Short pauses in thalamic deep brain stimulation promote tremor and neuronal bursting

Computational models advance deep brain stimulation for Parkinson's disease

Deep brain stimulation(DBS) has become an effective intervention for advanced Parkinson's disease(PD), but the exact mechanism of DBS is still unclear. In this review, we discuss the history of DBS, the anatomy and internal architecture of the basal ganglia (BG), the abnormal pathological changes of the BG in PD, and how computational models can help understand and advance DBS. We also describe two types of models: mathematical theoretical models and clinical predictive models. Mathematical theoretical models simulate neurons or neural networks of BG to shed light on the mechanistic principle underlying DBS, while clinical predictive models focus more on patients' outcomes, helping to adapt treatment plans for each patient and advance novel electrode designs. Finally, we provide insights and an outlook on future technologies.

Temporally non-regular patterns of deep brain stimulation (DBS) enhance assessment of evoked potentials while maintaining motor symptom management in Parkinson's disease (PD)

BACKGROUND

Traditional deep brain stimulation (DBS) at fixed regular frequencies (>100 Hz) is effective in treating motor symptoms of Parkinson's disease (PD). Temporally non-regular patterns of DBS are a new parameter space that may help increase efficacy and efficiency.

OBJECTIVE

To compare the effects of temporally non-regular patterns of DBS to traditional regularly-spaced pulses.

METHODS

We simultaneously recorded local field potentials (LFP) and monitored motor symptoms (tremor and bradykinesia) in persons with PD during DBS in subthalamic nucleus (STN). We quantified both oscillatory activity and DBS local evoked potentials (DLEPs) from the LFP.

RESULTS

Temporally non-regular patterns were as effective as traditional pulse patterns in modulating motor symptoms, oscillatory activity, and DLEPs. Moreover, one of our novel patterns enabled recording of longer duration DLEPs during clinically effective stimulation.

CONCLUSIONS

Stimulation gaps of 50 ms can be used to increase efficiency and to enable regular assessment of long-duration DLEPs while maintaining effective symptom management. This may be a promising paradigm for closed-loop DBS with biomarker assessment during the gaps.

Suppression of Neuronal Firing Following Antidromic High-Frequency Stimulations on the Neuronal Axons in Rat Hippocampal CA1 Region

High-frequency stimulation (HFS) of electrical pulses has been used to treat certain neurological diseases in brain with commonly utilized effects within stimulation periods. Post-stimulation effects after the end of HFS may also have functions but are lack of attention. To investigate the post-stimulation effects of HFS, we performed experiments in the rat hippocampal CA1 region in vivo. Sequences of 1-min antidromic-HFS (A-HFS) were applied at the alveus fibers. To evaluate the excitability of the neurons, separated orthodromic-tests (O-test) of paired pulses were applied at the Schaffer collaterals in the period of baseline, during late period of A-HFS, and following A-HFS. The evoked potentials of A-HFS pulses and O-test pulses were recorded at the stratum pyramidale and the stratum radiatum of CA1 region by an electrode array. The results showed that the antidromic population spikes (APS) evoked by the A-HFS pulses persisted through the entire 1-min period of 100 Hz A-HFS, though the APS amplitudes decreased significantly from the initial value of 9.9 ± 3.3 mV to the end value of 1.6 ± 0.60 mV. However, following the cessation of A-HFS, a silent period without neuronal firing appeared before the firing gradually recovered to the baseline level. The mean lengths of both silent period and recovery period of pyramidal cells (21.9 ± 22.9 and 172.8 ± 91.6 s) were significantly longer than those of interneurons (11.2 ± 8.9 and 45.6 ± 35.9 s). Furthermore, the orthodromic population spikes (OPS) and the field excitatory postsynaptic potentials (fEPSP) evoked by O-tests at ∼15 s following A-HFS decreased significantly, indicating the excitability of pyramidal cells decreased. In addition, when the pulse frequency of A-HFS was increased to 200, 400, and 800 Hz, the suppression of neuronal activity following A-HFS decreased rather than increased. These results indicated that the neurons with axons directly under HFS can generate a post-stimulation suppression of their excitability that may be due to an antidromic invasion of axonal A-HFS to somata and dendrites. The finding provides new clues to utilize post-stimulation effects generated in the intervals to design intermittent stimulations, such as closed-loop or adaptive stimulations.

