Integrated Neuromusculoskeletal Modeling within a Finite Element Framework to Investigate Mechanisms and Treatment of Neurodegenerative Conditions

Neurodegenerative and neurodevelopmental disorders are a group of conditions that stem from irregularities in the nervous system that lead to complications in function and movement. The goal of this work is to develop computational tools that: (1) measure the accuracy of surgical interventions in neurodegenerative and neurodevelopmental conditions, and (2) integrate neural and musculoskeletal frameworks to provide a platform to better investigate neurodegenerative and neurodevelopmental disorders. Parkinson’s disease (PD) is a neurodegenerative condition projected to affect over 1.2 million people by 2030 in the US. It is caused by atypical firing patterns in the basal ganglia region of the brain that leads to primary motor symptoms of tremor, slowness of movement, and rigidity. A potential treatment for PD is deep brain stimulation (DBS). DBS involves implanting electrodes into central brain structures to regulate the pathological signaling. Electrode placement accuracy is a key metric that helps to determine patient outcomes postoperatively. An automated measurement system was developed to quantify electrode placement accuracy in robot-assisted asleep DBS procedures (Chapter 2). This measurement system allows for precise metrics without human bias in large cohorts of patients. This measurement system was later modified to measure screw placement accuracy in spinal fusion procedures for the treatment of degenerative musculoskeletal conditions (Chapter 3). DBS is an effective treatment for PD, but it is not a cure for the cause of the disease itself. To cure neurodegenerative and neurodevelopmental diseases, the underlying disease mechanisms must be better understood. A major limitation in studying neural conditions is the infeasibility of performing in vivo experiments, particularly in humans due to ethical considerations. Computational modeling, specifically fully predictive neuromusculoskeletal (NMS) models, can help to accumulate additional knowledge about neural pathways that cannot be determined experimentally. NMS models typically include complexity in either the neuromuscular or musculoskeletal system, but not both, making it difficult or infeasible to investigate the relationship between neural signaling and musculoskeletal function. To overcome this, a fully predictive NMS model was developed by integrating NEURON software within Abaqus, a finite element (FE) environment (Chapter 4). The neural model consisted of a pool of motor neurons innervating the soleus muscle in a FE human ankle model. Software integration was verified against previously published data, and the neuronal network was verified for motor unit recruitment and rate coding, which are the two principles required for in vivo muscle generation. To demonstrate the applicability of the model to study neurodegenerative and neurodevelopmental diseases, a fully predictive mouse hindlimb NMS model was developed using the integrated framework to investigate Rett syndrome (RS) (Chapter 5). RS is a neurodevelopmental disorder caused by a mutation of the Mecp2 gene with hallmark motor symptoms of a loss of purposeful hand movement, changes in muscle tone, and a loss of speech. Recent experimental analysis has found that the axon initial segment (AIS) in mice that model RS has torsional morphology compared to wildtype littermate controls. The effects these neural morphological changes have on joint motion will be studied using the mouse NMS model. This work encompasses a range of research that uses computational models to study the underlying mechanisms and design targeted treatment options for neurodegenerative and neurodevelopmental disorders. The outcomes of this work have quantified the accuracy at which surgical interventions for these conditions can be performed and have resulted in a neuromusculoskeletal model that can be applied to understand how neural morphology, and associated changes due to these disorders, affects musculoskeletal function

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

Oscillatory activity in the basal ganglia - is it relevant to movement disorders therapy?

Chronic high frequency stimulation of the basal ganglia can be a highly effective intervention for movement disorders in patients. In the past decade, therapeutic benefits have been seen with stimulation of the subthalamic nucleus and globus pallidus interna for Parkinson's disease (PD) and dystonia, respectively. These procedures have allowed direct recording of basal ganglia activity and have suggested that abnormal synchronisation of neurons in these nuclei may contribute to motor impairment. This thesis explores the possible correlation between synchronised activity in the basal ganglia, as evidenced by oscillations in local field potentials, and movement disorders. In Chapter 3, we demonstrate the correlation between synchronization at frequencies under 10 Hz in the globus pallidus interna and dystonic EMG. This low frequency activity is shown to be locked to neuronal activity within GPi in patients with dystonia (Chapter 4). Deep brain stimulation is thought to suppress spontaneous pathological activity in the basal ganglia. Equally, however, it must also suppress any residual physiological activity in these nuclei. In Chapter 5, we demonstrate that the basal ganglia are involved in the processing of simple limb movements in the human, by separating the effects of deep brain stimulation on pathological and physiological activities based on baseline task performance. An impairment of motor performance was seen during high frequency stimulation in those patients with the best task performance at baseline. This deleterious effect, however, should be distinguished from the effect of direct stimulation at 20 Hz in Parkinson's disease. Oscillatory activity at around 20 Hz is thought to be a core feature in Parkinson's disease. In Chapter 6, we demonstrate that the excessive synchronization imposed by stimulation of the subthalamic nucleus at 20 Hz slows movement, in those patients with the best task performance at baseline. This supports the notion that synchronization around 20 Hz may be causally linked to bradykinesia. Last, the therapeutic effectiveness of DBS therapy for patients with PD partially relies on the accurate localisation of the motor region of the subthalamic nucleus. In Chapter 7, we propose an alternative method for the localization of this region using the spontaneous pathological 20 Hz activity to be found in this nucleus. The findings of these studies provide evidence that basal ganglia oscillatory activities of differing frequencies contribute to movement disorders

A Closed-Loop Deep Brain Stimulation Device With a Logarithmic Pipeline ADC.

