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

Auf dem Weg zur automatisierten Programmierung der tiefen Hirnstimulation

Background: Deep Brain Stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment option for patients with Parkinson’s Disease (PD). To maximize treatment benefit, stimulation parameters need to be adjusted individually. Currently, this is performed following a trial-and-error approach, which is time-consuming, costly, and challenging for both patients and medical personnel. The recent introduction of directional electrodes has aggravated those difficulties, highlighting the need for more elaborate procedures to tailor DBS parameter selection to the individual patient. Recent studies suggested that the anatomical location of DBS electrodes could be used to predict beneficial stimulation parameters and guide DBS programming procedures. Methods: We developed StimFit, a software to automatically suggest optimal stimulation parameters in PD patients treated with STN-DBS based on reconstructed electrode locations. The software was trained on a dataset of 612 stimulation settings (applied in 31 patients) to predict motor improvement and side-effect probabilities with respect to electrode location and stimulation parameters. Model performance was retrospectively validated within the training cohort and tested on an independent dataset of 19 PD patients. The predictive models were then embedded in a non-linear optimization algorithm to find parameter combinations which would maximize predicted therapeutic benefit. A graphical user interface was designed to allow for a streamlined use of StimFit and the software was made publicly available. Next, StimFit was prospectively applied in 35 PD patients in a double-blind, cross-over trial to assess whether motor benefit of StimFit stimulation parameters would be non-inferior to patients’ standard of care treatment (SoC). Motor performance was evaluated according to the MDS-UPDRS-III under StimFit and SoC stimulation, randomizing the sequence of both conditions in a 1:1 ratio. Results: Motor outcome predictions of the data-driven model integrated in StimFit correlated well with observed outcome within the training cohort (R = 0.57, p < 0.001) as well as in the retrospective test cohort (R = 0.53, p < 0.001). In our prospective clinical trial StimFit and SoC stimulation resulted in clinically significant average motor improvement of 43 and 48 %, respectively. Mean absolute difference of motor outcome between both conditions was -1.6 ± 7.1 (95% CI: [-4.0, 0.9]) establishing non-inferiority of StimFit at the pre-defined margin of -5 points (p = 0.004).Conclusion: Beneficial stimulation parameters can be automatically derived from electrode location using data-driven approaches. Our results hold promise for more efficient and streamlined DBS programming procedures, but additional prospective studies are required to assess the effects of image-based DBS programming on non-motor domains and long-term quality of life.Hintergrund: Die Tiefe Hirnstimulation (THS) des Nucleus subthalamicus (STN) ist eine effektive Therapieoption zur Behandlung des idiopathischen Parkinson-Syndroms (IPS). Hierbei müssen die Stimulationsparameter individuell angepasst werden, was derzeit durch zeit- und ressourcenintensives Austesten erfolgt. Jüngste Studienergebnisse legen nahe, dass Informationen über die anatomische Lage der THS-Elektroden dafür genutzt werden könnten, vorteilhafte Stimulationseinstellungen zu identifizieren und somit die THS-Programmierung zu erleichtern. Methoden: Wir entwickelten eine Software (StimFit), durch welche optimale Stimulationseinstellungen für Patient*innen mit STN-THS auf Basis ihrer individuellen Elektrodenlagen vorgeschlagen werden können. Hierbei wurde ein Trainingsdatensatz von 612 Stimulationseinstellungen (31 Patient*innen) genutzt, um THS-Effekte in Abhängigkeit von Elektrodenlage und Stimulationsparametern zu prädizieren. Vorhersagegenauigkeiten wurden retrospektiv innerhalb des Trainingsdatensatzes, sowie in einer unabhängigen Testkohorte von 19 Patient*innen quantifiziert. Die validierten Vorhersagemodelle wurden dann in einen Optimierungsalgorithmus integriert, um Stimulationseinstellungen mit maximalem (prädizierten) therapeutischen Benefit zu ermitteln. Der Algorithmus wurde in eine grafische Benutzeroberfläche eingebettet und öffentlich zugänglich gemacht. In einer doppelblinden cross-over Studie wurde StimFit dann prospektiv an 35 Patient*innen mit STN-THS angewandt. Hierbei wurden sowohl die von StimFit vorgeschlagenen, als auch die durch traditionelle Optimierungsverfahren ermittelten („Standard of Care“, SoC) Stimulationseinstellungen in randomisierter Reihenfolge eingestellt. Die therapeutischen Effekte der StimFit-Einstellungen wurden mittels des MDS-UPDRS-III quantifiziert und diesbezüglich auf Nicht-Unterlegenheit gegenüber dem SoC untersucht. Ergebnisse: Die durch StimFit prädizierten motorischen Effekte korrelierten mit den empirischen Effekten innerhalb der Trainingskohorte (R = 0,57; p < 0,001) sowie in der retrospektiven Testkohorte (R = 0,53; p < 0,001). In der prospektiven Studie verbesserten sich die motorischen Symptome sowohl unter StimFit- als auch unter SoC-Stimulation (43 und 48 %). Der Summenscore des MDS-UPDRS-III unterschied sich statistisch nicht signifikant um -1,6 ± 7,1 (95% CI: [-4,0; 0,9]) zwischen beiden Stimulationskonditionen. Die Nicht-Unterlegenheit von StimFit konnte bei einer vordefinierten Grenze von -5 Punkten gezeigt werden (p = 0,004). Schlussfolgerungen: Effektive Stimulationseinstellungen können anhand der Elektrodenpositionen durch automatisierte datengetriebene Algorithmen abgeleitet werden und somit die Optimierung der THS-Parameter erleichtern. Weitere prospektive Studien sind notwendig, um Langzeiteffekte und den Einfluss datengetriebener THS-Programmierungsmethoden auf nicht-motorische Domänen und die Lebensqualität der Patient*innen zu ermitteln

