Pallidal Connectivity Profiling of Stimulation‐Induced Dyskinesia in Parkinson's Disease

Mathematical derivation and mechanism analysis of beta oscillations in a cortex-pallidum model

In this paper, we develop a new cortex-pallidum model to study the origin mechanism of Parkinson's oscillations in the cortex. In contrast to many previous models, the globus pallidus internal (GPi) and externa (GPe) both exert direct inhibitory feedback to the cortex. Using Hopf bifurcation analysis, two new critical conditions for oscillations, which can include the self-feedback projection of GPe, are obtained. In this paper, we find that the average discharge rate (ADR) is an important marker of oscillations, which can divide Hopf bifurcations into two types that can uniformly be used to explain the oscillation mechanism. Interestingly, the ADR of the cortex first increases and then decreases with increasing coupling weights that are projected to the GPe. Regarding the Hopf bifurcation critical conditions, the quantitative relationship between the inhibitory projection and excitatory projection to the GPe is monotonically increasing; in contrast, the relationship between different coupling weights in the cortex is monotonically decreasing. In general, the oscillation amplitude is the lowest near the bifurcation points and reaches the maximum value with the evolution of oscillations. The GPe is an effective target for deep brain stimulation to alleviate oscillations in the cortex.

Modeling the volume of tissue activated in deep brain stimulation and its clinical influence: a review

Deep brain stimulation (DBS) is a neuromodulatory therapy that has been FDA approved for the treatment of various disorders, including but not limited to, movement disorders (e.g., Parkinson's disease and essential tremor), epilepsy, and obsessive-compulsive disorder. Computational methods for estimating the volume of tissue activated (VTA), coupled with brain imaging techniques, form the basis of models that are being generated from retrospective clinical studies for predicting DBS patient outcomes. For instance, VTA models are used to generate target-and network-based probabilistic stimulation maps that play a crucial role in predicting DBS treatment outcomes. This review defines the methods for calculation of tissue activation (or modulation) including ones that use heuristic and clinically derived estimates and more computationally involved ones that rely on finite-element methods and biophysical axon models. We define model parameters and provide a comparison of commercial, open-source, and academic simulation platforms available for integrated neuroimaging and neural activation prediction. In addition, we review clinical studies that use these modeling methods as a function of disease. By describing the tissue-activation modeling methods and highlighting their application in clinical studies, we provide the neural engineering and clinical neuromodulation communities with perspectives that may influence the adoption of modeling methods for future DBS studies.

A large normative connectome for exploring the tractographic correlates of focal brain interventions

Diffusion-weighted MRI (dMRI) is a widely used neuroimaging modality that permits the in vivo exploration of white matter connections in the human brain. Normative structural connectomics - the application of large-scale, group-derived dMRI datasets to out-of-sample cohorts - have increasingly been leveraged to study the network correlates of focal brain interventions, insults, and other regions-of-interest (ROIs). Here, we provide a normative, whole-brain connectome in MNI space that enables researchers to interrogate fiber streamlines that are likely perturbed by given ROIs, even in the absence of subject-specific dMRI data. Assembled from multi-shell dMRI data of 985 healthy Human Connectome Project subjects using generalized Q-sampling imaging and multispectral normalization techniques, this connectome comprises ~12 million unique streamlines, the largest to date. It has already been utilized in at least 18 peer-reviewed publications, most frequently in the context of neuromodulatory interventions like deep brain stimulation and focused ultrasound. Now publicly available, this connectome will constitute a useful tool for understanding the wider impact of focal brain perturbations on white matter architecture going forward.

