Methodological considerations for neuroimaging in deep brain stimulation of the subthalamic nucleus in Parkinson’s disease patients

Deep brain stimulation (DBS) of the subthalamic nucleus is a neurosurgical intervention for Parkinson’s disease patients who no longer appropriately respond to drug treatments. A small fraction of patients will fail to respond to DBS, develop psychiatric and cognitive side-effects, or incur surgery-related complications such as infections and hemorrhagic events. In these cases, DBS may require recalibration, reimplantation, or removal. These negative responses to treatment can partly be attributed to suboptimal pre-operative planning procedures via direct targeting through low-field and low-resolution magnetic resonance imaging (MRI). One solution for increasing the success and efficacy of DBS is to optimize preoperative planning procedures via sophisticated neuroimaging techniques such as high-resolution MRI and higher field strengths to improve visualization of DBS targets and vasculature. We discuss targeting approaches, MRI acquisition, parameters, and post-acquisition analyses. Additionally, we highlight a number of approaches including the use of ultra-high field (UHF) MRI to overcome limitations of standard settings. There is a trade-off between spatial resolution, motion artifacts, and acquisition time, which could potentially be dissolved through the use of UHF-MRI. Image registration, correction, and post-processing techniques may require combined expertise of traditional radiologists, clinicians, and fundamental researchers. The optimization of pre-operative planning with MRI can therefore be best achieved through direct collaboration between researchers and clinicians

The impact of volume of tissue activation on cortical-striatal networks and verbal fluency declines in post-deep brain stimulation Parkinson\u27s disease patients.

This study investigated the cortical-striatal networks of verbal fluency declines in 6-month, post-operative, deep brain stimulation Parkinson’s Disease patients. Nine Parkinson’s disease participants with implanted STN or GPi DBS systems were recruited for this study. Verbal fluency data was obtained from each patient preoperatively and 6- months post implantation. The stimulation-based volume of tissue activated area (VTA) of each target site (STN or GPi) was analyzed using Lead-DBS and Lead-Group. The white matter tracks intersecting each patient’s VTA, terminating in the pre-SMA, SMA, caudate nucleus, and anterior cingulate were investigated and correlated with verbal fluency declines. We found statistically significant effects of DBS on verbal fluency, with a trendtowards greater declines in the STN compared to the GPi. Verbal fluency declines were found to be the greatest in patients with more white matter tracts leading from the left hemisphere to the left caudate and bilaterally to the pre-SMA and SMA, and there were no correlations found between VF and the anterior cingulate

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

Personalized computational models of deep brain stimulation

University of Minnesota Ph.D. dissertation. December 2016. Major: Biomedical Engineering. Advisor: Matthew Johnson. 1 computer file (PDF); xii, 138 pages.Deep brain stimulation (DBS) therapy is used for managing symptoms associated with a growing number of neurological disorders. One of the primary challenges with delivering this therapy, however, continues to be accurate neurosurgical targeting of the DBS lead electrodes and post-operative programming of the stimulation settings. Two approaches for addressing targeting have been advanced in recent years. These include novel DBS lead designs with more electrodes and computational models that can predict cellular modulation during DBS. Here, we developed a personalized computational modeling framework to (1) thoroughly investigate the electrode design parameter space for current and future DBS array designs, (2) generate and evaluate machine learning feature sets for semi-automated programming of DBS arrays, (3) study the influence of model parameters in predicting behavioral and electrophysiological outcomes of DBS in a preclinical animal model of Parkinson’s disease, and (4) evaluate feasibility of a novel endovascular targeting approach to delivering DBS therapy in humans. These studies show how independent current controlled stimulation with advanced machine learning algorithms can negate the need for highly dense electrode arrays to shift, steer, and sculpt regions of modulation within the brain. Additionally, these studies show that while advanced and personalized computational models of DBS can predict many of the behavioral and electrophysiological outcomes of DBS, there are remaining inconsistencies that suggest there are additional physiological mechanisms of DBS that are not yet well understood. Finally, the results show how computational models can be beneficial for prospective development of novel approaches to neuromodulation prior to large-scale preclinical and clinical studies

