Basal Ganglia dysfunctions in movement disorders: What can be learned from computational simulations

Oscillatory vs. non-oscillatory subthalamic beta activity in Parkinson's disease

Parkinson's disease is characterized by exaggerated beta activity (13-35 Hz) in cortico-basal ganglia motor loops. Beta activity includes both periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and excitation/inhibition balance (i.e. non-oscillatory activity). However, the relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. We recorded, modelled and analysed subthalamic local field potentials in parkinsonian patients at rest while off or on medication. Autoregressive modelling with additive 1/f noise clarified the relationships between measures of beta activity in the time domain (i.e. amplitude and duration of beta bursts) or in the frequency domain (i.e. power and sharpness of the spectral peak) and oscillatory vs. non-oscillatory activity: burst duration and spectral sharpness are specifically sensitive to oscillatory activity, whereas burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Our experimental data confirmed the model predictions and assumptions. We subsequently analysed the effect of levodopa, obtaining strong-to-extreme Bayesian evidence that oscillatory beta activity is reduced in patients on vs. off medication, with moderate evidence for absence of modulation of the non-oscillatory component. Finally, specifically the oscillatory component of beta activity correlated with the rate of motor progression of the disease. Methodologically, these results provide an integrative understanding of beta-based biomarkers relevant for adaptive deep brain stimulation. Biologically, they suggest that primarily the oscillatory component of subthalamic beta activity is dopamine dependent and may play a role not only in the pathophysiology but also in the progression of Parkinson's disease. KEY POINTS: Beta activity in Parkinson's disease includes both true periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and synaptic balance (i.e. non-oscillatory activity). The relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. Burst duration and spectral sharpness are specifically sensitive to oscillatory activity, while burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Only the oscillatory component of subthalamic beta activity is dopamine-dependent. Stronger beta oscillatory activity correlates with faster motor progression of the disease.

Filgrastim, a Recombinant Form of Granulocyte Colony-stimulating Factor, Ameliorates 3-nitropropionic Acid and Haloperidol-induced Striatal Neurotoxicity in Rats

Striatal neurotoxicity is the pathological hallmark for a heterogeneous group of movement disorders like Tardive dyskinesia (TD) and Huntington's disease (HD). Both diseases are characterized by progressive impairment in motor function. TD and HD share common features at both cellular and subcellular levels. Filgrastim, a recombinant methionyl granulocyte colony-stimulating factor (GCSF), shows neuroprotective properties in in-vivo models of movement disorders. This study seeks to evaluate the neuroprotective effect of filgrastim in haloperidol and 3-NP-induced neurotoxicity in rats. The study was divided into two: in study one, rats were administered with haloperidol for 21 days, filgrastim at the dose of (20, 40, 60 µg/kg,s.c.) was administered once a day before haloperidol treatment and the following parameters (orofacial movements, rotarod, actophotometer) were performed to assess TD. Similarly, in the second study, rats were administered with 3-NP for 21 days, filgrastim at a dose of (20 and 40 µg/kg, s.c.) was administered, and the following parameters (rotarod, narrow beam walk, and open field test) were assessed for HD. On the 22nd day, animals were sacrificed and cortex and striatum isolated for oxidative stress (LPO, GSH, SOD, catalase, and nitrate) marker assessment. Results revealed that haloperidol and 3-NP treatment significantly impaired motor coordination, and oxidative defense inducing TD and HD-like symptoms. Treatment with filgrastim significantly averted haloperidol and 3-NP-induced behavioral and biochemical alterations. Conclusively, the neuroprotective effect of filgrastim is credited to its antioxidant properties. Hence, filgrastim might be a novel therapeutic candidate for the management of TD and HD.

