Dysfunctional and compensatory synaptic plasticity in Parkinson's disease

The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach

Motor learning involves a widespread brain network including the basal ganglia, cerebellum, motor cortex, and brainstem. Despite its importance, little is known about how this network learns motor tasks and which role different parts of this network take. We designed a systems-level computational model of motor learning, including a cortex-basal ganglia motor loop and the cerebellum that both determine the response of central pattern generators in the brainstem. First, we demonstrate its ability to learn arm movements toward different motor goals. Second, we test the model in a motor adaptation task with cognitive control, where the model replicates human data. We conclude that the cortex-basal ganglia loop learns via a novelty-based motor prediction error to determine concrete actions given a desired outcome, and that the cerebellum minimizes the remaining aiming error.

D2R signaling in striatal spiny neurons modulates L-DOPA induced dyskinesia

Degeneration of dopaminergic neurons leads to Parkinson’s disease (PD), characterized by reduced levels of striatal dopamine (DA) and impaired voluntary movements. DA replacement is achieved by levodopa treatment which in long-term causes involuntary movements or dyskinesia. Dyskinesia is linked to the pulsatile activation of D1 receptors of the striatal medium spiny neurons (MSNs) forming the direct output pathway (dMSNs). The contribution of DA stimulation of D2R in MSNs of the indirect pathway (iMSNs) is less clear. Using the 6-hydroxydopamine model of PD, here we show that loss of DA-mediated inhibition of these neurons intensifies levodopa-induced dyskinesia (LID) leading to reprogramming of striatal gene expression. We propose that the motor impairments characteristic of PD and of its therapy are critically dependent on D2R-mediated iMSNs activity. D2R signaling not only filters inputs to the striatum but also indirectly regulates dMSNs mediated responses.

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D2RKO in iMSNs increases L-DOPA-induced dyskinesia (LID)

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D2R signaling in iMSNs inhibits striatal gene and PD-associated genes

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Unopposed M1R signaling is responsible for the increased LID in iMSN-D2RKO mice

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Simultaneous modulation of M1R and M4R signaling on MSNs drastically reduces LID

Transcranial Current Stimulation as a Tool of Neuromodulation of Cognitive Functions in Parkinson’s Disease

Decrease in cognitive function is one of the most common causes of poor life quality and early disability in patients with Parkinson’s disease (PD). Existing methods of treatment are aimed at both correction of motor and non-motor symptoms. Methods of adjuvant therapy (or complementary therapy) for maintaining cognitive functions in patients with PD are of interest. A promising subject of research in this regard is the method of transcranial electric current stimulation (tES). Here we reviewed the current understanding of the pathogenesis of cognitive impairment in PD and of the effects of transcranial direct current stimulation and transcranial alternating current stimulation on the cognitive function of patients with PD-MCI (Parkinson’s Disease–Mild Cognitive Impairment).

Enhanced habit formation in Tourette patients explained by shortcut modulation in a hierarchical cortico-basal ganglia model

Devaluation protocols reveal that Tourette patients show an increased propensity to habitual behaviors as they continue to respond to devalued outcomes in a cognitive stimulus-response-outcome association task. We use a neuro-computational model of hierarchically organized cortico-basal ganglia-thalamo-cortical loops to shed more light on habit formation and its alteration in Tourette patients. In our model, habitual behavior emerges from cortico-thalamic shortcut connections, where enhanced habit formation can be linked to faster plasticity in the shortcut or to a stronger feedback from the shortcut to the basal ganglia. We explore two major hypotheses of Tourette pathophysiology-local striatal disinhibition and increased dopaminergic modulation of striatal medium spiny neurons-as causes for altered shortcut activation. Both model changes altered shortcut functioning and resulted in higher rates of responses towards devalued outcomes, similar to what is observed in Tourette patients. We recommend future experimental neuroscientific studies to locate shortcuts between cortico-basal ganglia-thalamo-cortical loops in the human brain and study their potential role in health and disease.

