Adaptive Parameter Selection for Deep Brain Stimulation in Parkinson’s Disease

Each year, around 60,000 people are diagnosed with Parkinson’s disease (PD) and the economic burden of PD is at least $14.4 billion a year in the United States. Pharmaceutical costs for a Parkinson’s patient can be reduced from$12,000 to $6,000 per year with the addition of neuromodulation therapies such as Deep Brain Stimulation (DBS), transcranial Direct Current Stimulation (tDCS), Transcranial Magnetic Stimulation (TMS), etc. In neurodegenerative disorders such as PD, deep brain stimulation (DBS) is a desirable approach when the medication is less effective for treating the symptoms. DBS incorporates transferring electrical pulses to a specific tissue of the central nervous system and obtaining therapeutic results by modulating the neuronal activity of that region. The hyperkinetic symptoms of 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 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 work, we developed several computational models of four interacting nuclei of the BG as well as considering the Thalamo-Cortical local effects on the oscillatory dynamics. These models are able to capture the emergence of 34 Hz beta band oscillations seen in the Local Field Potential (LFP) recordings of the PD state. Traditional High Frequency Stimulations (HFS) 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 investigated the effects of the stimulation waveform and interphase delays on the overall efficiency and efficacy of DBS. We also 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 amplitude needed. The efficiency and efficacy 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 behaviors observed in neurological disorders. Adaptive stimulation can open doors towards simultaneous stimulation with MRI recordings. We additionally propose a new pipeline to investigate the effect of Transcranial Magnetic Stimulation (TMS) on patient specific models. The pipeline allows us to generate a full head segmentation based on each individual MRI data. In the next step, the neurosurgeon can adaptively choose the proper location of stimulation and transmit accurate magnetic field with this pipeline

Deep Brain Stimulation (DBS) Applications

The issue is dedicated to applications of Deep Brain Stimulation and, in this issue, we would like to highlight the new developments that are taking place in the field. These include the application of new technology to existing indications, as well as ‘new’ indications. We would also like to highlight the most recent clinical evidence from international multicentre trials. The issue will include articles relating to movement disorders, pain, psychiatric indications, as well as emerging indications that are not yet accompanied by clinical evidence. We look forward to your expert contribution to this exciting issue

Reinforcement Learning in a Spiking Neural Model of Striatum Plasticity

The basal ganglia (BG), and more specifically the striatum, have long been proposed to play an essential role in action-selection based on a reinforcement learning (RL) paradigm. However, some recent findings, such as striatal spike-timing-dependent plasticity (STDP) or striatal lateral connectivity, require further research and modelling as their respective roles are still not well understood. Theoretical models of spiking neurons with homeostatic mechanisms, lateral connectivity, and reward-modulated STDP have demonstrated a remarkable capability to learn sensorial patterns that statistically correlate with a rewarding signal. In this article, we implement a functional and biologically inspired network model of the striatum, where learning is based on a previously proposed learning rule called spike-timing-dependent eligibility (STDE), which captures important experimental features in the striatum. The proposed computational model can recognize complex input patterns and consistently choose rewarded actions to respond to such sensorial inputs. Moreover, we assess the role different neuronal and network features, such as homeostatic mechanisms and lateral inhibitory connections, play in action-selection with the proposed model. The homeostatic mechanisms make learning more robust (in terms of suitable parameters) and facilitate recovery after rewarding policy swapping, while lateral inhibitory connections are important when multiple input patterns are associated with the same rewarded action. Finally, according to our simulations, the optimal delay between the action and the dopaminergic feedback is obtained around 300 ms, as demonstrated in previous studies of RL and in biological studies

Investigation of intraoperative accelerometer data recording for safer and improved target selection for deep brain stimulation

