Validation of the STN-DBS intervention as a treatment for Parkinson's disease by studying the accuracy of electrode placement and possible correlation with motor symptoms

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Director: Jordi Rumià Arboix. Tutor: Agustí Gutiérrez Gálvez.Parkinson’s disease is present approximately in 10 million individuals all over the world, being one of the most common neurodegenerative diseases, and statistics denote that its prevalence will raise in the coming years. Deep brain stimulation is an effective surgical treatment for patients who do not improve with drug treatment and who present many motor symptoms. Deep brain stimulation constitutes the implantation of electrodes in specific brain structures, nevertheless, this study has focused on the subthalamic nucleus, being the main target region for Parkinson’s disease. In order to have satisfactory post-surgical results, where the patient has a considerable reduction in motor symptoms, it is essential to present a correct lead placement accuracy, which corresponds with what has been planned before surgery by terms of using the neuronavigator. The principal objective of this study is to validate the accuracy of the actual technique used in the Hospital Clínic of Barcelona, which is guided exclusively by image, as well as to establish a possible relationship between the patient's clinic, following the UPDRS scale type III, once it has undergone surgery and the accuracy of the electrodes, to verify that it essential to achieve its maximum effectiveness. Thus, objective arguments of the image-guided and image-verified technique can also be given as well as providing assertion of completing the procedure with the patient completely anaesthetised, since currently in Catalonia most centres do so with the patient awake and with microelectrode recording..

Robotically Steered Needles: A Survey of Neurosurgical Applications and Technical Innovations

This paper surveys both the clinical applications and main technical innovations related to steered needles, with an emphasis on neurosurgery. Technical innovations generally center on curvilinear robots that can adopt a complex path that circumvents critical structures and eloquent brain tissue. These advances include several needle-steering approaches, which consist of tip-based, lengthwise, base motion-driven, and tissue-centered steering strategies. This paper also describes foundational mathematical models for steering, where potential fields, nonholonomic bicycle-like models, spring models, and stochastic approaches are cited. In addition, practical path planning systems are also addressed, where we cite uncertainty modeling in path planning, intraoperative soft tissue shift estimation through imaging scans acquired during the procedure, and simulation-based prediction. Neurosurgical scenarios tend to emphasize straight needles so far, and span deep-brain stimulation (DBS), stereoelectroencephalography (SEEG), intracerebral drug delivery (IDD), stereotactic brain biopsy (SBB), stereotactic needle aspiration for hematoma, cysts and abscesses, and brachytherapy as well as thermal ablation of brain tumors and seizure-generating regions. We emphasize therapeutic considerations and complications that have been documented in conjunction with these applications

Tecniche Elettrotomografiche per la caratterizzazione dei tessuti biologici

Electrical impedance tomography (EIT) is an imaging modality wherein the spatial map of conductivity and permittivity inside a medium is obtained from a set of surface electrical measurements. Electrodes are brought into contact with the surface of the object being imaged and a set of currents are applied and the corresponding voltages are measured. These voltages and currents are then used to estimate the electrical properties of the object using an image reconstruction algorithm which relies on an accurate model of the electrical interaction. The process of property estimation, called inverse problem, is highly ill-posed and it requires a Regularization method. The objective of this Thesis was to develop a device for imaging using the EIT technique, which was convenient, noninvasive, easily programmable, portable and relatively cheap in contrast to many other diagnostic tool. In this direction a simple EIT system and its hardware and software parts are developed. The data processing was accomplished by utilizing the EIDORS toolkit, which was developed for application to this nonlinear and ill-posed inverse problem. Experiments have indicated that the EIT system can reconstruct resistive and capacitive images of good contrast despite errors in the measurement are not taken in account

Single Neuron Correlates of Learning, Value, and Decision in the Human Brain

In this thesis, I present several new results on how the human brain performs value-based learning and decision-making, leveraging rare single neuron recordings from epilepsy patients in vmPFC, preSMA, dACC, amygdala, and hippocampus, as well as reinforcement learning models of behavior. With a probabilistic gambling task we determined that human preSMA neurons integrate computational components of stimulus value such as expected values, uncertainty, and novelty, to encode an utility value and, subsequently, decisions themselves. Additionally, we found that post-decision related encoding of variables for the chosen option was more widely distributed and especially prominent in vmPFC. Additionally, with a Pavlovian conditioning task we found evidence of stimulus-stimulus associations in vmPFC, while both vmPFC and amygdala performed predictive value coding, establishing direct evidence for model-based Pavlovian conditioning in human vmPFC neurons. Finally, in a Pavlovian observational learning paradigm, we found a significant proportion of amygdala neurons whose activity correlated with both expected rewards for oneself and others, and in tracking outcome values received by oneself or other agents, further establishing amygdala as an important center in social cognition. Taken together, our findings expand our understanding of the role of several human cortical brain regions in creating and updating value representations which are leveraged during decision-making.</p

BCIs and mobile robots for neurological rehabilitation: practical applications of remote control. Remote control of mobile robots applied in non-invasive BCI for disabled users afflicted by motor neurons diseases