Past, Present, and Future of Deep Brain Stimulation: Hardware, Software, Imaging, Physiology and Novel Approaches

Deep brain stimulation (DBS) has advanced treatment options for a variety of neurologic and neuropsychiatric conditions. As the technology for DBS continues to progress, treatment efficacy will continue to improve and disease indications will expand. Hardware advances such as longer-lasting batteries will reduce the frequency of battery replacement and segmented leads will facilitate improvements in the effectiveness of stimulation and have the potential to minimize stimulation side effects. Targeting advances such as specialized imaging sequences and “connectomics” will facilitate improved accuracy for lead positioning and trajectory planning. Software advances such as closed-loop stimulation and remote programming will enable DBS to be a more personalized and accessible technology. The future of DBS continues to be promising and holds the potential to further improve quality of life. In this review we will address the past, present and future of DBS.

Implantable Pulse Generators for Deep Brain Stimulation: Challenges, Complications, and Strategies for Practicality and Longevity

Deep brain stimulation (DBS) represents an important treatment modality for movement disorders and other circuitopathies. Despite their miniaturization and increasing sophistication, DBS systems share a common set of components of which the implantable pulse generator (IPG) is the core power supply and programmable element. Here we provide an overview of key hardware and software specifications of commercially available IPG systems such as rechargeability, MRI compatibility, electrode configuration, pulse delivery, IPG case architecture, and local field potential sensing. We present evidence-based approaches to mitigate hardware complications, of which infection represents the most important factor. Strategies correlating positively with decreased complications include antibiotic impregnation and co-administration and other surgical considerations during IPG implantation such as the use of tack-up sutures and smaller profile devices.Strategies aimed at maximizing battery longevity include patient-related elements such as reliability of IPG recharging or consistency of nightly device shutoff, and device-specific such as parameter delivery, choice of lead configuration, implantation location, and careful selection of electrode materials to minimize impedance mismatch. Finally, experimental DBS systems such as ultrasound, magnetoelectric nanoparticles, and near-infrared that use extracorporeal powered neuromodulation strategies are described as potential future directions for minimally invasive treatment.

Frequency-dependent spike-pattern changes in motor cortex during thalamic deep brain stimulation

The cerebellar-receiving area of the motor thalamus is the primary anatomical target for treating essential tremor with deep brain stimulation (DBS). Although neuroimaging studies have shown that higher stimulation frequencies in this target correlate with increased cortical metabolic activity, less is known about the cellular-level functional changes that occur in the primary motor cortex (M1) with thalamic stimulation and how these changes depend on the frequency of DBS. In this study, we used a preclinical animal model of DBS to collect single-unit spike recordings in M1 before, during, and after DBS targeting the cerebellar-receiving area of the motor thalamus (VPLo, nucleus ventralis posterior lateralis pars oralis). The effects of VPLo-DBS on M1 spike rates, interspike interval entropy, and peristimulus phase-locking were compared across stimulus pulse train frequencies ranging from 10 to 130 Hz. Although VPLo-DBS modulated the spike rates of 20-50% of individual M1 cells in a frequency-dependent manner, the population-level average spike rate only weakly depended on stimulation frequency. In contrast, the population-level entropy measure showed a pronounced decrease with high-frequency stimulation, caused by a subpopulation of cells that exhibited strong phase-locking and general spike-pattern regularization. Contrarily, low-frequency stimulation induced an entropy increase (spike-pattern disordering) in a relatively large portion of the recorded population, which diminished with higher stimulation frequencies. These results also suggest that changes in phase-locking and spike-pattern entropy are not necessarily equivalent pattern phenomena, but rather that they should both be weighed when quantifying stimulation-induced spike-pattern changes.NEW & NOTEWORTHY The network mechanisms of thalamic deep brain stimulation (DBS) are not well understood at the cellular level. This study investigated the neuronal firing rate and pattern changes in the motor cortex resulting from stimulation of the cerebellar-receiving area of the motor thalamus. We showed that there is a nonintuitive relationship between general entropy-based spike-pattern measures and phase-locked regularization to DBS.

