Defining the border of the subthalamic nucleus for deep brain stimulation: A proposed model using the symmetrical sigmoid curve function

Background: The subthalamic nucleus (STN) is an important target during deep brain stimulation (DBS). Accurate lead placement is integral to achieving satisfactory clinical outcomes; however, the STN remains a structure whose visualization is highly variable with borders often difficult to define. We aimed to develop an objective method of evaluating the visibility of the STN on preoperative magnetic resonance imaging (MRI) to standardize future comparative assessments between imaging protocols and patient-specific parameters. Methods: An imaging study of 64 prospectively collected patients undergoing bilateral DBS of the STN for various movement disorders was performed with institutional approval. MRI scans were acquired using a uniform protocol involving general anesthesia, cranial fixation in a Leksell stereotactic frame, and long acquisition times using a 3T MRI scanner. The images were analyzed using the iPlan Stereotaxy, version 2.6, workstation. High-resolution T2-weighted axial sections were evaluated, and the voxel values in the region of the presumed posterior border of the STN (as defined by the operating neurosurgeon) were obtained. A 4-parameter logistic symmetrical sigmoid curve was used to map the voxel values as they progressed from within to outside the region of the STN border. The inflection point and Hill coefficient of this symmetrical curve was calculated to provide objective information on the location and clarity of the STN border, respectively. These findings were compared with the surgeon\u27s judgment of the STN border. To demonstrate the use of the sigmoid curve, the patients\u27 head volumes were also calculated and evaluated to assess whether larger head volumes adversely affected STN visibility. Results: The symmetrical sigmoid curve model provided objective information on the visibility of the STN on T2-weighted MRI scans and could be generated in 86% of the patients. The other 14% of patients had MRI scans that generated linear graphs, indicating the poorest scoring for STN image quality. No correlation between head volume and STN visibility was identified. Conclusions: Our proposed statistical model allows for standardized examination of the visibility of the STN border for DBS and has potential for both clinical and academic applications

Neuroimaging and electrophysiology meet invasive neurostimulation for causal interrogations and modulations of brain states

Deep brain stimulation (DBS) has developed over the last twenty years into a highly effective evidenced-based treatment option for neuropsychiatric disorders. Moreover, it has become a fascinating tool to provide illustrative insights into the functioning of brain networks. New anatomical and pathophysiological models of DBS action have accelerated our understanding of neurological and psychiatric disorders and brain functioning. The description of the brain networks arose through the unique ability to illustrate long-range interactions between interconnected brain regions as derived from state-of-the-art neuroimaging (structural, diffusion, and functional MRI) and the opportunity to record local and large-scale brain activity at millisecond temporal resolution (microelectrode recordings, local field potential, electroencephalography, and magnetoencephalography). In the first part of this review, we describe how neuroimaging techniques have led to current understanding of DBS effects, by identifying and refining the DBS targets and illustrate the actual view on the relationships between electrode locations and clinical effects. One step further, we discuss how neuroimaging has shifted the view of localized DBS effects to a modulation of specific brain circuits, which has been possible from the combination of electrode location reconstructions with recently introduced network imaging methods. We highlight how these findings relate to clinical effects, thus postulating neuroimaging as a key factor to understand the mechanisms of DBS action on behavior and clinical effects. In the second part, we show how invasive electrophysiology techniques have been efficiently integrated into the DBS set-up to precisely localize the neuroanatomical targets of DBS based on distinct region-specific patterns of neural activity. Next, we show how multi-site electrophysiological recordings have granted a real-time window into the aberrant brain circuits within and beyond DBS targets to quantify and map the dynamic properties of rhythmic oscillations. We also discuss how DBS alters the transient synchrony states of oscillatory networks in temporal and spatial domains during resting, task-based and motion conditions, and how this modulation of brain states ultimately shapes the functional response. Finally, we show how a successful decoding and management of electrophysiological proxies (beta bursts, phase-amplitude coupling) of aberrant brain circuits was translated into adaptive DBS stimulation paradigms for a targeted and state-dependent invasive electrical neuromodulation

Reinforcement magnitudes modulate subthalamic beta band activity in patients with Parkinson's disease

We set out to investigate whether beta oscillations in the human basal ganglia are modulated during reinforcement learning. Based on previous research, we assumed that beta activity might either reflect the magnitudes of individuals' received reinforcements (reinforcement hypothesis), their reinforcement prediction errors (dopamine hypothesis) or their tendencies to repeat versus adapt responses based upon reinforcements (status-quo hypothesis). We tested these hypotheses by recording local field potentials (LFPs) from the subthalamic nuclei of 19 Parkinson's disease patients engaged in a reinforcement-learning paradigm. We then correlated patients' reinforcement magnitudes, reinforcement prediction errors and response repetition tendencies with task-related power changes in their LFP oscillations. During feedback presentation, activity in the frequency range of 14 to 27 Hz (beta spectrum) correlated positively with reinforcement magnitudes. During responding, alpha and low beta activity (6 to 18 Hz) was negatively correlated with previous reinforcement magnitudes. Reinforcement prediction errors and response repetition tendencies did not correlate significantly with LFP oscillations. These results suggest that alpha and beta oscillations during reinforcement learning reflect patients' observed reinforcement magnitudes, rather than their reinforcement prediction errors or their tendencies to repeat versus adapt their responses, arguing both against an involvement of phasic dopamine and against applicability of the status-quo theory

High frequency deep brain stimulation attenuates subthalamic and cortical rhythms in Parkinson's disease

Parkinson's disease (PD) is marked by excessive synchronous activity in the beta (8–35 Hz) band throughout the cortico-basal ganglia network. The optimal location of high frequency deep brain stimulation (HF DBS) within the subthalamic nucleus (STN) region and the location of maximal beta hypersynchrony are currently matters of debate. Additionally, the effect of STN HF DBS on neural synchrony in functionally connected regions of motor cortex is unknown and is of great interest. Scalp EEG studies demonstrated that stimulation of the STN can activate motor cortex antidromically, but the spatial specificity of this effect has not been examined. The present study examined the effect of STN HF DBS on neural synchrony within the cortico-basal ganglia network in patients with PD. We measured local field potentials dorsal to and within the STN of PD patients, and additionally in the motor cortex in a subset of these patients. We used diffusion tensor imaging (DTI) to guide the placement of subdural cortical surface electrodes over the DTI-identified origin of the hyperdirect pathway (HDP) between motor cortex and the STN. The results demonstrated that local beta power was attenuated during HF DBS both dorsal to and within the STN. The degree of attenuation was monotonic with increased DBS voltages in both locations, but this voltage-dependent effect was greater in the central STN than dorsal to the STN (p < 0.05). Cortical signals over the estimated origin of the HDP also demonstrated attenuation of beta hypersynchrony during DBS dorsal to or within STN, whereas signals from non-specific regions of motor cortex were not attenuated. The spatially-specific suppression of beta synchrony in the motor cortex support the hypothesis that DBS may treat Parkinsonism by reducing excessive synchrony in the functionally connected sensorimotor network

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

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