Building and Understanding the Minimal Self

Within the methodologically diverse interdisciplinary research on the minimal self, we identify two movements with seemingly disparate research agendas - cognitive science and cognitive (developmental) robotics. Cognitive science, on the one hand, devises rather abstract models which can predict and explain human experimental data related to the minimal self. Incorporating the established models of cognitive science and ideas from artificial intelligence, cognitive robotics, on the other hand, aims to build embodied learning machines capable of developing a self "from scratch" similar to human infants. The epistemic promise of the latter approach is that, at some point, robotic models can serve as a testbed for directly investigating the mechanisms that lead to the emergence of the minimal self. While both approaches can be productive for creating causal mechanistic models of the minimal self, we argue that building a minimal self is different from understanding the human minimal self. Thus, one should be cautious when drawing conclusions about the human minimal self based on robotic model implementations and vice versa. We further point out that incorporating constraints arising from different levels of analysis will be crucial for creating models that can predict, generate, and causally explain behavior in the real world.

Personalized Medicine to Improve Treatment of Dopa-Responsive Dystonia-A Focus on Tyrosine Hydroxylase Deficiency

Dopa-responsive dystonia (DRD) is a rare movement disorder associated with defective dopamine synthesis. This impairment may be due to the fact of a deficiency in GTP cyclohydrolase I (GTPCHI, GCH1 gene), sepiapterin reductase (SR), tyrosine hydroxylase (TH), or 6-pyruvoyl tetrahydrobiopterin synthase (PTPS) enzyme functions. Mutations in GCH1 are most frequent, whereas fewer cases have been reported for individual SR-, PTP synthase-, and TH deficiencies. Although termed DRD, a subset of patients responds poorly to L-DOPA. As this is regularly observed in severe cases of TH deficiency (THD), there is an urgent demand for more adequate or personalized treatment options. TH is a key enzyme that catalyzes the rate-limiting step in catecholamine biosynthesis, and THD patients often present with complex and variable phenotypes, which results in frequent misdiagnosis and lack of appropriate treatment. In this expert opinion review, we focus on THD pathophysiology and ongoing efforts to develop novel therapeutics for this rare disorder. We also describe how different modeling approaches can be used to improve genotype to phenotype predictions and to develop in silico testing of treatment strategies. We further discuss the current status of mathematical modeling of catecholamine synthesis and how such models can be used together with biochemical data to improve treatment of DRD patients.

On the Role of Arkypallidal and Prototypical Neurons for Phase Transitions in the External Pallidum

The external pallidum (globus pallidus pars externa [GPe]) plays a central role for basal ganglia functions and dynamics and, consequently, has been included in most computational studies of the basal ganglia. These studies considered the GPe as a homogeneous neural population. However, experimental studies have shown that the GPe contains at least two distinct cell types (prototypical and arkypallidal cells). In this work, we provide in silico insight into how pallidal heterogeneity modulates dynamic regimes inside the GPe and how they affect the GPe response to oscillatory input. We derive a mean-field model of the GPe system from a microscopic spiking neural network of recurrently coupled prototypical and arkypallidal neurons. Using bifurcation analysis, we examine the influence of dopamine-dependent changes of intrapallidal connectivity on the GPe dynamics. We find that increased self-inhibition of prototypical cells can induce oscillations, whereas increased inhibition of prototypical cells by arkypallidal cells leads to the emergence of a bistable regime. Furthermore, we show that oscillatory input to the GPe, arriving from striatum, leads to characteristic patterns of cross-frequency coupling observed at the GPe. Based on these findings, we propose two different hypotheses of how dopamine depletion at the GPe may lead to phase-amplitude coupling between the parkinsonian beta rhythm and a GPe-intrinsic γ rhythm. Finally, we show that these findings generalize to realistic spiking neural networks of sparsely coupled Type I excitable GPe neurons.SIGNIFICANCE STATEMENT Our work provides (1) insight into the theoretical implications of a dichotomous globus pallidus pars externa (GPe) organization, and (2) an exact mean-field model that allows for future investigations of the relationship between GPe spiking activity and local field potential fluctuations. We identify the major phase transitions that the GPe can undergo when subject to static or periodic input and link these phase transitions to the emergence of synchronized oscillations and cross-frequency coupling in the basal ganglia. Because of the close links between our model and experimental findings on the structure and dynamics of prototypical and arkypallidal cells, our results can be used to guide both experimental and computational studies on the role of the GPe for basal ganglia dynamics in health and disease.

Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson's disease

Parkinson's disease motor symptoms are associated with an increase in subthalamic nucleus beta band oscillatory power. However, these oscillations are phasic, and there is a growing body of evidence suggesting that beta burst duration may be of critical importance to motor symptoms. This makes insights into the dynamics of beta bursting generation valuable, in particular to refine closed-loop deep brain stimulation in Parkinson's disease. In this study, we ask the question "Can average burst duration reveal how dynamics change between the ON and OFF medication states?". Our analysis of local field potentials from the subthalamic nucleus demonstrates using linear surrogates that the system generating beta oscillations is more likely to act in a non-linear regime OFF medication and that the change in a non-linearity measure is correlated with motor impairment. In addition, we pinpoint the simplest dynamical changes that could be responsible for changes in the temporal patterning of beta oscillations between medication states by fitting to data biologically inspired models, and simpler beta envelope models. Finally, we show that the non-linearity can be directly extracted from average burst duration profiles under the assumption of constant noise in envelope models. This reveals that average burst duration profiles provide a window into burst dynamics, which may underlie the success of burst duration as a biomarker. In summary, we demonstrate a relationship between average burst duration profiles, dynamics of the system generating beta oscillations, and motor impairment, which puts us in a better position to understand the pathology and improve therapies such as deep brain stimulation.

Drug repositioning for psychiatric and neurological disorders through a network medicine approach

Psychiatric and neurological disorders (PNDs) affect millions worldwide and only a few drugs achieve complete therapeutic success in the treatment of these disorders. Due to the high cost of developing novel drugs, drug repositioning represents a promising alternative method of treatment. In this manuscript, we used a network medicine approach to investigate the molecular characteristics of PNDs and identify novel drug candidates for repositioning. Using IBM Watson for Drug Discovery, a powerful machine learning text-mining application, we built knowledge networks containing connections between PNDs and genes or drugs mentioned in the scientific literature published in the past 50 years. This approach revealed several drugs that target key PND-related genes, which have never been used to treat these disorders to date. We validate our framework by detecting drugs that have been undergoing clinical trial for treating some of the PNDs, but have no published results in their support. Our data provides comprehensive insights into the molecular pathology of PNDs and offers promising drug repositioning candidates for follow-up trials.

Basal ganglia role in learning rewarded actions and executing previously learned choices: Healthy and diseased states

The basal ganglia (BG) is a collection of nuclei located deep beneath the cerebral cortex that is involved in learning and selection of rewarded actions. Here, we analyzed BG mechanisms that enable these functions. We implemented a rate model of a BG-thalamo-cortical loop and simulated its performance in a standard action selection task. We have shown that potentiation of corticostriatal synapses enables learning of a rewarded option. However, these synapses became redundant later as direct connections between prefrontal and premotor cortices (PFC-PMC) were potentiated by Hebbian learning. After we switched the reward to the previously unrewarded option (reversal), the BG was again responsible for switching to the new option. Due to the potentiated direct cortical connections, the system was biased to the previously rewarded choice, and establishing the new choice required a greater number of trials. Guided by physiological research, we then modified our model to reproduce pathological states of mild Parkinson's and Huntington's diseases. We found that in the Parkinsonian state PMC activity levels become extremely variable, which is caused by oscillations arising in the BG-thalamo-cortical loop. The model reproduced severe impairment of learning and predicted that this is caused by these oscillations as well as a reduced reward prediction signal. In the Huntington state, the potentiation of the PFC-PMC connections produced better learning, but altered BG output disrupted expression of the rewarded choices. This resulted in random switching between rewarded and unrewarded choices resembling an exploratory phase that never ended. Along with other computational studies, our results further reconcile the apparent contradiction between the critical involvement of the BG in execution of previously learned actions and yet no impairment of these actions after BG output is ablated by lesions or deep brain stimulation. We predict that the cortico-BG-thalamo-cortical loop conforms to previously learned choice in healthy conditions, but impedes those choices in disease states.