Distinct perceptive pathways selected with tonic and bursting patterns of thalamic stimulation

BACKGROUND

Novel patterns of electrical stimulation of the brain and spinal cord hold tremendous promise to improve neuromodulation therapies for diverse disorders, including tremor and pain. To date, there are limited numbers of experimental studies in human subjects to help explain how stimulation patterns impact the clinical response, especially with deep brain stimulation. We propose using novel stimulation patterns during electrical stimulation of somatosensory thalamus in awake deep brain stimulation surgeries and hypothesize that stimulation patterns will influence the sensory percept without moving the electrode.

METHODS

In this study of 15 fully awake patients, the threshold of perception as well as perceptual characteristics were compared for tonic (trains of regularly-repeated pulses) and bursting stimulation patterns.

RESULTS

In a majority of subjects, tonic and burst percepts were located in separate, non-overlapping body regions (i.e., face vs. hand) without moving the stimulating electrode (p < 0.001; binomial test). The qualitative features of burst percepts also differed from those of tonic-evoked percepts as burst patterns were less likely to evoke percepts described as tingling (p = 0.013; Fisher's exact test).

CONCLUSIONS

Because somatosensory thalamus is somatotopically organized, percept location can be related to anatomic thalamocortical pathways. Thus, stimulation pattern may provide a mechanism to select for different thalamocortical pathways. This added control could lead to improvements in neuromodulation - such as improved efficacy and side effect attenuation - and may also improve localization for sensory prostheses.

Effect of Cycling Thalamosubthalamic Stimulation on Tremor Habituation and Rebound in Parkinson Disease

BACKGROUND

Deep brain stimulation is an effective treatment for severe tremor in essential tremor and Parkinson disease (PD). However, progressive loss of the beneficial effects of deep brain stimulation may occur due to several factors.

CASE DESCRIPTION

We assessed the effects of different temporal patterns of cycling stimulation in the posterior subthalamic area, subthalamic nucleus, and the ventral intermediate nucleus of the thalamus in 3 PD patients with early decline of tremor suppression associated with severe tremor rebound.

CONCLUSIONS

Certain temporal patterns of cycling (10 seconds on/1 second off or 30 seconds on/5 seconds off, soft start off) were useful for treating tremor habituation and rebound and showed long-term tremor suppression. Cycling stimulation may prevent tremor habituation in PD patients with severe tremor rebound.

The Appearance Order of Varying Intervals Introduces Extra Modulation Effects on Neuronal Firing Through Non-linear Dynamics of Sodium Channels During High-Frequency Stimulations