Optimal attention tuning in a neuro-computational model of the visual cortex-basal ganglia-prefrontal cortex loop

Visual attention is widely considered a vital factor in the perception and analysis of a visual scene. Several studies explored the effects and mechanisms of top-down attention, but the mechanisms that determine the attentional signal are less explored. By developing a neuro-computational model of visual attention including the visual cortex-basal ganglia loop, we demonstrate how attentional alignment can evolve based on dopaminergic reward during a visual search task. Unlike most previous modeling studies of feature-based attention, we do not implement a manually predefined attention template. Dopamine-modulated covariance learning enable the basal ganglia to learn rewarded associations between the visual input and the attentional gain represented in the PFC of the model. Hence, the model shows human-like performance on a visual search task by optimally tuning the attention signal. In particular, similar as in humans, this reward-based tuning in the model leads to an attentional template that is not centered on the target feature, but a relevant feature deviating away from the target due to the presence of highly similar distractors. Further analyses of the model shows, attention is mainly guided by the signal-to-noise ratio between target and distractors.

Insulin Signaling as a Therapeutic Target in Glaucomatous Neurodegeneration

Glaucoma is a multifactorial disease that is conventionally managed with treatments to lower intraocular pressure (IOP). Despite these efforts, many patients continue to lose their vision. The degeneration of retinal ganglion cells (RGCs) and their axons in the optic tract that characterizes glaucoma is similar to neurodegeneration in other age-related disorders of the central nervous system (CNS). Identifying the different molecular signaling pathways that contribute to early neuronal dysfunction can be utilized for neuroprotective strategies that prevent degeneration. The discovery of insulin and its receptor in the CNS and retina led to exploration of the role of insulin signaling in the CNS. Historically, insulin was considered a peripherally secreted hormone that regulated glucose homeostasis, with no obvious roles in the CNS. However, a growing number of pre-clinical and clinical studies have demonstrated the potential of modulating insulin signaling in the treatment of neurodegenerative diseases. This review will highlight the role that insulin signaling plays in RGC neurodegeneration. We will focus on how this pathway can be therapeutically targeted to promote RGC axon survival and preserve vision.

A spiking model of basal ganglia dynamics in stopping behavior supported by arkypallidal neurons

The common view that stopping action plans by the basal ganglia is achieved mainly by the subthalamic nucleus alone due to its direct excitatory projection onto the output nuclei of the basal ganglia has been challenged by recent findings. The proposed "pause-then-cancel" model suggests that the subthalamic nucleus provides a rapid stimulus-unspecific "pause" signal, followed by a stop-cue-specific "cancel" signal from striatum-projecting arkypallidal neurons. To determine more precisely the relative contribution of the different basal ganglia nuclei in stopping, we simulated a stop-signal task with a spiking neuron model of the basal ganglia, considering recently discovered connections from the arkypallidal neurons, and cortex-projecting GPe neurons. For the arkypallidal and prototypical GPe neurons, we obtained neuron model parameters by fitting their neuronal responses to published experimental data. Our model replicates findings of stop-signal tasks at neuronal and behavioral levels. We provide evidence for the existence of a stop-related cortical input to the arkypallidal and cortex-projecting GPe neurons such that the stop responses of the subthalamic nucleus, the arkypallidal neurons, and the cortex-projecting GPe neurons complement each other to achieve functional stopping behavior. Particularly, the cortex-projecting GPe neurons may complement the stopping within the basal ganglia caused by the arkypallidal and STN neurons by diminishing cortical go-related processes. Furthermore, we predict effects of lesions on stopping performance and propose that arkypallidal neurons mainly participate in stopping by inhibiting striatal neurons of the indirect rather than the direct pathway.

A non-linear deterministic model of action selection in the basal ganglia to simulate motor fluctuations in Parkinson's disease

Motor fluctuations and dyskinesias are severe complications of Parkinson's disease (PD), especially evident at its advanced stage, under long-term levodopa therapy. Despite their strong clinical prevalence, the neural origin of these motor symptoms is still a subject of intense debate. In this work, a non-linear deterministic neurocomputational model of the basal ganglia (BG), inspired by biology, is used to provide more insights into possible neural mechanisms at the basis of motor complications in PD. In particular, the model is used to simulate the finger tapping task. The model describes the main neural pathways involved in the BG to select actions [the direct or Go, the indirect or NoGo, and the hyperdirect pathways via the action of the sub-thalamic nucleus (STN)]. A sensitivity analysis is performed on some crucial model parameters (the dopamine level, the strength of the STN mechanism, and the strength of competition among different actions in the motor cortex) at different levels of synapses, reflecting major or minor motor training. Depending on model parameters, results show that the model can reproduce a variety of clinically relevant motor patterns, including normokinesia, bradykinesia, several attempts before movement, freezing, repetition, and also irregular fluctuations. Motor symptoms are, especially, evident at low or high dopamine levels, with excessive strength of the STN and with weak competition among alternative actions. Moreover, these symptoms worsen if the synapses are subject to insufficient learning. The model may help improve the comprehension of motor complications in PD and, ultimately, may contribute to the treatment design.