Multistable properties of human subthalamic nucleus neurons in Parkinson's disease

Behaviors are realized through concerted activity in neural circuits. This activity results from a combination of neural connectivity and the properties of the involved neurons. By studying the activity of neurons in the human subthalamic nucleus during surgery for Parkinson’s disease, we report that these neurons have multiple stable states, and that brief electrical stimuli can lead to transitions between states. We thus suggest that these neurons function as finite state machines. The different states could influence the function of key motor circuits of the basal ganglia, and thus knowledge of these states in disease or in response to treatment could help to define new treatment strategies for people with movement disorders.

To understand the function and dysfunction of neural circuits, it is necessary to understand the properties of the neurons participating in the behavior, the connectivity between these neurons, and the neuromodulatory status of the circuits at the time they are producing the behavior. Such knowledge of human neural circuits is difficult, at best, to obtain. Here, we study firing properties of human subthalamic neurons, using microelectrode recordings and microstimulation during awake surgery for Parkinson’s disease. We demonstrate that low-amplitude, brief trains of microstimulation can lead to persistent changes in neuronal firing behavior including switching between firing rates, entering silent periods, or firing several bursts then entering a silent period. We suggest that these multistable states reflect properties of finite state machines and could have implications for the function of circuits involving the subthalamic nucleus. Furthermore, understanding these states could lead to therapeutic strategies aimed at regulating the transitions between states.

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Effects of exercise on mGluR-mediated glutamatergic transmission in the striatum of hemiparkinsonian rats

Hyperexcitability in the corticostriatal glutamatergic pathway may have a pivotal role in the pathogenesis of Parkinson's disease (PD). Metabotropic glutamate receptors (mGluRs) modulate glutamate transmission by both pre- and postsynaptic mechanisms, making them attractive targets for modifying pathological changes in the corticostriatal pathway. Exercise reportedly alleviates motor dysfunction and induced neuroplasticity in glutamatergic transmission. Here, the mGluR-mediated plasticity mechanism underlying behavioral improvement by exercise intervention was investigated. The experimental models were prepared by 6-hydroxydopamine injection into the right medial forebrain bundle. The models were evaluated with the apomorphine-induced rotation test. Starting 2 weeks postoperatively, exercise intervention was applied to the PD + Ex group for 4 weeks. The exercise-intervention effects on locomotor behavior, glutamate levels, and mGluR (mGluR2/3 and mGluR5) expression in hemiparkinsonian rats were investigated. The results showed that hemiparkinsonian rats have a significant increase in extracellular glutamate levels in the lesioned-lateral striatum. MGluR2/3 protein expression was reduced while mGluR5 protein expression was increased in the striatum. Notably, treadmill exercise markedly reversed these abnormal changes in the corticostriatal glutamate system and promoted motor performance in PD rats. These findings suggest that mGluR-mediated glutamatergic transmission in the corticostriatal pathway may serve as an attractive target for exercise-induced neuroplasticity in hemiparkinsonian rats.

The effect of dopamine on response inhibition in Parkinson's disease relates to age-dependent patterns of nigrostriatal degeneration

INTRODUCTION

Motor but also non-motor effects are modulated by dopamine (DA) in Parkinson's disease (PD). Impaired inhibition has been related to dopamine overdosing of the associative striatum. We compared effects of dopaminergic medication on inhibitory control in patients with young (age at onset <50 years, YOPD) and late onset PD (LOPD) and related them to nigrostriatal degeneration.

METHODS

27 patients (10 YOPD, 17 LOPD) underwent a Go/NoGo paradigm comprising a global and specific NoGo condition ON and OFF DA. The ratio of dopamine transporter availability (DAT) in the associative relative to the sensorimotor striatum according to [¹²³I]FP-CIT SPECT was compared between YOPD and LOPD (n = 8/12). Neuro-computational modeling was used to identify pathway activation during Go/NoGo performance.