Electrical pulse stimulation in the brain has shown success in treating several brain disorders with constant pulse frequency or constant inter-pulse interval (IPI). Varying IPI may offer a variety of novel stimulation paradigms and may extend the clinical applications. However, a lack of understanding of neuronal responses to varying IPI limits its informed applications. In this study, to investigate the effects of varying IPI, we performed both rat experiments and computational modeling by applying high-frequency stimulation (HFS) to efferent axon fibers of hippocampal pyramidal cells. Antidromically evoked population spikes (PSs) were used to evaluate the neuronal responses to pulse stimulations with different IPI patterns including constant IPI, gradually varying IPI, and randomly varying IPI. All the varying IPI sequences were uniformly distributed in the same interval range of 10 to 5 ms (i.e., 100 to 200 Hz). The experimental results showed that the mean correlation coefficient of PS amplitudes to the lengths of preceding IPI during HFS with random IPI (0.72 ± 0.04, n = 7 rats) was significantly smaller than the corresponding correlation coefficient during HFS with gradual IPI (0.92 ± 0.03, n = 7 rats, P < 0.001, t-test). The PS amplitudes induced by the random IPI covered a wider range, over twice as much as that induced by the gradual IPI, indicating additional effects induced by merely changing the appearance order of IPI. The computational modeling reproduced these experimental results and provided insights into these modulatory effects through the mechanism of non-linear dynamics of sodium channels and potassium accumulation in the narrow peri-axonal space. The simulation results showed that the HFS-induced increase of extracellular potassium ([K⁺] _o ) elevated the membrane potential of axons, delayed the recovery course of sodium channels that were repeatedly activated and inactivated during HFS, and resulted in intermittent neuronal firing. Because of non-linear membrane dynamics, random IPI recruited more neurons to fire together following specific sub-sequences of pulses than gradual IPI, thereby widening the range of PS amplitudes. In conclusion, the study demonstrated novel HFS effects of neuronal modulation induced by merely changing the appearance order of the same group of IPI of pulses, which may inform the development of new stimulation patterns to meet different demands for treating various brain diseases.

Effects of ramped-frequency thalamic deep brain stimulation on tremor and activity of modeled neurons

OBJECTIVE

We conducted intraoperative measurements of tremor to quantify the effects of temporally patterned ramped-frequency DBS trains on tremor.

METHODS

Seven patterns of stimulation were tested in nine subjects with thalamic DBS for essential tremor: stimulation 'off', three ramped-frequency stimulation (RFS) trains from 130 → 50 Hz, 130 → 60 Hz, and 235 → 90 Hz, and three constant frequency stimulation (CFS) trains at 72, 82, and 130 Hz. The same patterns were applied to a computational model of the thalamic neural network.

RESULTS

Temporally patterned 130 → 60 Hz ramped-frequency trains suppressed tremor relative to stimulation 'off,' but 130 → 50 Hz, 130 → 60 Hz, and 235 → 90 Hz ramped-frequency trains were no more effective than constant frequency stimulation with the same mean interpulse interval (IPI). Computational modeling revealed that rhythmic burst-driver inputs to thalamus were masked during DBS, but long IPIs, concurrent with pauses in afferent cerebellar and cortical firing, allowed propagation of bursting activity. The mean firing rate of bursting-type model neurons as well as the firing pattern entropy of model neurons were both strongly correlated with tremor power across stimulation conditions.

CONCLUSION

Frequency-ramped DBS produced equivalent tremor suppression as constant frequency thalamic DBS. Tremor-related thalamic burst activity may result from burst-driver input, rather than by an intrinsic rebound mechanism.

SIGNIFICANCE

Ramping stimulation frequency may exacerbate thalamic burst firing by introducing consecutive pauses of increasing duration to the stimulation pattern.

Altered Primary Motor Cortex Neuronal Activity in a Rat Model of Harmaline-Induced Tremor During Thalamic Deep Brain Stimulation

Although deep brain stimulation (DBS) is a clinically effective surgical treatment for essential tremor (ET), and its neurophysiological mechanisms are not fully understood. As the motor thalamus is the most popular DBS target for ET, and it is known that the thalamic nucleus plays a key role in relaying information about the external environment to the cerebral cortex, it is important to investigate mechanisms of thalamic DBS in the context of the cerebello-thalamo-cortical neuronal network. To examine this, we measured single-unit neuronal activities in the resting state in M1 during VL thalamic DBS in harmaline-induced tremor rats and analyzed neuronal activity patterns in the thalamo-cortical circuit. Four activity patterns - including oscillatory burst, oscillatory non-burst, irregular burst, and irregular non-burst - were identified by harmaline administration; and those firing patterns were differentially affected by VL thalamic DBS, which seems to drive pathologic cortical signals to signals in normal status. As specific neuronal firing patterns like oscillation or burst are considered important for information processing, our results suggest that VL thalamic DBS may modify pathophysiologic relay information rather than simply inhibit the information transmission.