This dissertation is a summary of the research on integrated closed-loop deep brain stimulation for treatment of Parkinson’s disease. Parkinson's disease is a progressive disorder of the central nervous system affecting more than three million people in the United States. Deep Brain Stimulation (DBS) is one of the most effective treatments of Parkinson’s symptoms. DBS excites the Subthalamic Nucleus (STN) with a high frequency electrical signal. The proposed device is a single-chip closed-loop DBS (CDBS) system. Closed-loop feedback of sensed neural activity promises better control and optimization of stimulation parameters than with open-loop devices. Thanks to a novel architecture, the prototype system incorporates more functionality yet consumes less power and area compared to other systems. Eight front-end low-noise neural amplifiers (LNAs) are multiplexed to a single high-dynamic-range logarithmic, pipeline analog-to-digital converter (ADC). To save area and power consumption, a high dynamic-range log ADC is used, making analog automatic gain control unnecessary. The redundant 1.5b architecture relaxes the requirements for the comparator accuracy and comparator reference voltage accuracy. Instead of an analog filter, an on-chip digital filter separates the low frequency neural field potential signal from the neural spike energy. An on-chip controller generates stimulation patterns to control the 64 on-chip current-steering DACs. The 64 DACs are formed as a cascade of a single shared 2-bit coarse current DAC and 64 individual bi-directional 4-bit fine DACs. The coarse/fine configuration saves die area since the MSB devices tend to be large. Real-time neural activity was recorded with the prototype device connected to microprobes that were chronically implanted in two Long Evans rats. The recorded in-vivo signal clearly shows neural spikes of 10.2 dB signal-to-noise ratio (SNR) as well as a periodic artifact from neural stimulation. The recorded neural information has been analyzed with single unit sorting and principal component analysis (PCA). The PCA scattering plots from multi-layers of cortex represent diverse information from either single or multiple neural sources. The single-unit neural sorting analysis along with PCA verifies the feasibility of the implantable CDBS device for to in-vivo neural recording interface applications.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60733/1/milaca_1.pd

PERIORAL BIOMECHANICS, KINEMATICS, AND ELECTROPHYSIOLOGY IN PARKINSON'S DISEASE

This investigation quantitatively characterized the orofacial biomechanics, labial kinematics, and associated electromyography (EMG) patterns in individuals with Parkinson's disease (PD) as a function of anti-PD medication state. Passive perioral stiffness, a clinical correlate of rigidity, was sampled using a face-referenced OroSTIFF system in 10 mildly diagnosed PD and 10 age/sex-matched control elderly. Labial movement amplitudes and velocities were evaluated using a 4-dimensional computerized motion capture system. Associated perioral EMG patterns were sampled to examine the characteristics of perioral muscles and compensatory muscular activation patterns during repetitive syllable productions. This study identified several trends that reflect various characteristics of perioral system differences between PD and control subjects: 1. The presence of high tonic EMG patterns after administration of dopaminergic treatment indicated an up-regulation of the central mechanism, which may serve to regulate orofacial postural control. 2. Multilevel regression modeling showed greater perioral stiffness in PD subjects, confirming the clinical correlate of rigidity in these patients. 3. Similar to the clinical symptoms in the upper and lower limb, a reduction of range of motion (hypokinesia) and velocity (bradykinesia) was evident in the PD orofacial system. Administration of dopaminergic treatment improved hypokinesia and bradykinesia. 4. A significant correlation was found between perioral stiffness and the range of labial movement, indicating these two symptoms may result in part from a common neural substrate. 5. As speech rate increased, PD speakers down-scaled movement amplitude and velocity compared to the control subjects, reflecting a compensatory mechanism to maintain target speech rates. 6. EMG from orbicularis oris inferior (OOIm) and depressor labii inferioris (DLIm) muscles revealed a limited range of muscle activation level in PD speakers, reflecting the underlying changes in motor unit firing behavior due to basal ganglia dysfunction. The results of this investigation provided a quantitative description of the perioral stiffness, labial kinematics, and EMG patterns in PD speakers. These findings indicate that perioral stiffness may provide clinicians a quantitative biomechanical correlate to medication response, movement aberrations, and EMG compensatory patterns in PD. The utilization of these objective assessments will be helpful in diagnosing, assessing, and monitoring the progression of PD to examine the efficacy of pharmacological, neurosurgical, and behavioral interventions

Customizable Intraoperative Neural Stimulator and Recording System for Deep Brain Stimulation Research and Surgery.