Habit learning in hierarchical cortex-basal ganglia loops

How do the multiple cortico-basal ganglia-thalamo-cortical loops interact? Are they parallel and fully independent or controlled by an arbitrator, or are they hierarchically organized? We introduce here a set of four key concepts, integrated and evaluated by means of a neuro-computational model, that bring together current ideas regarding cortex-basal ganglia interactions in the context of habit learning. According to key concept 1, each loop learns to select an intermediate objective at a different abstraction level, moving from goals in the ventral striatum to motor in the putamen. Key concept 2 proposes that the cortex integrates the basal ganglia selection with environmental information regarding the achieved objective. Key concept 3 claims shortcuts between loops, and key concept 4 predicts that loops compute their own prediction error signal for learning. Computational benefits of the key concepts are demonstrated. Contrasting with former concepts of habit learning, the loops collaborate to select goal-directed actions while training slower shortcuts develops habitual responses.

Mathematical modeling and parameter estimation of levodopa motor response in patients with parkinson disease

Parkinson disease (PD) is characterized by a clear beneficial motor response to levodopa (LD) treatment. However, with disease progression and longer LD exposure, drug-related motor fluctuations usually occur. Recognition of the individual relationship between LD concentration and its effect may be difficult, due to the complexity and variability of the mechanisms involved. This work proposes an innovative procedure for the automatic estimation of LD pharmacokinetics and pharmacodynamics parameters, by a biologically-inspired mathematical model. An original issue, compared with previous similar studies, is that the model comprises not only a compartmental description of LD pharmacokinetics in plasma and its effect on the striatal neurons, but also a neurocomputational model of basal ganglia action selection. Parameter estimation was achieved on 26 patients (13 with stable and 13 with fluctuating LD response) to mimic plasma LD concentration and alternate finger tapping frequency along four hours after LD administration, automatically minimizing a cost function of the difference between simulated and clinical data points. Results show that individual data can be satisfactorily simulated in all patients and that significant differences exist in the estimated parameters between the two groups. Specifically, the drug removal rate from the effect compartment, and the Hill coefficient of the concentration-effect relationship were significantly higher in the fluctuating than in the stable group. The model, with individualized parameters, may be used to reach a deeper comprehension of the PD mechanisms, mimic the effect of medication, and, based on the predicted neural responses, plan the correct management and design innovative therapeutic procedures.

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.

Impaired Fine Motor Function of the Asymptomatic Hand in Unilateral Parkinson's Disease

The early detection of Parkinson's disease (PD) still remains a challenge to date. Although studies have previously reported subtle motor function abnormalities in early PD patients, it is unclear whether such clinical signs can be better detected while patients are concurrently performing a cognitive task, and whether they can be useful in predicting patients' clinical conversion state. Seventy-two right-handed participants (40 drug-naive patients with idiopathic unilateral PD and 32 age-matched healthy controls) were enrolled in this study. All participants were asked to perform the Purdue Pegboard test (PPT) either alone (single-task condition) or during a concurrent mental subtraction-by-3 task (dual-task condition). A 4-year telephone follow-up was later conducted to determine whether PD patients converted to bilateral signs. We found that PD patients showed a significant reduction in dexterity on the PPT compared to the controls in both single- and dual-task conditions. Yet patients' performance in the dual-task condition revealed a greater interference effect when patients performed the task with their right hand than with their left hand. PPT also revealed reasonable discriminative ability for prediagnosing PD. However, dual-tasking did not have added value in differentiating early patients and controls. At follow-up, the baseline PPT performance of the asymptomatic hands was positively correlated with time to convert from unilaterally to bilaterally affected states (r = 0.62, P = 0.031). Together, these findings suggest that PPT can serve as a useful auxiliary tool in evaluating early PD, and shed light on the neuroplasticity mechanism of fine motor deficit at this very early stage.

