Illusions of Self-Motion during Magnetic Resonance-Guided Focused Ultrasound Thalamotomy for Tremor.

OBJECTIVE: Brain networks mediating vestibular perception of self-motion overlap with those mediating balance. A systematic mapping of vestibular perceptual pathways in the thalamus may reveal new brain modulation targets for improving balance in neurological conditions. METHODS: Here, we systematically report how magnetic resonance-guided focused ultrasound surgery of the nucleus ventralis intermedius of the thalamus commonly evokes transient patient-reported illusions of self-motion. In 46 consecutive patients, we linked the descriptions of self-motion to sonication power and 3-dimensional (3D) coordinates of sonication targets. Target coordinates were normalized using a standard atlas, and a 3D model of the nucleus ventralis intermedius and adjacent structures was created to link sonication target to the illusion. RESULTS: A total of 63% of patients reported illusions of self-motion, which were more likely with increased sonication power and with targets located more inferiorly along the rostrocaudal axis. Higher power and more inferiorly targeted sonications increased the likelihood of experiencing illusions of self-motion by 4 and 2 times, respectively (odds ratios = 4.03 for power, 2.098 for location). INTERPRETATION: The phenomenon of magnetic vestibular stimulation is the most plausible explanation for these illusions of self-motion. Temporary unilateral modulation of vestibular pathways (via magnetic resonance-guided focused ultrasound) unveils the central adaptation to the magnetic field-induced peripheral vestibular bias, leading to an explicable illusion of motion. Consequently, systematic mapping of vestibular perceptual pathways via magnetic resonance-guided focused ultrasound may reveal new intracerebral targets for improving balance in neurological conditions. ANN NEUROL 2024;96:121-132.

The evolution of ventral intermediate nucleus targeting in MRI-guided focused ultrasound thalamotomy for essential tremor: an international multi-center evaluation.

Background: The ventral intermediate nucleus (VIM) is the premiere target in magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy for tremor; however, there is no consensus on the optimal coordinates for ablation. This study aims to ascertain the various international VIM targeting approaches (VIM-TA) and any evolution in practice. Methods: International MRgFUS centers were invited to share VIM-TAs in 2019 and 2021. Analyses of any modification in practice and of anatomical markers and/or tractography in use were carried out. Each VIM-TA was mapped in relation to the mid-commissural point onto a 3D thalamic nucleus model created from the Schaltenbrand-Wahren atlas. Results: Of the 39 centers invited, 30 participated across the study period, providing VIM-TAs from 26 centers in 2019 and 23 in 2021. The results are reported as percentages of the number of participating centers in that year. In 2019 and 2021, respectively, 96.2% (n = 25) and 95.7% (n = 22) of centers based their targeting on anatomical landmarks rather than tractography. Increased adoption of tractography in clinical practice and/or for research was noted, changing from 34.6% to 78.3%. There was a statistically significant change in VIM-TAs in the superior-inferior plane across the study period; the percentage of VIM-TAs positioned 2 mm above the intercommissural line (ICL) increased from 16.0% in 2019 to 40.9% in 2021 (WRST, p < 0.05). This position is mapped at the center of VIM on the 3D thalamic model created based on the Schaltenbrand-Wahren atlas. In contrast, the VIM-TA medial-lateral and anterior-posterior positions remained stable. In 2022, 63.3% of participating centers provided the rationale for their VIM-TAs and key demographics. The centers were more likely to target 2 mm above the ICL if they had increased experience (more than 100 treatments) and/or if they were North American. Conclusion: Across the study period, FUS centers have evolved their VIM targeting superiorly to target the center of the VIM (2 mm above the ICL) and increased the adoption of tractography to aid VIM localization. This phenomenon is observed across autonomous international centers, suggesting that it is a more optimal site for FUS thalamotomy in tremors.

A generic optimization and learning framework for Parkinson disease via speech and handwritten records.