Intraoperative targeting systems provide neurosurgeons with raw electrophysiological data through microelectrodes used for determining location in the brain. There are significant deficits to the available targeting systems, limiting the use in both clinical and research applications. The work presented in this dissertation is of the development and validation of an intraoperative neural stimulator and recording system for use in deep brain stimulation (DBS) surgeries. This intraoperative data acquisition system (IODA) was validated in three applications to ensure efficacy and improvements in research and clinical studies. The first application investigated was a clinical study illustrating the improvement IODA had on the targeting accuracy of DBS leads in the subthalamic nucleus (STN) over current targeting methods. It was demonstrated that the novel navigation algorithm developed for use with IODA targeted microelectrode probe locations significantly closer to final DBS lead positions compared to preoperatively planned trajectory positions. The second study investigated a clinical science application. There are considerable differences in recently published studies for the optimal chronic stimulation site in the STN region. It was shown, using beta oscillations of local field potentials (LFP) recorded by IODA, that optimal stimulation sites were significantly correlated with locations of peak beta activity when DBS leads were medial to the STN midpoint. While DBS lead trajectories lateral of the STN midpoint were significantly correlated with the dorsal border of the STN. The third study explored a basic science application involving the role of the STN in movement inhibition. Through wideband recordings made with IODA, it was shown that the STN is significantly activated during movement and movement inhibition cues as seen in the theta, alpha, and beta bands and single unit activity. Overall the results indicate the utility and adaptability of this system for use within DBS surgeries. There are many applications of IODA for use in research for other neurodegenerative disease including Essential Tremor and Depression. The use of this system has enables neurosurgeons to reduce surgical time, risk, and error for DBS procedures and made entry for those less experienced in this procedure easier.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99771/1/dodani_1.pd

An entropy-based investigation of underpinnings and impact of oscillations in a model of PD

The involvement of the basal ganglia in motor control has been highlighted in studies of Parkinson’s disease (PD) and other movement disorders. The loss of dopaminergic neurons in the substantia nigra pars compacta and subsequent decrease of the dopamine level in the basal ganglia is recognized as the hallmark of PD. The classical view of the architecture of the dopamine depleted basal ganglia-thalamo-cortical circuit identifies changes in firing rates as the probable cause for the motor impairments in PD. Yet, more recent findings have shown that disturbances in other intrinsic dynamical properties of these networks may also contribute to motor deficits. Electrophysiological recordings in the basal ganglia of deep brain stimulation (DBS) patients (when OFF stimulation) have found pathological oscillations at beta frequency (13-30 Hz). This abnormal oscillatory activity has also been found in basal ganglia nuclei of animal models of PD. Additionally, the beta frequency oscillations were found to decrease when the patients are on dopamine replacement therapies and as they initiate movement. Beta frequency oscillations have been identified in the firing of single neurons and in the coupling of discharges between neurons. Within the framework of information theory, we proposed a time series model to analyse and relate the effects of changes in the dynamics of individual factors – such as alterations in firing rates, oscillations and synchrony (or auto and cross-correlations) caused by dopamine depletion – on the coding capacity (i.e., entropy) of a network. We estimated the entropy of a neural network based on the probabilities of current spiking conditioned on the observation of firing rate and spiking history of the current neuron and of neighbouring neurons. Moreover, we could estimate entropies for each of these factors separately, in healthy and dopamine depleted conditions, and assess their relative contribution to the decrease of coding capacity in disease. Furthermore, the causal characteristics of the model made it possible to compare the synaptic connectivity of neuronal populations in health with that in disease, by measuring the amount of directed information transferred between populations. We employed the model to study the external globus pallidus (GPe) network in control and 6-hydroxydopamine (6-OHDA) lesioned rats – a model for PD. We found a significant decrease in the coding capacity in lesioned animals, compared to controls, and that this decrease was predominantly on account of a reduction in the GPe firing rates. Although to a lesser extent, the amplification of the oscillatory activity (mainly in the beta frequency range) observed in the lesioned animals had also a significant impact on the reduction of their coding capacity. The higher synchrony found in the 6-OHDA rats had the least effect. We also found that the levels of coding capacity in the GPe were restored to levels close to control when the lesioned animals were treated with the dopamine agonist apomorphine. In addition, we detected a stronger coupling between the subthalamic nucleus (STN) and the GPe in the dopamine depleted rats, pointing to an abnormally exaggerated transfer of information within this network. We have shown that the GPe and the STN-GPe networks in the dopamine depleted rat exhibit information processing irregularities. We believe these deficits in processing and relaying information may also be present in other structures of the basal ganglia-thalamo-cortical circuit and that they may underlie the motor impairment in PD