ATP11B deficiency leads to impairment of hippocampal synaptic plasticity

Synaptic plasticity is known to regulate and support signal transduction between neurons, while synaptic dysfunction contributes to multiple neurological and other brain disorders; however, the specific mechanism underlying this process remains unclear. In the present study, abnormal neural and dendritic morphology was observed in the hippocampus following knockout of Atp11b both in vitro and in vivo. Moreover, ATP11B modified synaptic ultrastructure and promoted spine remodeling via the asymmetrical distribution of phosphatidylserine and enhancement of glutamate release, glutamate receptor expression, and intracellular Ca2+ concentration. Furthermore, experimental results also indicate that ATP11B regulated synaptic plasticity in hippocampal neurons through the MAPK14 signaling pathway. In conclusion, our data shed light on the possible mechanisms underlying the regulation of synaptic plasticity and lay the foundation for the exploration of proteins involved in signal transduction during this process.

Learning the payoffs and costs of actions

A set of sub-cortical nuclei called basal ganglia is critical for learning the values of actions. The basal ganglia include two pathways, which have been associated with approach and avoid behavior respectively and are differentially modulated by dopamine projections from the midbrain. Inspired by the influential opponent actor learning model, we demonstrate that, under certain circumstances, these pathways may represent learned estimates of the positive and negative consequences (payoffs and costs) of individual actions. In the model, the level of dopamine activity encodes the motivational state and controls to what extent payoffs and costs enter the overall evaluation of actions. We show that a set of previously proposed plasticity rules is suitable to extract payoffs and costs from a prediction error signal if they occur at different moments in time. For those plasticity rules, successful learning requires differential effects of positive and negative outcome prediction errors on the two pathways and a weak decay of synaptic weights over trials. We also confirm through simulations that the model reproduces drug-induced changes of willingness to work, as observed in classical experiments with the D2-antagonist haloperidol.

The basal ganglia are structures underneath the surface of the vertebrate brain, associated with error-driven learning. Much is known about the anatomical and biological features of the basal ganglia; scientists now try to understand the algorithms implemented by these structures. Numerous models aspire to capture the learning functionality, but many of them only cover some specific aspect of the algorithm. Instead of further adding to that pool of partial models, we unify two existing ones—one which captures what the basal ganglia learn, and one that describes the learning mechanism itself. The first model suggests that the basal ganglia weigh positive against negative consequences of actions according to the motivational state. It hints how payoff and cost might be represented, but does not explain how those representations arise. The other model consists of biologically plausible plasticity rules, which describe how learning takes place, but not how the brain makes use of what is learned. We show that the two theories are compatible. Together, they form a model of learning and decision making that integrates the motivational state as well as the learned payoffs and costs of opportunities.

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.

A Computational Model of Dual Competition between the Basal Ganglia and the Cortex

We propose a model that includes interactions between the cortex, the basal ganglia (BG), and the thalamus based on a dual competition. We hypothesize that the striatum, the subthalamic nucleus (STN), the internal globus pallidus (GPi), the thalamus, and the cortex are involved in closed feedback loops through the hyperdirect and direct pathways. These loops support a competition process that results in the ability of BG to make a cognitive decision followed by a motor one. Considering lateral cortical interactions, another competition takes place inside the cortex allowing the latter to make a cognitive and a motor decision. We show how this dual competition endows the model with two regimes. One is driven by reinforcement learning and the other by Hebbian learning. The final decision is made according to a combination of these two mechanisms with a gradual transfer from the former to the latter. We confirmed these theoretical results on primates (Macaca mulatta) using a novel paradigm predicted by the model.

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.

On the Role of Cortex-Basal Ganglia Interactions for Category Learning: A Neurocomputational Approach