Average firing rate rather than temporal pattern determines metabolic cost of activity in thalamocortical relay neurons

Thalamocortical (TC) relay cells exhibit different temporal patterns of activity, including tonic mode and burst mode, to transmit sensory information to the cortex. Our aim was to quantify the metabolic cost of different temporal patterns of neural activity across a range of average firing rates. We used a biophysically-realistic model of a TC relay neuron to simulate tonic and burst patterns of firing. We calculated the metabolic cost by converting the calculated ion fluxes into the demand for ATP to maintain homeostasis of intracellular ion concentrations. Most energy was expended on reversing Na⁺ entry during action potentials and pumping Ca²⁺ out of the cell. Average firing rate determined the ATP cost across firing patterns by controlling the overall number of spikes. Varying intraburst frequency or spike number in each burst influenced the metabolic cost by altering the interactions of inward and outward currents on multiple timescales, but temporal pattern contributed substantially less to the metabolic demand of neural activity as compared to average firing rate. These predictions should be considered when interpreting findings of functional imaging studies that rely of estimates of neuronal metabolic demand, e.g., functional magnetic resonance imaging.

Small Changes in Inter-Pulse-Intervals Can Cause Synchronized Neuronal Firing During High-Frequency Stimulations in Rat Hippocampus

Deep brain stimulation (DBS) traditionally utilizes electrical pulse sequences with a constant frequency, i.e., constant inter-pulse-interval (IPI), to treat certain brain disorders in clinic. Stimulation sequences with varying frequency have been investigated recently to improve the efficacy of existing DBS therapy and to develop new treatments. However, the effects of such sequences are inconclusive. The present study tests the hypothesis that stimulations with varying IPI can generate neuronal activity markedly different from the activity induced by stimulations with constant IPI. And, the crucial factor causing the distinction is the relative differences in IPI lengths rather than the absolute lengths of IPI nor the average lengths of IPI. In rat experiments in vivo, responses of neuronal populations to applied stimulation sequences were collected during stimulations with both constant IPI (control) and random IPI. The stimulations were applied in the efferent fibers antidromically (in alveus) or in the afferent fibers orthodromically (in Schaffer collaterals) of pyramidal cells, the principal cells of hippocampal CA1 region. Amplitudes and areas of population spike (PS) waveforms were used to evaluate the neuronal responses induced by different stimulation paradigms. During the periods of both antidromic and orthodromic high-frequency stimulation (HFS), the HFS with random IPI induced synchronous neuronal firing with large PS even if the lengths of random IPI were limited to a small range of 5-10 ms, corresponding to a frequency range 100-200 Hz. The large PS events did not appear during control stimulations with a constant frequency at 100, 200, or 130 Hz (i.e., the mean frequency of HFS with random IPI uniformly distributed within 5-10 ms). Presumably, nonlinear dynamics in neuronal responses to random IPI might cause the generation of synchronous firing under the situation without any long pauses in HFS sequences. The results indicate that stimulations with random IPI can generate salient impulses to brain tissues and modulate the synchronization of neuronal activity, thereby providing potential stimulation paradigms for extending DBS therapy in treating more brain diseases, such as disorders of consciousness and vegetative states.