Imaging the subthalamic nucleus in Parkinson’s disease

This thesis is comprised of a set of work that aims to visualize and quantify the anatomy, structural variability, and connectivity of the subthalamic nucleus (STN) with optimized neuroimaging methods. The study populations include both healthy cohorts and individuals living with Parkinson's disease (PD). PD was chosen specifically due to the involvement of the STN in the pathophysiology of the disease. Optimized neuroimaging methods were primarily obtained using ultra-high field (UHF) magnetic resonance imaging (MRI). An additional component of this thesis was to determine to what extent UHF-MRI can be used in a clinical setting, specifically for pre-operative planning of deep brain stimulation (DBS) of the STN for patients with advanced PD. The thesis collectively demonstrates that i, MRI research, and clinical applications must account for the different anatomical and structural changes that occur in the STN with both age and PD. ii, Anatomical connections involved in preparatory motor control, response inhibition, and decision-making may be compromised in PD. iii. The accuracy of visualizing and quantifying the STN strongly depends on the type of MR contrast and voxel size. iv, MRI at a field strength of 3 Tesla (T) can under certain circumstances be optimized to produce results similar to that of 7 T at the expense of increased acquisition time

Hitting the right target : noninvasive localization of the subthalamic nucleus motor part for specific deep brain stimulation

Deep brain stimulation of the subthalamic nucleus (STN) has gained momentum as a therapy for advanced Parkinson’s disease. The stimulation effectively alleviates the patients’ typical motor symptoms on a long term, but can give rise to cognitive and psychiatric adverse effects as well. Based on primate studies, the STN has been divided into three functionally different parts, which were distinguished by their afferent and efferent connections. The largest part is the motor area, followed by an associative and a limbic area. The serious adverse effects on cognition and behavior occurring after deep brain stimulation are assumed to be caused by electrical current spread to the associative and limbic areas of the STN. Therefore, selective stimulation of the motor part of the STN seems crucial, both to obtain the best possible therapeutic effect on the motor symptoms and to minimize the debilitating effects on cognition and behavior. However, current medical imaging techniques do not yet facilitate the required accurate identification of the STN itself, let alone its different functional areas. The final target for DBS is still often adjusted using intraoperative electrophysiology. Therefore, in this thesis we aimed to improve imaging for deep brain stimulation using noninvasive MRI protocols, in order to identify the STN and its motor part. We studied the advantages and drawbacks of already available noninvasive methods to target the STN. This review did not lead to a straightforward conclusion; identification of the STN motor part remained an open question. In follow-up on this question, we investigated the possibility to distinguish the different functional STN parts based on their connectivity information. Three types of information were carefully analyzed in this thesis. First, we looked into the clustering of local diffusion information within the STN region. We visually inspected the complex diffusion profiles, derived from postmortem rat brain data with high angular resolution, and augmented this manual segmentation method using k-means and graph cuts clustering. Because the weighing of different orders of diffusion information in the traditionally used L2 norm on the orientation distribution functions (ODFs) remained an open issue, we developed a specialized distance measure, the so-called Sobolev norm. This norm does not only take into account the amplitudes of the diffusion profiles, but also their extrema. We showed it to perform better than the L2 norm on synthetic phantom data and real brain (thalamus) data. The research done on this topic facilitates better classification by clustering of gray matter structures in the (deep) brain. Secondly, we were the first to analyze the STN’s full structural connectivity, based on probabilistic fiber tracking in diffusion MRI data of healthy volunteers. The results correspond well to topical literature on STN projections. Furthermore, we assessed the structural connectivity per voxel of the STN seed region and discovered a gradient in connectivity to the premotor cortex within the STN. While going from the medial to the lateral part of the STN, the connectivity increases, confirming the expected lateral location of the STN motor part. Finally, the connectivity analysis produced evidence for the existence of a "hyperdirect" pathway between the motor cortex and the STN in humans, which is very useful for future research into stimulation targets. The results of these experiments indicate that it is possible to find the motor part of the STN as specific target for deep brain stimulation using structural connectivity information acquired in a noninvasive way. Third and last, we studied functional connectivity using resting state functional MRI data of healthy volunteers. The resulting significant clusters provided us with the first complete description of the STN’s resting state functional connectivity, which corresponds with the expectations based on available literature. Moreover, we performed a reverse regression procedure with the average time series signals in motor and limbic areas as principal regressors. The results were analyzed for each STN voxel separately and also showed mediolateral gradients in functional connectivity within the STN. The lateral STN part exhibited more motor connectivity, while the medial part seemed to be more functionally connected to limbic brain areas, as described in neuronal tracer studies. These results show that functional connectivity analysis also is a viable noninvasive method to find the motor part of the STN. The work on noninvasive MRI methods for identification of the STN and its functional parts, as presented in this thesis, thus contributes to future specific stimulation of the motor part of the STN for deep brain stimulation in patients with Parkinson’s disease. This may help to maximize the motor effects and minimize severe cognitive and psychiatric side effects