Topological analysis of functional connectivity in Parkinson's disease

Parkinson's disease (PD) is a clinically heterogeneous disorder, which mainly affects patients' motor and non-motor function. Functional connectivity was preliminary explored and studied through resting state functional magnetic resonance imaging (rsfMRI). Through the topological analysis of 54 PD scans and 31 age-matched normal controls (NC) in the Neurocon dataset, leveraging on rsfMRI data, the brain functional connection and the Vietoris-Rips (VR) complex were constructed. The barcodes of the complex were calculated to reflect the changes of functional connectivity neural circuits (FCNC) in brain network. The 0-dimensional Betti number β0 means the number of connected branches in VR complex. The average number of connected branches in PD group was greater than that in NC group when the threshold δ ≤ 0.7. Two-sample Mann-Whitney U test and false discovery rate (FDR) correction were used for statistical analysis to investigate the FCNC changes between PD and NC groups. In PD group, under threshold of 0.7, the number of FCNC involved was significantly differences and these brain regions include the Cuneus_R, Lingual_R, Fusiform_R and Heschl_R. There are also significant differences in brain regions in the Frontal_Inf_Orb_R and Pallidum_R, when the threshold increased to 0.8 and 0.9 (p < 0.05). In addition, when the length of FCNC was medium, there was a significant statistical difference between the PD group and the NC group in the Neurocon dataset and the Parkinson's Progression Markers Initiative (PPMI) dataset. Topological analysis based on rsfMRI data may provide comprehensive information about the changes of FCNC and may provide an alternative for clinical differential diagnosis.

A structural magnetic resonance imaging review of clinical motor outcomes from deep brain stimulation in movement disorders

Patients with movement disorders treated by deep brain stimulation do not always achieve successful therapeutic alleviation of motor symptoms, even in cases where surgery is without complications. Magnetic resonance imaging (MRI) offers methods to investigate structural brain-related factors that may be predictive of clinical motor outcomes. This review aimed to identify features which have been associated with variability in clinical post-operative motor outcomes in patients with Parkinson's disease, dystonia, and essential tremor from structural MRI modalities. We performed a literature search for articles published between 1 January 2000 and 1 April 2022 and identified 5197 articles. Following screening through our inclusion criteria, we identified 60 total studies (39 = Parkinson's disease, 11 = dystonia syndromes and 10 = essential tremor). The review captured a range of structural MRI methods and analysis techniques used to identify factors related to clinical post-operative motor outcomes from deep brain stimulation. Morphometric markers, including volume and cortical thickness were commonly identified in studies focused on patients with Parkinson's disease and dystonia syndromes. Reduced metrics in basal ganglia, sensorimotor and frontal regions showed frequent associations with reduced motor outcomes. Increased structural connectivity to subcortical nuclei, sensorimotor and frontal regions was also associated with greater motor outcomes. In patients with tremor, increased structural connectivity to the cerebellum and cortical motor regions showed high prevalence across studies for greater clinical motor outcomes. In addition, we highlight conceptual issues for studies assessing clinical response with structural MRI and discuss future approaches towards optimizing individualized therapeutic benefits. Although quantitative MRI markers are in their infancy for clinical purposes in movement disorder treatments, structural features obtained from MRI offer the powerful potential to identify candidates who are more likely to benefit from deep brain stimulation and provide insight into the complexity of disorder pathophysiology.

Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

The mechanism of Parkinson oscillation in the cortex: Possible evidence in a feedback model projecting from the globus pallidus to the cortex

The origin, location and cause of Parkinson's oscillation are not clear at present. In this paper, we establish a new cortex-basal ganglia model to study the origin mechanism of Parkinson beta oscillation. Unlike many previous models, this model includes two direct inhibitory projections from the globus pallidus external (GPe) segment to the cortex. We first obtain the critical calculation formula of Parkinson's oscillation by using the method of Quasilinear analysis. Different from previous studies, the formula obtained in this paper can include the self-feedback connection of GPe. Then, we use the bifurcation analysis method to systematically explain the influence of some key parameters on the oscillation. We find that the bifurcation principle of different cortical nuclei is different. In general, the increase of the discharge capacity of the nuclei will cause oscillation. In some special cases, the sharp reduction of the discharge rate of the nuclei will also cause oscillation. The direction of bifurcation simulation is consistent with the critical condition curve. Finally, we discuss the characteristics of oscillation amplitude. At the beginning of the oscillation, the amplitude is relatively small; with the evolution of oscillation, the amplitude will gradually strengthen. This is consistent with the experimental phenomenon. In most cases, the amplitude of cortical inhibitory nuclei (CIN) is greater than that of cortical excitatory nuclei (CEX), and the two direct inhibitory projections feedback from GPe can significantly reduce the amplitude gap between them. We calculate the main frequency of the oscillation generated in this model, which basically falls between 13 and 30 Hz, belonging to the typical beta frequency band oscillation. Some new results obtained in this paper can help to better understand the origin mechanism of Parkinson's disease and have guiding significance for the development of experiments.