RESULTS

Patients made more errors ON compared to OFF in the global NoGo. This DA effect on global NoGo errors correlated with disease duration (r = 0.489, p = 0.010). YOPD made more errors in the specific NoGo ON-OFF compared to LOPD (p = 0.015). YOPD showed higher associative-to-sensorimotor DAT ratios compared to LOPD (p < 0.001). Neuro-computational modeling revealed DA overdosing of the associative striatum in YOPD resulting in excess activation of the direct basal ganglia pathway triggering incorrect responses.

CONCLUSIONS

Depending on the age of symptom onset, DA differentially modulated inhibition in PD with detrimental effects on specific NoGo performance in YOPD but increased performance in LOPD. YOPD showed relatively less degeneration in the associative striatum suggesting DA overdosing that is supported by our neuro-computational model. Reduced inhibition in the global NoGo condition suggests different pathway activation.

Insights into Parkinson's disease from computational models of the basal ganglia

Movement disorders arise from the complex interplay of multiple changes to neural circuits. Successful treatments for these disorders could interact with these complex changes in myriad ways, and as a consequence their mechanisms of action and their amelioration of symptoms are incompletely understood. Using Parkinson's disease as a case study, we review here how computational models are a crucial tool for taming this complexity, across causative mechanisms, consequent neural dynamics and treatments. For mechanisms, we review models that capture the effects of losing dopamine on basal ganglia function; for dynamics, we discuss models that have transformed our understanding of how beta-band (15-30 Hz) oscillations arise in the parkinsonian basal ganglia. For treatments, we touch on the breadth of computational modelling work trying to understand the therapeutic actions of deep brain stimulation. Collectively, models from across all levels of description are providing a compelling account of the causes, symptoms and treatments for Parkinson's disease.

Chronic deep brain stimulation normalizes scalp EEG activity in isolated dystonia

OBJECTIVE

To investigate cortical activity using scalp EEG in patients with isolated dystonia treated with chronic deep brain stimulation (DBS), on and off stimulation.

METHODS

We analyzed 64-channel scalp EEG in 12 isolated dystonia patients treated with chronic DBS (7 generalized, 5 cervical/segmental; 7 globus pallidus (GP), 5 subthalamic nucleus (STN)), and 20 healthy age-matched controls. Recordings during rest and movement task, and clinical motor scores, were collected with DBS-on and during a 90-min DBS washout.

RESULTS

Resting state alpha power in the dominant (or contralateral to more dystonic side) motor cortex channel during DBS was comparable to healthy controls, but it increased when DBS was stopped. Resting state and movement-related alpha coherence between bilateral motor cortex channels was increased off DBS.

CONCLUSIONS

Chronic DBS reduces exaggerated alpha oscillations and interhemispheric alpha coherence in the motor cortex of patients with isolated dystonia.

SIGNIFICANCE

These findings complement related studies in Parkinson's disease and support the view that network desynchronization is a prominent mechanism of DBS in movement disorders.

Impairments in Spatiotemporal Gait Adaptation During Obstacle Navigation in Huntington's Disease

BACKGROUND

Navigating obstacles whilst walking might be associated with poorer balance and a higher risk of falling in individuals with symptomatic Huntington's disease (symp-HD). However, this issue has not been investigated within the literature.

OBJECTIVE

A unique obstacle navigation experiment was designed to examine adaptive gait patterns in order to identify spatiotemporal gait characteristics that might be associated with poorer balance and a higher risk of falling in symp-HD.

METHOD

Sixteen diagnosed symp-HD participants and 16 age- and sex-matched healthy controls were included. Gait was examined in 3 experimental conditions: baseline walking, walking while navigating around 1 obstacle, and walking while navigating around 2 obstacles. Navigation around obstacle walks was divided into three step phases (approach, navigation, recovery). Group differences in gait variables were analyzed at baseline and during walking for each obstacle condition respectively. Gait variables were also correlated with the Berg Balance Scale (BBS) and Timed Up and Go (TUG) test.