Parkinson's disease (PD) is a neurodegenerative disorder with slow progression whose symptoms can be identified at late stages. Early diagnosis and treatment of PD can help to relieve the symptoms and delay progression. However, this is very challenging due to the similarities between the symptoms of PD and other diseases. The current study proposes a generic framework for the diagnosis of PD using handwritten images and (or) speech signals. For the handwriting images, 8 pre-trained convolutional neural networks (CNN) via transfer learning tuned by Aquila Optimizer were trained on the NewHandPD dataset to diagnose PD. For the speech signals, features from the MDVR-KCL dataset are extracted numerically using 16 feature extraction algorithms and fed to 4 different machine learning algorithms tuned by Grid Search algorithm, and graphically using 5 different techniques and fed to the 8 pretrained CNN structures. The authors propose a new technique in extracting the features from the voice dataset based on the segmentation of variable speech-signal-segment-durations, i.e., the use of different durations in the segmentation phase. Using the proposed technique, 5 datasets with 281 numerical features are generated. Results from different experiments are collected and recorded. For the NewHandPD dataset, the best-reported metric is 99.75% using the VGG19 structure. For the MDVR-KCL dataset, the best-reported metrics are 99.94% using the KNN and SVM ML algorithms and the combined numerical features; and 100% using the combined the mel-specgram graphical features and VGG19 structure. These results are better than other state-of-the-art researches.

What the near Future Holds for Sacubitril/Valsartan: A Summary of Major Ongoing Studies.

Early research on neprilysin inhibition showed that sacubitril/valsartan, a combination of the valsartan and the neprilysin inhibitor sacubitril, was superior to enalapril in patients with heart failure with reduced ejection fraction (HFrEF) in the PARADIGM-HF study in 2014. Therefore, for patients with HFrEF, worldwide recommendations have been reformed to include sacubitril/valsartan. In addition, sacubitril/valsartan has been investigated in other cardiovascular disease states, such as patients with heart failure and preserved ejection fraction (HFpEF) and following myocardial infarction (MI) events. In February 2021, the FDA expanded the indication use of sacubitril/valsartan to include the HFpEF patient population based on the results of the PARAGON-HF trial. However, randomized clinical trials post-MI did not show promising results. Sacubitril/valsartan is currently being investigated in many other cardiovascular and non-cardiovascular conditions. This review aims to shed light and summarize the ongoing sacubitril/valsartan registered studies on the United States National Library of Medicine clinical trials registry.

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.

Traditional Trial and Error versus Neuroanatomic 3-Dimensional Image Software-Assisted Deep Brain Stimulation Programming in Patients with Parkinson Disease.

BACKGROUND: Programming deep brain stimulation (DBS) settings in patients with Parkinson disease (PD) is challenging and time consuming because of the vast number of possible parameter combinations. This results in long sessions that can be exhausting for the patients and physicians. GUIDE (Boston Scientific) is a 3-dimensional neuroanatomic visual software that precisely visualizes the location of the DBS electrode in the subthalamic nucleus (STN). The objective of this paper is to compare the duration and clinical effects of traditional trial and error versus GUIDE-assisted DBS programming in 10 patients with PD treated with STN DBS. METHODS: For each patient, neurostimulation parameters were selected with GUIDE to create a stimulation field encompassing the dorsal part of the STN. On programming day, each patient was assessed with both traditional and GUIDE approaches using a crossover design. For GUIDE-assisted sessions, the patients were programmed directly with the DBS settings obtained with the stimulated field model, and if necessary, parameters were adjusted to achieve optimal clinical response. Clinical improvement was assessed with Unified Parkinson's Disease Rating Scale scores for limb bradykinesia, tremor, and rigidity. RESULTS: In 7 patients, DBS settings obtained with GUIDE led to suboptimal clinical improvement and mild adjustments were required. After these adjustments, the magnitude of clinical improvement with the 2 approaches was comparable (P = 0.8219). Programming time with GUIDE was significantly shorter than with the traditional programming approach (P < 0.0001). CONCLUSIONS: Visualization of stimulation fields with GUIDE provides useful information to achieve a clinical improvement comparable with that obtained with the traditional trial and error approach, but with shorter and more efficient programming sessions.

Mapping the current flow in sacral nerve stimulation using computational modelling.