In addition to the prefrontal cortex (PFC), the basal ganglia (BG) have been increasingly often reported to play a fundamental role in category learning, but the circuit mechanisms mediating their interaction remain to be explored. We developed a novel neurocomputational model of category learning that particularly addresses the BG-PFC interplay. We propose that the BG bias PFC activity by removing the inhibition of cortico-thalamo-cortical loop and thereby provide a teaching signal to guide the acquisition of category representations in the corticocortical associations to the PFC. Our model replicates key behavioral and physiological data of macaque monkey learning a prototype distortion task from Antzoulatos and Miller (2011) Our simulations allowed us to gain a deeper insight into the observed drop of category selectivity in striatal neurons seen in the experimental data and in the model. The simulation results and a new analysis of the experimental data based on the model's predictions show that the drop in category selectivity of the striatum emerges as the variability of responses in the striatum rises when confronting the BG with an increasingly larger number of stimuli to be classified. The neurocomputational model therefore provides new testable insights of systems-level brain circuits involved in category learning that may also be generalized to better understand other cortico-BG-cortical loops.SIGNIFICANCE STATEMENT Inspired by the idea that basal ganglia (BG) teach the prefrontal cortex (PFC) to acquire category representations, we developed a novel neurocomputational model and tested it on a task that was recently applied in monkey experiments. As an advantage over previous models of category learning, our model allows to compare simulation data with single-cell recordings in PFC and BG. We not only derived model predictions, but already verified a prediction to explain the observed drop in striatal category selectivity. When testing our model with a simple, real-world face categorization task, we observed that the fast striatal learning with a performance of 85% correct responses can teach the slower PFC learning to push the model performance up to almost 100%.

Top-Down Disconnectivity in Schizophrenia During P300 Tasks

Cognitive deficits in schizophrenia are correlated with the dysfunctions of distinct brain regions including anterior cingulate cortex (ACC) and prefrontal cortex (PFC). Apart from the dysfunctions of the intrinsic connectivity of related areas, how the coupled neural populations work is also crucial in related processes. Twenty-four patients with schizophrenia (SZs) and 24 matched healthy controls (HCs) were recruited in our study. Based on the electroencephalogram (EEG) datasets recorded, the Dynamic Causal Modeling (DCM) was then adopted to estimate how the brain architecture adapts among related areas in SZs and to investigate the mechanism that accounts for their cognitive deficits. The distinct winning models in SZs and HCs consistently emphasized the importance of ACC in regulating the elicitations of P300s. Specifically, comparing to that in HCs, the winning model in SZs uncovered a compensatory pathway from dorsolateral PFC to intraparietal sulcus that promised the SZs' accomplishing P300 tasks. The findings demonstrated that the “disconnectivity hypothesis” is helpful and useful in explaining the cognitive deficits in SZs, while the brain architecture adapted with related compensatory pathway promises the limited brain cognitions in SZs. This study provides a new viewpoint that deepens our understanding of the cognitive deficits in schizophrenia.

Propagation of beta/gamma rhythms in the cortico-basal ganglia circuits of the parkinsonian rat

Much of the motor impairment associated with Parkinson’s disease is thought to arise from pathological activity in the networks formed by the basal ganglia (BG) and motor cortex. To evaluate several hypotheses proposed to explain the emergence of pathological oscillations in parkinsonism, we investigated changes to the directed connectivity in BG networks following dopamine depletion. We recorded local field potentials (LFPs) in the cortex and basal ganglia of rats rendered parkinsonian by injection of 6-hydroxydopamine (6-OHDA) and in dopamine-intact controls. We performed systematic analyses of the networks using a novel tool for estimation of directed interactions (nonparametric directionality, NPD). We used a “conditioned” version of the NPD analysis that reveals the dependence of the correlation between two signals on a third reference signal. We find evidence of the dopamine dependency of both low-beta (14–20 Hz) and high-beta/low-gamma (20–40 Hz) directed network interactions. Notably, 6-OHDA lesions were associated with enhancement of the cortical “hyperdirect” connection to the subthalamic nucleus (STN) and its feedback to the cortex and striatum. We find that pathological beta synchronization resulting from 6-OHDA lesioning is widely distributed across the network and cannot be located to any individual structure. Furthermore, we provide evidence that high-beta/gamma oscillations propagate through the striatum in a pathway that is independent of STN. Rhythms at high beta/gamma show susceptibility to conditioning that indicates a hierarchical organization compared with those at low beta. These results further inform our understanding of the substrates for pathological rhythms in salient brain networks in parkinsonism.

NEW & NOTEWORTHY We present a novel analysis of electrophysiological recordings in the cortico-basal ganglia network with the aim of evaluating several hypotheses concerning the origins of abnormal brain rhythms associated with Parkinson’s disease. We present evidence for changes in the directed connections within the network following chronic dopamine depletion in rodents. These findings speak to the plausibility of a “short-circuiting” of the network that gives rise to the conditions from which pathological synchronization may arise.