RESULTS

Symp-HD participants, compared with controls, performed significantly poorer on most gait variables during baseline walking. Symp-HD participants significantly decreased their step-length while navigating around 1 obstacle, and increased their step-time while navigating around 1 and 2 obstacles. There were no significant group differences in step-width. Variables associated with navigating around obstacles correlated significantly with BBS and TUG clinical tools, which have been associated in the literature with an increased risk of falling in symp-HD.

CONCLUSION

These findings could aid clinicians in better managing risk of falls in people with Huntington's disease through targeted and effective strategies.

Computational models of basal ganglia dysfunction: the dynamics is in the details

The development, simulation, and analysis of mathematical models offer helpful tools for integrating experimental findings and exploring or suggesting possible explanatory mechanisms. As models relating to basal ganglia dysfunction have proliferated, however, there has not always been consistency among their findings. This work points out several ways in which biological details, relating to ionic currents and synaptic pathways, can influence the dynamics of models of the basal ganglia under parkinsonian conditions and hence may be important for inclusion in models. It also suggests some additional useful directions for future modeling studies relating to basal ganglia dysfunction.

Quantitative Susceptibility Mapping Reveals an Association between Brain Iron Load and Depression Severity

Previous studies have detected abnormal serum ferritin levels in patients with depression; however, the results have been inconsistent. This study used quantitative susceptibility mapping (QSM) for the first time to examine brain iron concentration in depressed patients and evaluated whether it is related to severity. We included three groups of age- and gender-matched participants: 30 patients with mild-moderate depression (MD), 14 patients with major depression disorder (MDD) and 20 control subjects. All participants underwent MR scans with a 3D gradient-echo sequence reconstructing for QSM and performed the 17-item Hamilton Depression Rating Scale (HDRS) test. In MDD, the susceptibility value in the bilateral putamen was significantly increased compared with MD or control subjects. In addition, a significant difference was also observed in the left thalamus in MDD patients compared with controls. However, the susceptibility values did not differ between MD patients and controls. The susceptibility values positively correlated with the severity of depression as indicated by the HDRS scores. Our results provide evidence that brain iron deposition may be associated with depression and may even be a biomarker for investigating the pathophysiological mechanism of depression.

A Dynamic Circuit Hypothesis for the Pathogenesis of Blepharospasm

Blepharospasm (sometimes called “benign essential blepharospasm,” BEB) is one of the most common focal dystonias. It involves involuntary eyelid spasms, eye closure, and increased blinking. Despite the success of botulinum toxin injections and, in some cases, pharmacologic or surgical interventions, BEB treatments are not completely efficacious and only symptomatic. We could develop principled strategies for preventing and reversing the disease if we knew the pathogenesis of primary BEB. The objective of this study was to develop a conceptual framework and dynamic circuit hypothesis for the pathogenesis of BEB. The framework extends our overarching theory for the multifactorial pathogenesis of focal dystonias (Peterson et al., 2010) to incorporate a two-hit rodent model specifically of BEB (Schicatano et al., 1997). We incorporate in the framework three features critical to cranial motor control: (1) the joint influence of motor cortical regions and direct descending projections from one of the basal ganglia output nuclei, the substantia nigra pars reticulata, on brainstem motor nuclei, (2) nested loops composed of the trigeminal blink reflex arc and the long sensorimotor loop from trigeminal nucleus through thalamus to somatosensory cortex back through basal ganglia to the same brainstem nuclei modulating the reflex arc, and (3) abnormalities in the basal ganglia dopamine system that provide a sensorimotor learning substrate which, when combined with patterns of increased blinking, leads to abnormal sensorimotor mappings manifest as BEB. The framework explains experimental data on the trigeminal reflex blink excitability (TRBE) from Schicatano et al. and makes predictions that can be tested in new experimental animal models based on emerging genetics in dystonia, including the recently characterized striatal-specific D1R dopamine transduction alterations caused by the GNAL mutation. More broadly, the model will provide a guide for future efforts to mechanistically link multiple factors in the pathogenesis of BEB and facilitate simulations of how exogenous manipulations of the pathogenic factors could ultimately be used to prevent and reverse the disorder.