Digital twin brain: a bridge between biological intelligence and artificial intelligence

In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities for understanding the complexity of the brain and its emulation by computational systems. Cutting-edge advancements in neuroscience research have revealed the intricate relationship between brain structure and function, while the success of artificial neural networks highlights the importance of network architecture. Now is the time to bring them together to better unravel how intelligence emerges from the brain's multiscale repositories. In this review, we propose the Digital Twin Brain (DTB) as a transformative platform that bridges the gap between biological and artificial intelligence. It consists of three core elements: the brain structure that is fundamental to the twinning process, bottom-layer models to generate brain functions, and its wide spectrum of applications. Crucially, brain atlases provide a vital constraint, preserving the brain's network organization within the DTB. Furthermore, we highlight open questions that invite joint efforts from interdisciplinary fields and emphasize the far-reaching implications of the DTB. The DTB can offer unprecedented insights into the emergence of intelligence and neurological disorders, which holds tremendous promise for advancing our understanding of both biological and artificial intelligence, and ultimately propelling the development of artificial general intelligence and facilitating precision mental healthcare

Technological advances in deep brain stimulation:Towards an adaptive therapy

Parkinson's disease (PD) is neurodegenerative movement disorder and a treatment method called deep brain stimulation (DBS) may considerably reduce the patient’s motor symptoms. The clinical procedure involves the implantation of a DBS lead, consisting of multiple electrode contacts, through which continuous high frequency (around 130 Hz) electric pulses are delivered in the brain. In this thesis, I presented the research which had the goal to improve current DBS technology, focusing on bringing the conventional DBS system a step closer to adaptive DBS, a personalized DBS therapy. The chapters in this thesis can be seen as individual building blocks for such an adaptive DBS system. After the general introduction, the first two chapters, two novel DBS lead designs are studied in a computational model. The model showed that both studied leads were able to exploit the novel distribution of the electrode contacts to shape and steer the stimulation field to activate more neurons in the chosen target compared to the conventional lead, and to counteract lead displacement. In the fourth chapter, an inverse current source density (CSD) method is applied on local field potentials (LFP) measured in a rat model. The pattern of CSD sources can act as a landmark within the STN to locate the potential stimulation target. The fifth and final chapter described the last building block of the DBS system. We introduced an inertial sensors and force sensor based measurement system, which can record hand kinematics and joint stiffness of PD patients. A system which can act as a feedback signal in an adaptive DBS system

Motor symptoms in Parkinson's disease: A unified framework

Parkinson’s disease (PD) is characterized by a range of motor symptoms. Besides the cardinal symptoms (akinesia and bradykinesia, tremor and rigidity), PD patients show additional motor deficits, including: gait disturbance, impaired handwriting, grip force and speech deficits, among others. Some of these motor symptoms (e.g., deficits of gait, speech, and handwriting) have similar clinical profiles, neural substrates, and respond similarly to dopaminergic medication and deep brain stimulation (DBS). Here, we provide an extensive review of the clinical characteristics and neural substrates of each of these motor symptoms, to highlight precisely how PD and its medical and surgical treatments impact motor symptoms. In conclusion, we offer a unified framework for understanding the range of motor symptoms in PD. We argue that various motor symptoms in PD reflect dysfunction of neural structures responsible for action selection, motor sequencing, and coordination and execution of movement

Optimized Targeting in Deep Brain Stimulation for Movement Disorders.