Decision making under uncertainty in a spiking neural network model of the basal ganglia

The mechanisms of decision-making and action selection are generally thought to be under the control of parallel cortico-subcortical loops connecting back to distinct areas of cortex through the basal ganglia and processing motor, cognitive and limbic modalities of decision-making. We have used these properties to develop and extend a connectionist model at a spiking neuron level based on a previous rate model approach. This model is demonstrated on decision-making tasks that have been studied in primates and the electrophysiology interpreted to show that the decision is made in two steps. To model this, we have used two parallel loops, each of which performs decision-making based on interactions between positive and negative feedback pathways. This model is able to perform two-level decision-making as in primates. We show here that, before learning, synaptic noise is sufficient to drive the decision-making process and that, after learning, the decision is based on the choice that has proven most likely to be rewarded. The model is then submitted to lesion tests, reversal learning and extinction protocols. We show that, under these conditions, it behaves in a consistent manner and provides predictions in accordance with observed experimental data.

Insulin resistance and Parkinson's disease: A new target for disease modification?

There is growing evidence that patients with Type 2 diabetes have an increased risk of developing Parkinson's disease and share similar dysregulated pathways suggesting common underlying pathological mechanisms. Historically insulin was thought solely to be a peripherally acting hormone responsible for glucose homeostasis and energy metabolism. However accumulating evidence indicates insulin can cross the blood-brain-barrier and influence a multitude of processes in the brain including regulating neuronal survival and growth, dopaminergic transmission, maintenance of synapses and pathways involved in cognition. In conjunction, there is growing evidence that a process analogous to peripheral insulin resistance occurs in the brains of Parkinson's disease patients, even in those without diabetes. This raises the possibility that defective insulin signalling pathways may contribute to the development of the pathological features of Parkinson's disease, and thereby suggests that the insulin signalling pathway may potentially be a novel target for disease modification. Given these growing links between PD and Type 2 diabetes it is perhaps not unsurprising that drugs used the treatment of T2DM are amongst the most promising treatments currently being prioritised for repositioning as possible novel treatments for PD and several clinical trials are under way. In this review, we will examine the underlying cellular links between insulin resistance and the pathogenesis of PD and then we will assess current and future pharmacological strategies being developed to restore neuronal insulin signalling as a potential strategy for slowing neurodegeneration in Parkinson's disease.

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

The basal ganglia are a complex neuronal system that is impaired in several movement disorders, including Parkinson's disease, Huntington's disease, and dystonia. Empirical studies have provided valuable insights into the brain dysfunctions underlying these disorders. The systems-level perspective, however, of how patients' motor, cognitive, and emotional impairments originate from known brain dysfunctions has been a challenge to empirical investigations. These causal relations have been analyzed via computational modeling, a method that describes the simulation of interacting brain processes in a computer system. In this article, we review computational insights into the brain dysfunctions underlying Parkinson's disease, Huntington's disease, and dystonia, with particular foci on dysfunctions of the dopamine system, basal ganglia pathways, and neuronal oscillations. © 2016 International Parkinson and Movement Disorder Society.

A Mathematical Model of Levodopa Medication Effect on Basal Ganglia in Parkinson's Disease: An Application to the Alternate Finger Tapping Task

Malfunctions in the neural circuitry of the basal ganglia (BG), induced by alterations in the dopaminergic system, are responsible for an array of motor disorders and milder cognitive issues in Parkinson's disease (PD). Recently Baston and Ursino (2015a) presented a new neuroscience mathematical model aimed at exploring the role of basal ganglia in action selection. The model is biologically inspired and reproduces the main BG structures and pathways, modeling explicitly both the dopaminergic and the cholinergic system. The present work aims at interfacing this neurocomputational model with a compartmental model of levodopa, to propose a general model of medicated Parkinson's disease. Levodopa effect on the striatum was simulated with a two-compartment model of pharmacokinetics in plasma joined with a motor effect compartment. The latter is characterized by the levodopa removal rate and by a sigmoidal relationship (Hill law) between concentration and effect. The main parameters of this relationship are saturation, steepness, and the half-maximum concentration. The effect of levodopa is then summed to a term representing the endogenous dopamine effect, and is used as an external input for the neurocomputation model; this allows both the temporal aspects of medication and the individual patient characteristics to be simulated. The frequency of alternate tapping is then used as the outcome of the whole model, to simulate effective clinical scores. Pharmacokinetic-pharmacodynamic modeling was preliminary performed on data of six patients with Parkinson's disease (both "stable" and "wearing-off" responders) after levodopa standardized oral dosing over 4 h. Results show that the model is able to reproduce the temporal profiles of levodopa in plasma and the finger tapping frequency in all patients, discriminating between different patterns of levodopa motor response. The more influential parameters are the Hill coefficient, related with the slope of the effect sigmoidal relationship, the drug concentration at half-maximum effect, and the drug removal rate from the effect compartment. The model can be of value to gain a deeper understanding on the pharmacokinetics and pharmacodynamics of the medication, and on the way dopamine is exploited in the neural circuitry of the basal ganglia in patients at different stages of the disease progression.