Sacral nerve stimulation (SNS) is an established treatment for faecal incontinence involving the implantation of a quadripolar electrode into a sacral foramen, through which an electrical stimulus is applied. Little is known about the induced spread of electric current around the SNS electrode and its effect on adjacent tissues, which limits optimisation of this treatment. The authors constructed a 3-dimensional imaging based finite element model in order to calculate and visualise the stimulation induced current and coupled this to biophysical models of nerve fibres. They investigated the impact of tissue inhomogeneity, electrode model choice and contact configuration and found a number of effects. (i) The presence of anatomical detail changes the estimate of stimulation effects in size and shape. (ii) The difference between the two models of electrodes is minimal for electrode contacts of the same length. (iii) Surprisingly, in this arrangement of electrode and neural fibre, monopolar and bipolar stimulation induce a similar effect. (iv) Interestingly when the active contact is larger, the volume of tissue activated reduces. This work establishes a protocol to better understand both therapeutic and adverse stimulation effects and in the future will enable patient-specific adjustments of stimulation parameters.

Chronic Stroke Survivors Improve Reaching Accuracy by Reducing Movement Variability at the Trained Movement Speed.

BACKGROUND: Recovery from stroke is often said to have "plateaued" after 6 to 12 months. Yet training can still improve performance even in the chronic phase. Here we investigate the biomechanics of accuracy improvements during a reaching task and test whether they are affected by the speed at which movements are practiced. METHOD: We trained 36 chronic stroke survivors (57.5 years, SD ± 11.5; 10 females) over 4 consecutive days to improve endpoint accuracy in an arm-reaching task (420 repetitions/day). Half of the group trained using fast movements and the other half slow movements. The trunk was constrained allowing only shoulder and elbow movement for task performance. RESULTS: Before training, movements were variable, tended to undershoot the target, and terminated in contralateral workspace (flexion bias). Both groups improved movement accuracy by reducing trial-to-trial variability; however, change in endpoint bias (systematic error) was not significant. Improvements were greatest at the trained movement speed and generalized to other speeds in the fast training group. Small but significant improvements were observed in clinical measures in the fast training group. CONCLUSIONS: The reduction in trial-to-trial variability without an alteration to endpoint bias suggests that improvements are achieved by better control over motor commands within the existing repertoire. Thus, 4 days' training allows stroke survivors to improve movements that they can already make. Whether new movement patterns can be acquired in the chronic phase will need to be tested in longer term studies. We recommend that training needs to be performed at slow and fast movement speeds to enhance generalization.

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.

Dopamine Activation Preserves Visual Motion Perception Despite Noise Interference of Human V5/MT.

UNLABELLED: When processing sensory signals, the brain must account for noise, both noise in the stimulus and that arising from within its own neuronal circuitry. Dopamine receptor activation is known to enhance both visual cortical signal-to-noise-ratio (SNR) and visual perceptual performance; however, it is unknown whether these two dopamine-mediated phenomena are linked. To assess this, we used single-pulse transcranial magnetic stimulation (TMS) applied to visual cortical area V5/MT to reduce the SNR focally and thus disrupt visual motion discrimination performance to visual targets located in the same retinotopic space. The hypothesis that dopamine receptor activation enhances perceptual performance by improving cortical SNR predicts that dopamine activation should antagonize TMS disruption of visual perception. We assessed this hypothesis via a double-blinded, placebo-controlled study with the dopamine receptor agonists cabergoline (a D2 agonist) and pergolide (a D1/D2 agonist) administered in separate sessions (separated by 2 weeks) in 12 healthy volunteers in a William's balance-order design. TMS degraded visual motion perception when the evoked phosphene and the visual stimulus overlapped in time and space in the placebo and cabergoline conditions, but not in the pergolide condition. This suggests that dopamine D1 or combined D1 and D2 receptor activation enhances cortical SNR to boost perceptual performance. That local visual cortical excitability was unchanged across drug conditions suggests the involvement of long-range intracortical interactions in this D1 effect. Because increased internal noise (and thus lower SNR) can impair visual perceptual learning, improving visual cortical SNR via D1/D2 agonist therapy may be useful in boosting rehabilitation programs involving visual perceptual training. SIGNIFICANCE STATEMENT: In this study, we address the issue of whether dopamine activation improves visual perception despite increasing sensory noise in the visual cortex. We show specifically that dopamine D1 (or combined D1/D2) receptor activation enhances the cortical signal-to-noise-ratio to boost perceptual performance. Together with the previously reported effects of dopamine upon brain plasticity and learning (Wolf et al., 2003; Hansen and Manahan-Vaughan, 2014), our results suggest that combining rehabilitation with dopamine agonists could enhance both the saliency of the training signal and the long-term effects on brain plasticity to boost rehabilitation regimens for brain injury.

