Spatiotemporal visualization of deep brain stimulation-induced effects in the subthalamic nucleus

A Population Model of Deep Brain Stimulation in Movement Disorders From Circuits to Cells

For more than 30 years, deep brain stimulation (DBS) has been used to target the symptoms of a number of neurological disorders and in particular movement disorders such as Parkinson’s disease (PD) and essential tremor (ET). It is known that the loss of dopaminergic neurons in the substantia nigra leads to PD, while the exact impact of this on the brain dynamics is not fully understood, the presence of beta-band oscillatory activity is thought to be pathological. The cause of ET, however, remains uncertain, however pathological oscillations in the thalamocortical-cerebellar network have been linked to tremor. Both of these movement disorders are treated with DBS, which entails the surgical implantation of electrodes into a patient’s brain. While DBS leads to an improvement in symptoms for many patients, the mechanisms underlying this improvement is not clearly understood, and computational modeling has been used extensively to improve this. Many of the models used to study DBS and its effect on the human brain have mainly utilized single neuron and single axon biophysical models. We have previously shown in separate models however, that the use of population models can shed much light on the mechanisms of the underlying pathological neural activity in PD and ET in turn, and on the mechanisms underlying DBS. Together, this work suggested that the dynamics of the cerebellar-basal ganglia thalamocortical network support oscillations at frequency range relevant to movement disorders. Here, we propose a new combined model of this network and present new results that demonstrate that both Parkinsonian oscillations in the beta band and oscillations in the tremor frequency range arise from the dynamics of such a network. We find regions in the parameter space demonstrating the different dynamics and go on to examine the transition from one oscillatory regime to another as well as the impact of DBS on these different types of pathological activity. This work will allow us to better understand the changes in brain activity induced by DBS, and allow us to optimize this clinical therapy, particularly in terms of target selection and parameter setting.

Closed-Loop Implantable Therapeutic Neuromodulation Systems Based on Neurochemical Monitoring

Closed-loop or intelligent neuromodulation allows adjustable, personalized neuromodulation which usually incorporates the recording of a biomarker, followed by implementation of an algorithm which decides the timing (when?) and strength (how much?) of stimulation. Closed-loop neuromodulation has been shown to have greater benefits compared to open-loop neuromodulation, particularly for therapeutic applications such as pharmacoresistant epilepsy, movement disorders and potentially for psychological disorders such as depression or drug addiction. However, an important aspect of the technique is selection of an appropriate, preferably neural biomarker. Neurochemical sensing can provide high resolution biomarker monitoring for various neurological disorders as well as offer deeper insight into neurological mechanisms. The chemicals of interest being measured, could be ions such as potassium (K⁺), sodium (Na⁺), calcium (Ca²⁺), chloride (Cl⁻), hydrogen (H⁺) or neurotransmitters such as dopamine, serotonin and glutamate. This review focusses on the different building blocks necessary for a neurochemical, closed-loop neuromodulation system including biomarkers, sensors and data processing algorithms. Furthermore, it also highlights the merits and drawbacks of using this biomarker modality.

Voltage adjustment improves rigidity and tremor in Parkinson's disease patients receiving deep brain stimulation

Deep brain stimulation of the subthalamic nucleus is recognized as the most effective treatment for moderate and advanced Parkinson's disease. Programming of the stimulation parameters is important for maintaining the efficacy of deep brain stimulation. Voltage is considered to be the most effective programming parameter. The present study is a retrospective analysis of six patients with Parkinson's disease (four men and two women, aged 37–65 years), who underwent bilateral deep brain stimulation of the subthalamic nucleus at the First Affiliated Hospital of Sun Yat-sen University, China, and who subsequently adjusted only the stimulation voltage. We evaluated motor symptom severity using the Unified Parkinson's Disease Rating Scale Part III, symptom progression using the Hoehn and Yahr scale, and the levodopa equivalent daily dose, before surgery and 1 and 2 years after surgery. The 2-year follow-up results show that rigidity and tremor improved, and clinical symptoms were reduced, while pulse width was maintained at 60 μs and frequency at 130 Hz. Voltage adjustment alone is particularly suitable for patients who cannot tolerate multiparameter program adjustment. Levodopa equivalent daily dose was markedly reduced 1 and 2 years after surgery compared with baseline. Our results confirm that rigidity, tremor and bradykinesia can be best alleviated by voltage adjustment. The trial was registered at ClinicalTrials.gov (identifier: NCT01934881).

A Network Model of Local Field Potential Activity in Essential Tremor and the Impact of Deep Brain Stimulation

Essential tremor (ET), a movement disorder characterised by an uncontrollable shaking of the affected body part, is often professed to be the most common movement disorder, affecting up to one percent of adults over 40 years of age. The precise cause of ET is unknown, however pathological oscillations of a network of a number of brain regions are implicated in leading to the disorder. Deep brain stimulation (DBS) is a clinical therapy used to alleviate the symptoms of a number of movement disorders. DBS involves the surgical implantation of electrodes into specific nuclei in the brain. For ET the targeted region is the ventralis intermedius (Vim) nucleus of the thalamus. Though DBS is effective for treating ET, the mechanism through which the therapeutic effect is obtained is not understood. To elucidate the mechanism underlying the pathological network activity and the effect of DBS on such activity, we take a computational modelling approach combined with electrophysiological data. The pathological brain activity was recorded intra-operatively via implanted DBS electrodes, whilst simultaneously recording muscle activity of the affected limbs. We modelled the network hypothesised to underlie ET using the Wilson-Cowan approach. The modelled network exhibited oscillatory behaviour within the tremor frequency range, as did our electrophysiological data. By applying a DBS-like input we suppressed these oscillations. This study shows that the dynamics of the ET network support oscillations at the tremor frequency and the application of a DBS-like input disrupts this activity, which could be one mechanism underlying the therapeutic benefit.

Essential tremor (ET) is acknowledged to be the most common movement disorder affecting 1% of the population. Although the underlying mechanisms remain elusive, the thalamus, cortex and cerebellum are implicated in the underlying pathology. More recently, it has been shown that ET can be successfully treated by deep brain stimulation (DBS). This clinical treatment involves the surgical implantation of electrodes into the brain, through which current is applied. However, the mechanisms of how DBS achieves clinical benefit continue to be debated. A key question is whether ET can be modeled as a pathological network behavior as has been suggested previously. If so, we can then ask how DBS would modulate this brain activity. Our study combines: (i) simultaneous electrophysiological recordings from the brain and muscle; (ii) computational modelling; (iii) mathematical analysis. We found that the network supports oscillations in the tremor range, and the application of high frequency DBS switches this to low amplitude, high-frequency activity. We propose that our model can be used to predict DBS parameter settings that suppress pathological network activity and consequently tremor. In summary, we provide the first population level model of essential tremor including the effect of DBS on network behaviour.

The dynamics of neurological disease: integrating computational, experimental and clinical neuroscience

There is a vast (and rapidly growing) amount of experimental and clinical data of the nervous system at very diverse spatial scales of activity (e.g. from sub-cellular through to whole organ), with many neurological disorders characterized by oscillations in neural activity across these disparate scales. Computer modelling and the development of associated mathematical theories provide us with a unique opportunity to integrate information from across these diverse scales of activity; leading to explanations of the potential mechanisms underlying the time-evolving dynamics and, more importantly, allowing the development of new hypotheses regarding neural function that may be tested experimentally and ultimately translated into the clinic. The purpose of this special issue is to present an overview of current integrative research in the areas of epilepsy, Parkinson's disease and schizophrenia, where multidisciplinary relationships involving theory, experimental and clinical research are becoming increasingly established.