Deep brain stimulation (DBS) is the dominant surgical therapy for medically-refractory Parkinson’s Disease (PD) and Essential Tremor (ET). Despite its success in treating the physical symptoms of many movement disorders, optimal targeting protocols are unknown. The success of the surgery is highly dependent upon proper placement of the electrode in the brain. However, the anatomical targets for PD and ET DBS—the subthalamic nucleus (STN) and ventral intermediate (Vim) nucleus of the thalamus, respectively—are not distinguishable on conventional magnetic resonance imaging. Neurosurgeons typically locate these structures using imprecise atlas-based indirect targeting methods requiring several attempts, increasing the risk of intracranial hemorrhage. The purpose of this work was to optimize targeting in DBS for PD and ET. First, we evaluated the most common indirect STN targeting methods with our validated 3-Tesla MRI protocol optimized for STN visualization. We calculated indirect targets as prescribed by midcommissural point (MCP) -based and red nucleus-based (RN) methods, and compared those coordinates to the position of the STN. We found that RN-based targeting is statistically superior to MCP-based targeting and should be routinely used in the absence of direct STN visualization. In our next study, we investigated the volume of tissue activated (VTA) in thalamic DBS. First, we developed a k-means clustering algorithm that operates on diffusion tensor imaging data to segment the thalamus into its functionally-distinct nuclei. We segmented individual patient thalami and an atlas thalamus in an existing VTA model, and created an individualized VTA model by utilizing each patient’s own anatomy and tissue conductivity. We measured stimulation overlaps with relevant nuclei for clinically efficacious stimulation settings. Our preliminary results indicated that individualized VTA modeling may provide more precise modeling results than existing atlas-based VTA modeling. Next, we investigated the ability of atlas-based and individualized VTA modeling methods to explain common side effects from thalamic DBS. We found that individualized VTA modeling is superior to atlas-based modeling in the prediction of side effects. The results of this work advance the understanding of proper DBS targeting for movement disorders, and our VTA modeling system represents the most individualized approach for ET DBS surgical planning.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111402/1/hlayla_1.pd

Simulating Idiopathic Parkinson\u27s Disease by In Vitro and Computational Models

In general there is a wide gap between experimental animal results, especially with respect to neuroanatomical data, and computational modeling. In order to be able to investigate the anatomical and functional properties of afferent and efferent connections between the different nuclei of the basal ganglia, similar studies need to be performed as described in this review for the Substantia Nigra. These studies, though very time-consuming, are essential to decide which pathways play important roles in normal functioning and therefore need to be included in modeling studies. In addition, it should be known what neuroanatomical changes take place resulting from the neurodegeneration associated with Parkinson’s disease and how they affect network behavior. For instance, the direct effects of DBS on motor control are of interest, but since DBS has a low threshold to side effects, additional non-motor pathways are expected to be involved. Including these pathways in network models may shed light on the extent and effect of stimulation. Similarly, as PPN stimulation may have a beneficial influence on gait and balance, different pathways are important regarding the different motor symptoms of Parkinson’s disease

Non-Human Primate Models in Neuroscience Research

Neuroscience is progressively increasing its comprehension of the normal functioning of the central and  peripheral nervous system. Such understanding is essential to challenge important neurodegenerative disorders  and clinical conditions such as Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, etc. The  aim of neuroscience research is to improve understanding of normal and pathological functions and to  develop therapeutic strategies and tools. Fundamental neuroscience utilizes a variety of techniques which  include: electrophysiology, imaging, and computational modelling and entails interactions with clinical  studies. Non-human primates are the closest species to humans in terms of biological, physiological, immunological  and neurological characteristics; their closeness has been, and is still, an important reason for  using them in biomedical studies. These animals have a vertebrate brain that is most like that of humans  in terms of neural circuitry and this, together with similarities with human physiological and behavioural  characteristics, makes them more valuable and accurate models of neurological and psychiatric diseases  than other animals. This article provides an overview of the contribution of non-human primate models in  fundamental neuroscience research and in generating clinically relevant findings and therapeutic developments.