Reversing the polarity of bipolar stimulation in deep brain stimulation for essential tremor: a theoretical explanation for a useful clinical intervention.

The quadripolar electrodes used for deep brain stimulation are designed to give flexibility in contact configuration, optimize therapeutic effect, and minimize side-effects. A patient with essential tremor did not tolerate a bipolar setting due to the emergence of a pulling sensation in her face. However, when the polarity of the contacts was reversed, a 70% higher voltage was tolerated. Using an electric field model, we predicted that this effect was due to the proximity of the topmost contact to the internal capsule. Post-operative imaging supported this prediction. These results demonstrate how a multi-disciplinary approach allows us to optimize parameter settings.

An automated approach towards detecting complex behaviours in deep brain oscillations.

Extracting event-related potentials (ERPs) from neurological rhythms is of fundamental importance in neuroscience research. Standard ERP techniques typically require the associated ERP waveform to have low variance, be shape and latency invariant and require many repeated trials. Additionally, the non-ERP part of the signal needs to be sampled from an uncorrelated Gaussian process. This limits methods of analysis to quantifying simple behaviours and movements only when multi-trial data-sets are available. We introduce a method for automatically detecting events associated with complex or large-scale behaviours, where the ERP need not conform to the aforementioned requirements. The algorithm is based on the calculation of a detection contour and adaptive threshold. These are combined using logical operations to produce a binary signal indicating the presence (or absence) of an event with the associated detection parameters tuned using a multi-objective genetic algorithm. To validate the proposed methodology, deep brain signals were recorded from implanted electrodes in patients with Parkinson's disease as they participated in a large movement-based behavioural paradigm. The experiment involved bilateral recordings of local field potentials from the sub-thalamic nucleus (STN) and pedunculopontine nucleus (PPN) during an orientation task. After tuning, the algorithm is able to extract events achieving training set sensitivities and specificities of [87.5 ± 6.5, 76.7 ± 12.8, 90.0 ± 4.1] and [92.6 ± 6.3, 86.0 ± 9.0, 29.8 ± 12.3] (mean ± 1 std) for the three subjects, averaged across the four neural sites. Furthermore, the methodology has the potential for utility in real-time applications as only a single-trial ERP is required.

Movement speed is biased by prior experience.

How does the motor system choose the speed for any given movement? Many current models assume a process that finds the optimal balance between the costs of moving fast and the rewards of achieving the goal. Here, we show that such models also need to take into account a prior representation of preferred movement speed, which can be changed by prolonged practice. In a time-constrained reaching task, human participants made 25-cm reaching movements within 300, 500, 700, or 900 ms. They were then trained for 3 days to execute the movement at either the slowest (900-ms) or fastest (300-ms) speed. When retested on the 4th day, movements executed under all four time constraints were biased toward the speed of the trained movement. In addition, trial-to-trial variation in speed of the trained movement was significantly reduced. These findings are indicative of a use-dependent mechanism that biases the selection of speed. Reduced speed variability was also associated with reduced errors in movement amplitude for the fast training group, which generalized nearly fully to a new movement direction. In contrast, changes in perpendicular error were specific to the trained direction. In sum, our results suggest the existence of a relatively stable but modifiable prior of preferred movement speed that influences the choice of movement speed under a range of task constraints.

An interactive channel model of the Basal Ganglia: bifurcation analysis under healthy and parkinsonian conditions.