Brain Connectomics

A central tenet of modern neuroscience is the conceptualization of the brain as a collection of complex networks or circuits with a shift away from traditional "localizationist" theories. Connectomics seeks to unravel these brain networks and their role in the pathophysiology of neurologic diseases. This article discusses the science of connectomics with the examples of its potential role in clinical medicine and neuromodulation in multiple disorders, such as essential tremor, Parkinson's disease, obsessive-compulsive disorder, and epilepsy.

Altered Intra- and Inter-Network Connectivity in Drug-Naïve Patients With Early Parkinson’s Disease

The aim of our study was to investigate differences in whole brain connectivity at different levels between drug-naïve individuals with early Parkinson’s disease (PD) and healthy controls (HCs). Resting-state functional magnetic resonance imaging data were collected from 47 patients with early-stage, drug-naïve PD and 50 HCs. Functional brain connectivity was analyzed at the integrity, network, and edge levels; UPDRS-III, MMSE, MOCA, HAMA, and HAMD scores, reflecting the symptoms of PD, were collected for further regression analysis. Compared with age-matched HCs, reduced functional connectivity were mainly observed in the visual (VSN), somatomotor (SMN), limbic (LBN), and deep gray matter networks (DGN) at integrity level [p < 0.05, false discovery rate (FDR) corrected]. Intra-network analysis indicated decreased functional connectivity in DGN, SMN, LBN, and ventral attention networks (VAN). Inter-network analysis indicated reduced functional connectivity in nine pairs of resting-state networks. At the edge level, the LBN was the center of abnormal functional connectivity (p < 0.05, FDR corrected). MOCA score was associated with the intra-network functional connectivity strength (FC) of the DGN, and inter-network FC of the DGN-VAN. HAMA and HAMD scores were associated with the FC of the SMN and DGN, and either the LBN or VAN, respectively. We demonstrated variations in whole brain connections of drug-naïve patients with early PD. Major changes involved the SMN, DGN, LBN, and VSN, which may be relevant to symptoms of early PD. Additionally, our results support PD as a disconnection syndrome.

Time for a New 3-D Image for Globus Pallidus Internus Deep Brain Stimulation Targeting and Programming

Deep brain stimulation (DBS) is an effective neuromodulatory therapy for Parkinson’s disease (PD). Early studies using globus pallidus internus (GPi) DBS for PD profiled the nucleus as having two functional zones. This concept disseminated throughout the neuromodulation community as the “GPi triangle”. Although our understanding of the pallidum has greatly evolved over the past 20 years, we continue to reference the triangle in our clinical decision-making process. We propose a new direction, termed the spatial boundary hypothesis, to build upon the 2-dimensional outlook on GPi DBS. We believe an updated 3-D GPi model can produce more consistent, positive patient outcomes.

Connectivity correlates to predict essential tremor deep brain stimulation outcome: Evidence for a common treatment pathway

BACKGROUND AND PURPOSE

Deep brain stimulation (DBS) is the most common surgical treatment for essential tremor (ET), yet there is variation in outcome and stimulation targets. This study seeks to consolidate proposed stimulation "sweet spots," as well as assess the value of structural connectivity in predicting treatment outcomes.