Temporal Pattern of Electrical Stimulation is a New Dimension of Therapeutic Innovation

Artificial activation of the nervous system requires selection of appropriate stimulation parameters including stimulation amplitude, stimulation pulse duration, and stimulation pulse repetition rate. The temporal pattern of stimulation, i.e., the timing between stimulation pulses, is a novel dimension of stimulation parameter tuning. The effects evoked by artificial activation of the nervous system are dependent on the pattern of stimulation, and different patterns of stimulation, even when delivered at the same average rate, evoke different functional effects, different changes in synaptic plasticity, and even different patterns of gene expression. Non-regular temporal patterns of stimulation offer the opportunity to improve the efficacy and efficiency of therapeutic stimulation as well as to manipulate other processes in the nervous system. The potential design space for sequences of varying interpulse intervals is exceedingly large and sound approaches to design stimulation patterns are required as an empirical approach is not practical.

Frequency-dependent antidromic activation in thalamocortical relay neurons: effects of synaptic inputs

OBJECTIVE

Deep brain stimulation (DBS) generates action potentials (APs) in presynaptic axons and fibers of passage. The APs may be antidromically propagated to invade the cell body and/or orthodromically transmitted to downstream structures, thereby affecting widespread targets distant from the electrode. Activation of presynaptic terminals also causes trans-synaptic effects, which in turn alter the excitability of the post-synaptic neurons. Our aim was to determine how synaptic inputs affect the antidromic invasion of the cell body.

APPROACH

We used a biophysically-based multi-compartment model to simulate antidromic APs in thalamocortical relay (TC) neurons. We applied distributed synaptic inputs to the model and quantified how excitatory and inhibitory inputs contributed to the fidelity of antidromic activation over a range of antidromic frequencies.

MAIN RESULTS

Antidromic activation exhibited strong frequency dependence, which arose from the hyperpolarizing afterpotentials in the cell body and its respective recovery cycle. Low-frequency axonal spikes faithfully invaded the soma, whereas frequent failures of antidromic activation occurred at high frequencies. The frequency-dependent pattern of the antidromic activation masked burst-driver inputs to TC neurons from the cerebellum in a frequency-dependent manner. Antidromic activation also depended on the excitability of the cell body. Excitatory synaptic inputs improved the fidelity of antidromic activation by increasing the excitability, and inhibitory inputs suppressed antidromic activation by reducing soma excitability. Stimulus-induced depolarization of neuronal segments also facilitated antidromic propagation and activation.

SIGNIFICANCE

The results reveal that synaptic inputs, stimulus frequency, and electrode position regulate antidromic activation of the cell body during extracellular stimulation. These findings provide a biophysical basis for interpreting the widespread inhibition/activation of target nuclei during DBS.

Recent Trends in the Use of Electrical Neuromodulation in Parkinson's Disease

PURPOSE OF REVIEW

This review aims to survey recent trends in electrical forms of neuromodulation, with a specific application to Parkinson's disease (PD). Emerging trends are identified, highlighting synergies in state-of-the-art neuromodulation strategies, with directions for future improvements in stimulation efficacy suggested.

RECENT FINDINGS

Deep brain stimulation remains the most common and effective form of electrical stimulation for the treatment of PD. Evidence suggests that transcranial direct current stimulation (tDCS) most likely impacts the motor symptoms of the disease, with the most prominent results relating to rehabilitation. However, utility is limited due to its weak effects and high variability, with medication state a key confound for efficacy level. Recent innovations in transcranial alternating current stimulation (tACS) offer new areas for investigation.

SUMMARY

Our understanding of the mechanistic foundations of electrical current stimulation is advancing and as it does so, trends emerge which steer future clinical trials towards greater efficacy.

Reengineering deep brain stimulation for movement disorders: Emerging technologies

Deep brain stimulation (DBS) is a neurosurgical technique, which consists of continuous delivery of an electrical pulse through chronically implanted electrodes connected to a neurostimulator, programmable in amplitude, pulse width, frequency, and stimulation channel. DBS is a promising treatment option for addressing severe and drug-resistant movement disorders. The success of DBS therapy is a combination of surgical implantation techniques, device technology, and clinical programming strategies. Changes in device settings require highly trained and experienced clinicians to achieve maximal therapeutic benefit for each targeted symptom, and optimization of stimulation parameters can take many visits. Thus, the development of innovative DBS technologies that can optimize the clinical implementation of DBS will lead to wider scale utilization. This review aims to present engineering approaches that have the potential to improve clinical outcomes of DBS, focusing on the development novel temporal patterns, innovative electrode designs, computational models to guide stimulation, closed-loop DBS, and remote programming.