Oscillations in the basal ganglia are an active area of research and have been shown to relate to the hypokinetic motor symptoms of Parkinson's disease. We study oscillations in a multi-channel mean field model, where each channel consists of an interconnected pair of subthalamic nucleus and globus pallidus sub-populations.To study how the channels interact, we perform two-dimensional bifurcation analysis of a model of an individual channel, which reveals the critical boundaries in parameter space that separate different dynamical modes; these modes include steady-state, oscillatory, and bi-stable behaviour. Without self-excitation in the subthalamic nucleus a single channel cannot generate oscillations, yet there is little experimental evidence for such self-excitation. Our results show that the interactive channel model with coupling via pallidal sub-populations demonstrates robust oscillatory behaviour without subthalamic self-excitation, provided the coupling is sufficiently strong. We study the model under healthy and Parkinsonian conditions and demonstrate that it exhibits oscillations for a much wider range of parameters in the Parkinsonian case. In the discussion, we show how our results compare with experimental findings and discuss their possible physiological interpretation. For example, experiments have found that increased lateral coupling in the rat basal ganglia is correlated with oscillations under Parkinsonian conditions.

Vestibular activation differentially modulates human early visual cortex and V5/MT excitability and response entropy.

Head movement imposes the additional burdens on the visual system of maintaining visual acuity and determining the origin of retinal image motion (i.e., self-motion vs. object-motion). Although maintaining visual acuity during self-motion is effected by minimizing retinal slip via the brainstem vestibular-ocular reflex, higher order visuovestibular mechanisms also contribute. Disambiguating self-motion versus object-motion also invokes higher order mechanisms, and a cortical visuovestibular reciprocal antagonism is propounded. Hence, one prediction is of a vestibular modulation of visual cortical excitability and indirect measures have variously suggested none, focal or global effects of activation or suppression in human visual cortex. Using transcranial magnetic stimulation-induced phosphenes to probe cortical excitability, we observed decreased V5/MT excitability versus increased early visual cortex (EVC) excitability, during vestibular activation. In order to exclude nonspecific effects (e.g., arousal) on cortical excitability, response specificity was assessed using information theory, specifically response entropy. Vestibular activation significantly modulated phosphene response entropy for V5/MT but not EVC, implying a specific vestibular effect on V5/MT responses. This is the first demonstration that vestibular activation modulates human visual cortex excitability. Furthermore, using information theory, not previously used in phosphene response analysis, we could distinguish between a specific vestibular modulation of V5/MT excitability from a nonspecific effect at EVC.

Structural learning in feedforward and feedback control.

For smooth and efficient motor control, the brain needs to make fast corrections during the movement to resist possible perturbations. It also needs to adapt subsequent movements to improve future performance. It is important that both feedback corrections and feedforward adaptation need to be made based on noisy and often ambiguous sensory data. Therefore, the initial response of the motor system, both for online corrections and adaptive responses, is guided by prior assumptions about the likely structure of perturbations. In the context of correcting and adapting movements perturbed by a force field, we asked whether these priors are hard wired or whether they can be modified through repeated exposure to differently shaped force fields. We found that both feedback corrections to unexpected perturbations and feedforward adaptation to a new force field changed, such that they were appropriate to counteract the type of force field that participants had experienced previously. We then investigated whether these changes were driven by a common mechanism or by two separate mechanisms. Participants experienced force fields that were either temporally consistent, causing sustained adaptation, or temporally inconsistent, causing little overall adaptation. We found that the consistent force fields modified both feedback and feedforward responses. In contrast, the inconsistent force field modified the temporal shape of feedback corrections but not of the feedforward adaptive response. These results indicate that responses to force perturbations can be modified in a structural manner and that these modifications are at least partly dissociable for feedback and feedforward control.

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

Deep brain stimulation (DBS) is a successful surgical therapy used to treat the disabling symptoms of movement disorders such as Parkinson's disease. It involves the chronic stimulation of disorder-specific nuclei. However, the mechanisms that lead to clinical improvements remain unclear. Consequently, this slows the optimization of present-day DBS therapy and hinders its future development and application. We used a computational model to calculate the distribution of electric potential induced by DBS and study the effect of stimulation on the spiking activity of a subthalamic nucleus (STN) projection neuron. We previously showed that such a model can reveal detailed spatial effects of stimulation in the vicinity of the electrode. However, this multi-compartmental STN neuron model can fire in either a burst or tonic mode and, in this study, we hypothesized that the firing mode of the cell will have a major impact on the DBS-induced effects. Our simulations showed that the bursting model exhibits behaviour observed in studies of high-frequency stimulation of STN neurons, such as the presence of a silent period at stimulation offset and frequency-dependent stimulation effects. We validated the model by simulating the clinical parameter settings used for a Parkinsonian patient and showed, in a patient-specific anatomical model, that the region of affected tissue is consistent with clinical observations of the optimal DBS site. Our results demonstrated a method of quantitatively assessing neuronal changes induced by DBS, to maximize therapeutic benefit and minimize unwanted side effects.