MATERIALS AND METHODS

Ninety-seven ET individuals with unilateral thalamic DBS were retrospectively included. Using normative brain connectomes, structural connectivity measures were correlated with the percentage improvement in contralateral tremor, based on the Fahn-Tolosa-Marin tremor rating scale (TRS), after parameter optimization (range 3.1-12.9 months) using a leave-one-out cross-validation in 83 individuals. The predictive feature map was used for cross-validation in a separate cohort of 14 ET individuals treated at another center. Lastly, estimated volumes of tissue activated (VTA) were used to assess a treatment "sweet spot," which was compared to seven previously reported stimulation sweet spots and their relationship to the tract identified by the predictive feature map.

RESULTS

In the training cohort, structural connectivity between the VTA and dentato-rubro-thalamic tract (DRTT) correlated with contralateral tremor improvement (R = 0.41; p < 0.0001). The same connectivity profile predicted outcomes in a separate validation cohort (R = 0.59; p = 0.028). The predictive feature map represented the anatomical course of the DRTT, and all seven analyzed sweet spots overlapped the predictive tract (DRTT).

CONCLUSIONS

Our results strongly support the possibility that structural connectivity is a predictor of contralateral tremor improvement in ET DBS. The results suggest the future potential for a patient-specific functionally based surgical target. Finally, the results showed convergence in "sweet spots" suggesting the importance of the DRTT to the outcome.

Comparative connectivity correlates of dystonic and essential tremor deep brain stimulation

The pathophysiology of dystonic tremor and essential tremor remains partially understood. In patients with medication-refractory dystonic tremor or essential tremor, deep brain stimulation (DBS) targeting the thalamus or posterior subthalamic area has evolved into a promising treatment option. However, the optimal DBS targets for these disorders remains unknown. This retrospective study explored the optimal targets for DBS in essential tremor and dystonic tremor using a combination of volumes of tissue activated estimation and functional and structural connectivity analyses. We included 20 patients with dystonic tremor who underwent unilateral thalamic DBS, along with a matched cohort of 20 patients with essential tremor DBS. Tremor severity was assessed preoperatively and approximately 6 months after DBS implantation using the Fahn-Tolosa-Marin Tremor Rating Scale. The tremor-suppressing effects of DBS were estimated using the percentage improvement in the unilateral tremor-rating scale score contralateral to the side of implantation. The optimal stimulation region, based on the cluster centre of gravity for peak contralateral motor score improvement, for essential tremor was located in the ventral intermediate nucleus region and for dystonic tremor in the ventralis oralis posterior nucleus region along the ventral intermediate nucleus/ventralis oralis posterior nucleus border (4 mm anterior and 3 mm superior to that for essential tremor). Both disorders showed similar functional connectivity patterns: a positive correlation between tremor improvement and involvement of the primary sensorimotor, secondary motor and associative prefrontal regions. Tremor improvement, however, was tightly correlated with the primary sensorimotor regions in essential tremor, whereas in dystonic tremor, the correlation was tighter with the premotor and prefrontal regions. The dentato-rubro-thalamic tract, comprising the decussating and non-decussating fibres, significantly correlated with tremor improvement in both dystonic and essential tremor. In contrast, the pallidothalamic tracts, which primarily project to the ventralis oralis posterior nucleus region, significantly correlated with tremor improvement only in dystonic tremor. Our findings support the hypothesis that the pathophysiology underpinning dystonic tremor involves both the cerebello-thalamo-cortical network and the basal ganglia-thalamo-cortical network. Further our data suggest that the pathophysiology of essential tremor is primarily attributable to the abnormalities within the cerebello-thalamo-cortical network. We conclude that the ventral intermediate nucleus/ventralis oralis posterior nucleus border and ventral intermediate nucleus region may be a reasonable DBS target for patients with medication-refractory dystonic tremor and essential tremor, respectively. Uncovering the pathophysiology of these disorders may in the future aid in further improving DBS outcomes.