Presented By: Biomedical Engineering
Ph.D. Defense: Karlo A. Malaga
Finite Element Electrode and Individual Patient Modeling to Optimize Restorative Neuroengineering
Department of Biomedical Engineering Final Oral Examination
Karlo A. Malaga
Finite Element Electrode and Individual Patient Modeling to Optimize Restorative Neuroengineering
Parkinson disease (PD) and essential tremor (ET) are the most common neurological movement disorders among adults. Deep brain stimulation (DBS) is an established surgical treatment for both conditions that involves implanting electrodes in the brain and then applying electrical stimulation. Despite the clinical effectiveness of DBS, its underlying mechanisms remain unclear. As DBS advances into a viable treatment for other conditions, it has become important to address the fundamental principles behind the procedure, specifically the spatial extent of stimulation. Furthermore, as DBS moves toward the adoption of closed-loop stimulation paradigms, an increased understanding of how neural recordings are affected by different biological factors is also key. Broadly, the work presented in this dissertation utilizes finite element electrode and individual patient modeling in an effort to help improve established procedures within neural stimulation and recording for restorative neuroengineering applications.
The therapeutic benefit of DBS is strongly dependent on the spatial distribution of the stimulation-induced electric field relative to the individual neuroanatomy of the patient undergoing treatment. To maximize symptom suppression while minimizing side effects, accurate predictions of the spread of stimulation in the brain are essential. Due to the inherent difficulty in measuring the electric field in vivo, computational models have been used to visualize and quantify the spatial extent of neural activation, termed the volume of tissue activated (VTA). The VTA is a stimulation parameter-dependent metric that can be used to predict clinical outcomes and optimize stimulation parameters. The clinical utility of these models hinges on their ability to make meaningful and accurate predictions. Significant efforts have gone towards validating VTA predictions with experimental and clinical data. Computational models have also been developed to increase understanding of neural recordings and how they are affected by different factors. These models employ many of the same tools used in VTA modeling, such as finite element analysis.
Tissue activation modeling continues to grow more complex. Models can now incorporate detailed neuroanatomy, heterogeneous and anisotropic tissue properties, explicit representation of the DBS lead and electrode-tissue interface, and clinically determined stimulation parameters. Each of these modeling advancements have been made in an effort to tailor DBS models to individual patients. However, there is still room for improvement when it comes to creating fully individualized models. For example, deep brain structures are typically derived from a brain atlas, translated, rotated, and scaled to best fit the anatomy of the patient. Anisotropic tissue properties, derived from diffusion tensor (DT) imaging, are also typically atlas-based. Since most atlases are based on a single subject, there is a limitation in how representative one can be to a patient population, especially one that is in a diseased state. To accurately characterize the VTA on an individual basis, model components should be derived from a single source (the patient).
The objective of this dissertation is two-fold: (1) to characterize the spatial extent of stimulation associated with therapeutic outcome and side effects in DBS for PD and ET by developing atlas-independent, fully individualized DT-based VTA models; (2) to investigate the effects of gliosis and the electrode-tissue interface on single-unit recording quality by developing a data-driven neural recording model. The significance of the work presented here is in the individualized modeling framework that it provides. As insight regarding stimulation spread in the brain increases, the techniques described here can be applied to other conditions to inform novel stimulation strategies and help bridge the gap between model-based evidence and clinical practice.
Date: Thursday, July 18, 2019
Time: 9:00 AM
Location: General Motors Conference Room, Lurie Engineering Center
Chair: Parag Patil
Karlo A. Malaga
Finite Element Electrode and Individual Patient Modeling to Optimize Restorative Neuroengineering
Parkinson disease (PD) and essential tremor (ET) are the most common neurological movement disorders among adults. Deep brain stimulation (DBS) is an established surgical treatment for both conditions that involves implanting electrodes in the brain and then applying electrical stimulation. Despite the clinical effectiveness of DBS, its underlying mechanisms remain unclear. As DBS advances into a viable treatment for other conditions, it has become important to address the fundamental principles behind the procedure, specifically the spatial extent of stimulation. Furthermore, as DBS moves toward the adoption of closed-loop stimulation paradigms, an increased understanding of how neural recordings are affected by different biological factors is also key. Broadly, the work presented in this dissertation utilizes finite element electrode and individual patient modeling in an effort to help improve established procedures within neural stimulation and recording for restorative neuroengineering applications.
The therapeutic benefit of DBS is strongly dependent on the spatial distribution of the stimulation-induced electric field relative to the individual neuroanatomy of the patient undergoing treatment. To maximize symptom suppression while minimizing side effects, accurate predictions of the spread of stimulation in the brain are essential. Due to the inherent difficulty in measuring the electric field in vivo, computational models have been used to visualize and quantify the spatial extent of neural activation, termed the volume of tissue activated (VTA). The VTA is a stimulation parameter-dependent metric that can be used to predict clinical outcomes and optimize stimulation parameters. The clinical utility of these models hinges on their ability to make meaningful and accurate predictions. Significant efforts have gone towards validating VTA predictions with experimental and clinical data. Computational models have also been developed to increase understanding of neural recordings and how they are affected by different factors. These models employ many of the same tools used in VTA modeling, such as finite element analysis.
Tissue activation modeling continues to grow more complex. Models can now incorporate detailed neuroanatomy, heterogeneous and anisotropic tissue properties, explicit representation of the DBS lead and electrode-tissue interface, and clinically determined stimulation parameters. Each of these modeling advancements have been made in an effort to tailor DBS models to individual patients. However, there is still room for improvement when it comes to creating fully individualized models. For example, deep brain structures are typically derived from a brain atlas, translated, rotated, and scaled to best fit the anatomy of the patient. Anisotropic tissue properties, derived from diffusion tensor (DT) imaging, are also typically atlas-based. Since most atlases are based on a single subject, there is a limitation in how representative one can be to a patient population, especially one that is in a diseased state. To accurately characterize the VTA on an individual basis, model components should be derived from a single source (the patient).
The objective of this dissertation is two-fold: (1) to characterize the spatial extent of stimulation associated with therapeutic outcome and side effects in DBS for PD and ET by developing atlas-independent, fully individualized DT-based VTA models; (2) to investigate the effects of gliosis and the electrode-tissue interface on single-unit recording quality by developing a data-driven neural recording model. The significance of the work presented here is in the individualized modeling framework that it provides. As insight regarding stimulation spread in the brain increases, the techniques described here can be applied to other conditions to inform novel stimulation strategies and help bridge the gap between model-based evidence and clinical practice.
Date: Thursday, July 18, 2019
Time: 9:00 AM
Location: General Motors Conference Room, Lurie Engineering Center
Chair: Parag Patil
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