This project aims at testing the possible advantages of introducing a mobile robot as a physical input/output device in a Brain Computer Interface (BCI) system. In the proposed system, the actions triggered by the subject’s brain activity results in the motions of a physical device in the real world, and not only in a modification of a graphical interface. A goal-based system for destination detecting and the high entertainment level offered by controlling a mobile robot are hence main features for actually increase patients' life quality leve

Improving the forward model for electrical impedance tomography of brain function through rapid generation of subject specific finite element models

Electrical Impedance Tomography (EIT) is a non-invasive imaging method which allows internal electrical impedance of any conductive object to be imaged by means of current injection and surface voltage measurements through an array of externally applied electrodes. The successful generation of the image requires the simulation of the current injection patterns on either an analytical or a numerical model of the domain under examination, known as the forward model, and using the resulting voltage data in the inverse solution from which images of conductivity changes can be constructed. Recent research strongly indicates that geometric and anatomical conformance of the forward model to the subject under investigation significantly affects the quality of the images. This thesis focuses mainly on EIT of brain function and describes a novel approach for the rapid generation of patient or subject specific finite element models for use as the forward model. After introduction of the topic, methods of generating accurate finite element (FE) models using commercially available Computer-Aided Design (CAD) tools are described and show that such methods, though effective and successful, are inappropriate for time critical clinical use. The feasibility of warping or morphing a finite element mesh as a means of reducing the lead time for model generation is then presented and demonstrated. This leads on to the description of methods of acquiring and utilising known system geometry, namely the positions of electrodes and registration landmarks, to construct an accurate surface of the subject, the results of which are successfully validated. The outcome of this procedure is then used to specify boundary conditions to a mesh warping algorithm based on elastic deformation using well-established continuum mechanics procedures. The algorithm is applied to a range of source models to empirically establish optimum values for the parameters defining the problem which can successfully generate meshes of acceptable quality in terms of discretization errors and which more accurately define the geometry of the target subject. Further validation of the algorithm is performed by comparison of boundary voltages and image reconstructions from simulated and laboratory data to demonstrate that benefits in terms of image artefact reduction and localisation of conductivity changes can be gained. The processes described in the thesis are evaluated and discussed and topics of further work and application are described

Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

Computational modelling of brain shift in stereotactic neurosurgery

Navigation in stereotactic neurosurgery relies solely on preoperative images, with the location of anatomical targets defined relative to the skull. Displacement of the anatomical target from its expected position is a common complication during surgery; however, the magnitude of this deviation is currently unpredictable. One potential source of this error occurs with reorientation of the head alone and is known as positional brain shift (PBS). PBS is the focus of this thesis, which aims to better understand the phenomenon through computational methods. A finite element (FE) model was generated in FEBio, incorporating a novel spring element/fluid structure-interaction representation of the pia-arachnoid complex (PAC). The model was loaded to represent gravity in the prone and supine positions. Material parameter identification and sensitivity analysis were performed using statistical software, comparing the FE results to human in-vivo measurements. Results for the brain Ogden parameters

Neuromodulation of Spatial Associations: Evidence from Choice Reaction Tasks During Transcranial Direct Current Stimulation

Various portions of human behavior and cognition are influenced by covert implicit processes without being necessarily available to intentional planning. Implicit cognitive biases can be measured in behavioral tasks yielding SNARC effects for spatial associations of numerical and non-numerical sequences, or yielding the implicit association test effect for associations between insect-flower and negative-positive categories. By using concurrent neuromodulation with transcranial direct current stimulation (tDCS), subthreshold activity patterns in prefrontal cortical regions can be experimentally manipulated to reduce implicit processing. Thus, the application of tDCS can test neurocognitive hypotheses on a unique neurocognitive origin of implicit cognitive biases in different spatial-numerical and non-numerical domains. However, the effects of tDCS are not only determined by superimposed electric fields, but also by task characteristics. To outline the possibilities of task-specific targeting of tDCS, task characteristics and instructions can be varied systematically when combined with neuromodulation. In the present thesis, implicit cognitive processes are assessed in different paradigms concurrent to left-hemispheric prefrontal tDCS to investigate a verbal processing hypothesis for implicit associations in general. In psychological experiments, simple choice reaction tasks measure implicit SNARC and SNARC-like effects as relative left-hand vs. right-hand latency advantages for responding to smaller number or ordinal sequence targets. However, different combinations of polarity-dependent tDCS with stimuli and task procedures also reveal domain-specific involvements and dissociations. Discounting previous unified theories on the SNARC effect, polarity-specific neuromodulation effects dissociate numbers and weekday or month ordinal sequences. By considering also previous results and patient studies, I present a hybrid and augmented working memory account and elaborate the linguistic markedness correspondence principle as one critical verbal mechanism among competing covert coding mechanisms. Finally, a general stimulation rationale based on verbal working memory is tested in separate experiments extending also to non-spatial implicit association test effects. Regarding cognitive tDCS effects, the present studies show polarity asymmetry and task-induced activity dependence of state-dependent neuromodulation. At large, distinct combinations of the identical tDCS electrode configuration with different tasks influences behavioral outcomes tremendously, which will allow for improved task- and domain-specific targeting