Neuroplasticity and Repair in Rodent Neurotoxic Models of Spinal Motoneuron Disease

Retrogradely transported toxins are widely used to set up protocols for selective lesioning of the nervous system. These methods could be collectively named "molecular neurosurgery" because they are able to destroy specific types of neurons by using targeted neurotoxins. Lectins such as ricin, volkensin, or modeccin and neuropeptide- or antibody-conjugated saporin represent the most effective toxins used for neuronal lesioning. Some of these specific neurotoxins could be used to induce selective depletion of spinal motoneurons. In this review, we extensively describe two rodent models of motoneuron degeneration induced by volkensin or cholera toxin-B saporin. In particular, we focus on the possible experimental use of these models to mimic neurodegenerative diseases, to dissect the molecular mechanisms of neuroplastic changes underlying the spontaneous functional recovery after motoneuron death, and finally to test different strategies of neural repair. The potential clinical applications of these approaches are also discussed.

A Biologically Inspired Computational Model of Basal Ganglia in Action Selection

The basal ganglia (BG) are a subcortical structure implicated in action selection. The aim of this work is to present a new cognitive neuroscience model of the BG, which aspires to represent a parsimonious balance between simplicity and completeness. The model includes the 3 main pathways operating in the BG circuitry, that is, the direct (Go), indirect (NoGo), and hyperdirect pathways. The main original aspects, compared with previous models, are the use of a two-term Hebb rule to train synapses in the striatum, based exclusively on neuronal activity changes caused by dopamine peaks or dips, and the role of the cholinergic interneurons (affected by dopamine themselves) during learning. Some examples are displayed, concerning a few paradigmatic cases: action selection in basal conditions, action selection in the presence of a strong conflict (where the role of the hyperdirect pathway emerges), synapse changes induced by phasic dopamine, and learning new actions based on a previous history of rewards and punishments. Finally, some simulations show model working in conditions of altered dopamine levels, to illustrate pathological cases (dopamine depletion in parkinsonian subjects or dopamine hypermedication). Due to its parsimonious approach, the model may represent a straightforward tool to analyze BG functionality in behavioral experiments.

Differential degradation of motor deficits during gradual dopamine depletion with 6-hydroxydopamine in mice

Parkinson's disease (PD) is a movement disorder whose cardinal motor symptoms arise due to the progressive loss of dopamine. Although this dopamine loss typically progresses slowly over time, currently there are very few animal models that enable incremental dopamine depletion over time within the same animal. This type of gradual dopamine depletion model would be useful in studies aimed at the prodromal phase of PD, when dopamine levels are pathologically low but motor symptoms have not yet presented. Utilizing the highly characterized neurotoxin 6-hydroxydopamine (6-OHDA), we have developed a paradigm to gradually deplete dopamine levels in the striatum over a user-defined time course - spanning weeks to months - in C57BL/6 mice. Dopamine depletions were achieved by administration of five low-dose injections (0.75μg) of 6-OHDA through an implanted intracranial bilateral cannula targeting the medial forebrain bundle. Levels of dopamine within the striatum declined linearly with successive injections, quantified using tyrosine hydroxylase immunostaining and high-performance liquid chromatography. Behavioral testing was carried out at each time point to study the onset and progression of motor impairments as a function of dopamine loss over time. We found that spontaneous locomotion, measured in an open field, was robust until ∼70% of striatal dopamine was lost. Beyond this point, additional dopamine loss caused a sharp decline in motor performance, reaching a final level comparable to that of acutely depleted mice. Similarly, although rearing behavior was more sensitive to dopamine loss and declined linearly as a function of dopamine levels, it eventually declined to levels similar to those seen in acutely depleted mice. In contrast, motor coordination, measured on a vertical pole task, was only moderately impaired in gradually depleted mice, despite severe impairments observed in acutely depleted mice. These results demonstrate the importance of the temporal profile of dopamine loss on the magnitude and progression of behavioral impairments. Our gradual depletion model thus establishes a new paradigm with which to study how circuits respond and adapt to dopamine loss over time, information which could uncover important cellular events during the prodromal phase of PD that ultimately impact the presentation or treatability of behavioral symptoms.

