMS for ME patients. The purpose of this study is to clarify whether baseline severity of fatigue symptom would influence the efficacy of rTMS applied for ME patients.Methods: Twenty-two patients with ME were studied. Each patient was hospitalized to receive 6-8 sessions of rTMS. In this study, high-frequency rTMS of 10 Hz was applied over prefrontal cortex. To evaluate the severity of fatigue symptom, Brief Fatigue Inventory (BFI) score and Visual Analogue Scale (VAS) rate were applied before and after rTMS application. Based on the BFI score before rTMS, the patients were divided into two groups: 'severe group' (n = 9) and 'mild group' (n = 13). We compared the extent of the improvements of fatigue symptom between two groups.Results: In severe group, compared to before rTMS, VAS rate was significantly lower not only at discharge but also 2 weeks after discharge. Similarly, mild group also showed significant decrease in VAS rate at the same timepoints. However, the extent of VAS rate change did not differ between two groups. In addition, no significant correlation between baseline score of BFI and the changes in VAS rate was indicated.Conclusions: It can be concluded that rTMS can improve fatigue symptom in ME patients regardless of baseline severity of fatigue symptom. It is expected that rTMS can be a novel therapeutic intervention for ME patients.

Development of a miniature device for emerging deep brain stimulation paradigms

Deep brain stimulation (DBS) is a neuromodulatory approach for treatment of several neurological and psychiatric disorders. A new focus on optimising the waveforms used for stimulation is emerging regarding the mechanism of DBS treatment. Many existing DBS devices offer only a limited set of predefined waveforms, mainly rectangular, and hence are inapt for exploring the emerging paradigm. Advances in clinical DBS are moving towards incorporating new stimulation parameters, yet we remain limited in our capacity to test these in animal models, arguably a critical first step. Accordingly, there is a need for the development of new miniature, low-power devices to enable investigation into the new DBS paradigms in preclinical settings. The ideal device would allow for flexibility in the stimulation waveforms, while remaining suitable for chronic, tetherless, biphasic deep brain stimulation. In this work, we elucidate several key parameters in a DBS system, identify gaps in existing solutions, and propose a new device to support preclinical DBS. The device allows for a high degree of flexibility in the output waveform with easily altered shape, frequency, pulse-width and amplitude. The device is suitable for both traditional and modern stimulation schemes, including those using non-rectangular waveforms, as well as delayed feedback schemes. The device incorporates active charge balancing to ensure safe operation, and allows for simple production of custom biphasic waveforms. This custom waveform output is unique in the field of preclinical DBS devices, and could be advantageous in performing future DBS studies investigating new treatment paradigms. This tetherless device can be easily and comfortably carried by an animal in a back-mountable configuration. The results of in-vitro tests are presented and discussed.

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.

Delayed Feedback Frequency Adjustment for Deep Brain Stimulation of Subthalamic Nucleus Oscillations

Neural oscillations within the Basal Ganglia (BG) circuitry are associated with Parkinson's Disease (PD) and are observable through the Local Field Potential (LFP) of the Subthalamic Nucleus (STN) or Globus Pallidus externa (GPe) neurons. LFP amplitude modulation in a delayed feedback protocol for Deep Brain Stimulation (DBS) is shown to destabilize the complex intermittent synchronous states. However, traditional High Frequency Stimulations (HFS) often intensify the synchronization of highly fluctuating neurons, are less efficient in activating all neurons in large scale networks and consume more battery of the DBS device. Here, we investigate the partially synchronous dynamics of a STN-GPe coupling network to examine the effect of frequency adjustment in the stimulation signal. The frequency of the stimulation signal is adjusted according to the nonlinear delayed feedback LFP of the STN population. Frequency adjustment protocol with a fixed stimulation amplitude is shown to increase the desynchronization efficiency and neuronal activation by 25% and 16.2%, respectively, while reducing the energy consumption by 31.5% compared to amplitude modulation methods for stimulation of large networks (1000 neurons).

Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate

Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studies, there still remain solving works for the purpose of realizing an ECoG-based prosthesis. We suggest a neuromuscular interface to control robot using decoded muscle activities and joint angles. We used sparse linear regression to find the best fit between band-passed ECoGs and electromyograms (EMG) or joint angles. The best coefficient of determination for 100 s continuous prediction was 0.6333 ± 0.0033 (muscle activations) and 0.6359 ± 0.0929 (joint angles), respectively. We also controlled a 4 degree of freedom (DOF) robot arm using only decoded 4 DOF angles from the ECoGs in this study. Consequently, this study shows the possibility of contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

Neuroprotection by immunomodulatory agents in animal models of Parkinson's disease

Parkinson's disease (PD) is an age-related neurodegenerative disease for which the characteristic motor symptoms emerge after an extensive loss of dopamine containing neurons. The cell bodies of these neurons are present in the substantia nigra, with the nerve terminals being in the striatum. Both innate and adaptive immune responses may contribute to dopaminergic neurodegeneration and disease progression is potentially linked to these. Studies in the last twenty years have indicated an important role for neuroinflammation in PD through degeneration of the nigrostriatal dopaminergic pathway. Characteristic of neuroinflammation is the activation of brain glial cells, principally microglia and astrocytes that release various soluble factors. Many of these factors are proinflammatory and neurotoxic and harmful to nigral dopaminergic neurons. Recent studies have identified several different agents with immunomodulatory properties that protected dopaminergic neurons from degeneration and death in animal models of PD. All of the agents were effective in reducing the motor deficit and alleviating dopaminergic neurotoxicity and, when measured, preventing the decrease of dopamine upon being administered therapeutically after 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, 6-hydroxydopamine, rotenone-lesioning or delivery of adeno-associated virus-α-synuclein to the ventral midbrain of animals. Some of these agents were shown to exert an anti-inflammatory action, decrease oxidative stress, and reduce lipid peroxidation products. Activation of microglia and astrocytes was also decreased, as well as infiltration of T cells into the substantia nigra. Pretreatment with fingolimod, tanshinoine I, dimethyl fumarate, thalidomide, or cocaine- and amphetamine-regulated transcript peptide as a preventive strategy ameliorated motor deficits and nigral dopaminergic neurotoxicity in brain-lesioned animals. Immunomodulatory agents could be used to treat patients with early clinical signs of the disease or potentially even prior to disease onset in those identified as having pre-disposing risk, including genetic factors.