Background: Deep Brain Stimulation (DBS) is a well established surgical treatment for Parkinson’s Disease (PD) and Essential Tremor (ET). Electrical leads are surgically implanted in the deeply seated structures in the brain and chronically stimulated. The location of the lead with respect to the anatomy is very important for optimal treatment. Therefore, clinicians carefully plan the surgery, record electrophysiological signals from the region of interest and perform stimulation tests to identify the best location to permanently place the leads. Nevertheless, there are certain aspects of the surgery that can still be improved. Firstly, therapeutic effects of stimulation are estimated by visually evaluating changes in tremor or passively moving patient's limb to evaluate changes in rigidity. These methods are subjective and depend heavily on the experience of the evaluator. Secondly, a significant amount of patient data is collected before and during the surgery like various CT and MR images, surgical planning information, electrophysiological recordings and results of stimulation tests. These are not fully utilized at the time of choosing the position for lead placement as they are either not available or acquired on separate systems or in the form of paper notes only. Thirdly, studies have shown that the current target structures to implant the leads (Subthalamic Nucleus (STN) for PD and Ventral Intermediate Nucleus (VIM) for ET) may not be the only ones responsible for the therapeutic effects. The objective of this doctoral work is to develop new methods that help clinicians subdue the above limitations which could in the long term improve the DBS therapy. Method: After a thorough review of the existing literature, specifically customized solutions were designed for the shortcomings described above. A new method to quantitatively evaluate tremor during DBS surgery using acceleration sensor was developed. The method was then adapted to measure acceleration of passive movements and to evaluate changes in rigidity through it. Data from 30 DBS surgeries was collected by applying these methods in two clinical studies: one in Centre Hospitalier Universitaire, Clermont-Ferrand, France and another multi-center study in Universitäspital Basel and Inselspital Bern in Switzerland. To study the role of different anatomical structures in the therapeutic and adverse effects of stimulation, the data collected during the study was analysed using two methods. The first classical approach was to classify the data based on the anatomical structure in which the stimulating contact of the electrode was located. The second advanced approach was to use patient-specific Finite Element Method (FEM) simulations of the Electric Field (EF) to estimate the spatial distribution of stimulation in the structures surrounding the electrode. Such simulations of the adverse effect inducing stimulation current amplitudes are used to visualize the boundaries of safe stimulation and identify structures that could be responsible for these effects. In addition, the patient-specific simulations are also used to develop a new method called "Improvement Maps" to generate 2D and 3D visualization of intraoperative stimulation test results with the patient images and surgical planning. This visualization summarized the stimulation test results by dividing the explored area into multiple regions based on the improvement in symptoms as measured by the accelerometric methods. Results: The accelerometric method successfully measured changes in tremor and rigidity. Standard deviation, signal energy and spectral amplitude of dominant frequency correlated with changes in the symptoms. Symptom suppressing stimulation current amplitudes identified through quantitative methods were lower than those identified through the subjective methods. Comparison of anatomical targets using the accelerometric data showed that to suppress rigidity in PD patients, stimulation current needed was marginally higher for Fields of Forel (FF) and Zona Incerta (ZI) compared to STN. On the other hand, the adverse effect occurrence rate was significantly lower in ZI and FF, indicating them to be better targets compared to STN. Similarly, for ET patients, other thalamic nuclei like the Intermediolateral (InL) and Ventro-Oral (VO) as well as the Pre-Lemniscal Radiations (PLR) are as efficient in suppressing tremor as the VIM but have lower occurrence of adverse effects. Volumetric analysis of spatial distribution of stimulation agreed with these results suggesting that the structures other than the VIM could also play a role in therapeutic effects of stimulation. The visualization of the adverse effect simulations clearly show the structures which could be responsible for such effects e.g. stimulation in the internal capsula induced pyramidal effects. These findings concur with the published literature. With regard to the improvement maps, the clinicians found them intuitive and easy to use to identify the optimal position for lead placement. If the maps were available during the surgery, the clinicians' choice of lead placement would have been different. Conclusion: This doctoral work has shown that modern techniques like quantitative symptom evaluation and electric field simulations can suppress the existing drawbacks of the DBS surgery. Furthermore, these methods along with 3D visualization of data can simplify tasks for clinicians of optimizing lead placement. Better placement of the DBS lead can potentially reduce adverse effects and increase battery life of implanted pulse generator, resulting in better therapy for patients

Functional Networks in Parkinson’s Disease

Parkinson’s Disease (PD) is a common neurodegenerative condition characterised pathologically by progressive dopaminergic cell loss in the substantia nigra pars compacta, dopamine depletion and resulting cortico- basal ganglia circuit dysfunction. There is a considerable variation in symptoms and treatment response between patients and therefore a need to individualise treatments, such as dopamine replacement therapy, and deep brain stimulation (DBS). We therefore require a better understanding of how different motor and non-motor symptoms emerge from the cortico-basal ganglia dysfunction characteristic of PD. In this thesis, I investigated the hypothesis that distinct symptoms in PD may be due to the dysfunction of distinct cortico-basal ganglia circuits. I characterised cortico-basal ganglia coupling by simultaneously recording cortical activity with magnetoencephalography (MEG) and basal ganglia activity from intracranial electrodes placed during DBS surgery for PD. Coupling was measured in terms of coherence – a frequency specific measure of coupling. I found that resting cortico-basal ganglia networks had distinct cortical topographies at different frequencies. Frontal regions coupled to both the subthalamic nucleus (STN) and the pedunculopontine nucleus region (PPNR) in the beta frequency band whilst temporal, parietal and cerebellar areas coupled in the alpha range. I hypothesised that activity in the frontal beta network may relate to executive function, and found that local synchronisation in two frontal cortical hubs was related to stopping an on-going movement – a crucial executive function. In a related experiment in PD patients, transient frontal – basal ganglia coupling was again apparent during motor inhibition, but how this is related to behavioural performance needs further investigation. These results are useful in highlighting how cortico-basal ganglia networks can be separated both spatially and spectrally and how the function and dysfunction of these networks can be interrogated in PD patients. Future work should determine how different stimulation parameters differentially affect these distinct circuits