Higher-order power harmonics of pulsed electrical stimulation modulates corticospinal contribution of peripheral nerve stimulation

It is well established that electrical-stimulation frequency is crucial to determining the scale of induced neuromodulation, particularly when attempting to modulate corticospinal excitability. However, the modulatory effects of stimulation frequency are not only determined by its absolute value but also by other parameters such as power at harmonics. The stimulus pulse shape further influences parameters such as excitation threshold and fiber selectivity. The explicit role of the power in these harmonics in determining the outcome of stimulation has not previously been analyzed. In this study, we adopted an animal model of peripheral electrical stimulation that includes an amplitude-adapted pulse train which induces force enhancements with a corticospinal contribution. We report that the electrical-stimulation-induced force enhancements were correlated with the amplitude of stimulation power harmonics during the amplitude-adapted pulse train. In an exploratory analysis, different levels of correlation were observed between force enhancement and power harmonics of 20–80 Hz (r = 0.4247, p = 0.0243), 100–180 Hz (r = 0.5894, p = 0.0001), 200–280 Hz (r = 0.7002, p < 0.0001), 300–380 Hz (r = 0.7449, p < 0.0001), 400–480 Hz (r = 0.7906, p < 0.0001), 500–600 Hz (r = 0.7717, p < 0.0001), indicating a trend of increasing correlation, specifically at higher order frequency power harmonics. This is a pilot, but important first demonstration that power at high order harmonics in the frequency spectrum of electrical stimulation pulses may contribute to neuromodulation, thus warrant explicit attention in therapy design and analysis.

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.

Optimized temporal pattern of brain stimulation designed by computational evolution

Brain stimulation is a promising therapy for several neurological disorders, including Parkinson's disease. Stimulation parameters are selected empirically and are limited to the frequency and intensity of stimulation. We varied the temporal pattern of deep brain stimulation to ameliorate symptoms in a parkinsonian animal model and in humans with Parkinson's disease. We used model-based computational evolution to optimize the stimulation pattern. The optimized pattern produced symptom relief comparable to that from standard high-frequency stimulation (a constant rate of 130 or 185 Hz) and outperformed frequency-matched standard stimulation in a parkinsonian rat model and in patients. Both optimized and standard high-frequency stimulation suppressed abnormal oscillatory activity in the basal ganglia of rats and humans. The results illustrate the utility of model-based computational evolution of temporal patterns to increase the efficiency of brain stimulation in treating Parkinson's disease and thereby reduce the energy required for successful treatment below that of current brain stimulation paradigms.

Computational modeling to improve treatments for essential tremor

Essential tremor (ET) is a neurological disorder of unknown etiology that is typically characterized by an involuntary periodic movement of the upper limbs. No longer considered monosymptomatic, ET patients often have additional motor and even cognitive impairments. Although there are several pharmacological treatments, no drugs have been developed specifically for ET [1], and 30-70% of patients are medication-refractory [2]. A subset of medication-refractory patients may benefit from electrical deep brain stimulation (DBS) of the ventral intermediate nucleus of the thalamus (VIM), which receives cerebellar inputs. Abnormal cerebellar input to VIM is presumed to be a major contributor to tremor symptoms, which is alleviated by DBS. Computational modeling of the effects of DBS in VIM has been a powerful tool to design DBS protocols to reduce tremor activity. However, far less is known about how these therapies affect non-tremor symptoms, and more experimental and computational modeling work is required to address these growing considerations. Models capable of addressing multiple facets of ET will lead to novel, more efficient treatment.