Probing V5/MT excitability with transcranial magnetic stimulation following visual motion adaptation to random and coherent motion.

The response to stimulating the visual cortex with transcranial magnetic stimulation (TMS) depends on its initial activation state, for example, visual motion adaptation biases perceived TMS-induced phosphene characteristics (e.g., color). We quantified this state dependence by assessing the probability of reporting a phosphene (P(λ) ) with "threshold" TMS (i.e., the TMS intensity producing P(λ) = 0.5 at baseline) following visual motion adaptation to a random dot motion display. Postadaptation, P(λ) was increased, and this effect was confined to the adapted neuronal population. We then adapted subjects using a population of moving dots of fixed average motion direction with standard deviations (SD) ranging from 1° to 128° (SD fixed for a given trial). P(λ) was significantly increased at all dot motion SDs except SD = 1°. Neuronal adaptation increases the susceptibility of the neuronal population to activation by threshold intensity TMS. Thus the process of neuronal adaption is not necessarily synonymous with a downmodulation of neuronal excitability.

Evaluating the impact of the deep brain stimulation induced electric field on subthalamic neurons: a computational modelling study.

Deep brain stimulation (DBS) is an effective surgical treatment used to alleviate the symptoms of neurological disorders, most commonly movement disorders. However, the mechanism of how the applied stimulus pulses interact with the surrounding neuronal elements is not yet clearly understood, slowing progress and development of this promising therapeutic technology. To extend previous approaches of using isolated, myelinated axon models used to estimate the effect of DBS, we propose that taking into account entire neurons will reveal stimulation induced effects overlooked by previous studies. We compared the DBS induced volume of tissue activated (VTA) using arrays of whole cell models of subthalamic nucleus (STN) excitatory neurons consisting of a cell body and an anatomically accurate dendritic tree, to the common models of axon arrays. Our results demonstrate that STN neurons have a higher excitation threshold than axons, as stimulus amplitudes 10 times as large elicit a VTA range a fifth of the distance from the electrode surface. However, the STN neurons do show a change in background firing rate in response to stimulation, even when they are classified as sub-threshold by the VTA definition. Furthermore the whole neuron models are sensitive to regions of high current density, as the distribution of firing is centred on the electrode contact edges These results demonstrate the importance of accurate neuron models for fully appreciating the spatial effects of DBS on the immediate surrounding brain volume within small distances of the electrode, which are overlooked by previous models of isolated axons and individual neurons.

Investigating the depth electrode-brain interface in deep brain stimulation using finite element models with graded complexity in structure and solution.

Deep brain stimulation (DBS) is an increasingly used surgical therapy for a range of neurological disorders involving the long-term electrical stimulation of various regions of the human brain in a disorder specific manner. Despite being used for the last 20 years, the underlying mechanisms are still not known, and disputed. In particular, when the electrodes are implanted into the human brain, an interface is created with changing biophysical properties which may impact on stimulation. We previously defined the electrode-brain interface (EBI) as consisting of three structural elements: the quadripolar DBS electrode, the peri-electrode space and the surrounding brain tissue. In order to understand more about the nature of this EBI, we used structural computational models of this interface, and estimated the effects of stimulation using coupled axon models. These finite element models differ in complexity, each highlighting a different feature of the EBI's effect on the DBS-induced electric field. We show that the quasi-static models are sufficient to demonstrate the difference between the acute and chronic clinical stages post-implantation. However, the frequency-dependent models are necessary as the waveform shaping has a major influence on the activation of neuronal fibres. We also investigate anatomical effects on the electric field, by taking specific account of the ventricular system in the human brain. Taken together, these models allow us to visualise the static, dynamic and target specific properties of the DBS-induced field in the surrounding brain regions.