ANNarchy: a code generation approach to neural simulations on parallel hardware

Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions.

Differential contributions of the globus pallidus and ventral thalamus to stimulus-response learning in humans

The ability to learn associations between stimuli, responses and rewards is a prerequisite for survival. Models of reinforcement learning suggest that the striatum, a basal ganglia input nucleus, vitally contributes to these learning processes. Our recently presented computational model predicts, first, that not only the striatum, but also the globus pallidus contributes to the learning (i.e., exploration) of stimulus-response associations based on rewards. Secondly, it predicts that the stable execution (i.e., exploitation) of well-learned associations involves further learning in the thalamus. To test these predictions, we postoperatively recorded local field potentials (LFPs) from patients that had undergone surgery for deep brain stimulation to treat severe movement disorders. Macroelectrodes were placed either in the globus pallidus or in the ventral thalamus. During recordings, patients performed a reward-based stimulus-response learning task that comprised periods of exploration and exploitation. We analyzed correlations between patients' LFP amplitudes and model-based estimates of their reward expectations and reward prediction errors. In line with our first prediction, pallidal LFP amplitudes during the presentation of rewards and reward omissions correlated with patients' reward prediction errors, suggesting pallidal access to reward-based teaching signals. Unexpectedly, the same was true for the thalamus. In further support of this prediction, pallidal LFP amplitudes during stimulus presentation correlated with patients' reward expectations during phases of low reward certainty - suggesting pallidal participation in the learning of stimulus-response associations. In line with our second prediction, correlations between thalamic stimulus-related LFP amplitudes and patients' reward expectations were significant within phases of already high reward certainty, suggesting thalamic participation in exploitation.

Computing reward-prediction error: an integrated account of cortical timing and basal-ganglia pathways for appetitive and aversive learning

There are two prevailing notions regarding the involvement of the corticobasal ganglia system in value‐based learning: (i) the direct and indirect pathways of the basal ganglia are crucial for appetitive and aversive learning, respectively, and (ii) the activity of midbrain dopamine neurons represents reward‐prediction error. Although (ii) constitutes a critical assumption of (i), it remains elusive how (ii) holds given (i), with the basal‐ganglia influence on the dopamine neurons. Here we present a computational neural‐circuit model that potentially resolves this issue. Based on the latest analyses of the heterogeneous corticostriatal neurons and connections, our model posits that the direct and indirect pathways, respectively, represent the values of upcoming and previous actions, and up‐regulate and down‐regulate the dopamine neurons via the basal‐ganglia output nuclei. This explains how the difference between the upcoming and previous values, which constitutes the core of reward‐prediction error, is calculated. Simultaneously, it predicts that blockade of the direct/indirect pathway causes a negative/positive shift of reward‐prediction error and thereby impairs learning from positive/negative error, i.e. appetitive/aversive learning. Through simulation of reward‐reversal learning and punishment‐avoidance learning, we show that our model could indeed account for the experimentally observed features that are suggested to support notion (i) and could also provide predictions on neural activity. We also present a behavioral prediction of our model, through simulation of inter‐temporal choice, on how the balance between the two pathways relates to the subject's time preference. These results indicate that our model, incorporating the heterogeneity of the cortical influence on the basal ganglia, is expected to provide a closed‐circuit mechanistic understanding of appetitive/aversive learning.

Improving desynchronization of Parkinsonian neuronal network via triplet-structure coordinated reset stimulation

We investigate how the triplet-structure coordinated reset stimulations (CRS), which acts on the GPe, STN and GPi within the basal ganglia-thalamocortical motor circuit, can destabilize the strong synchronous state and improve the reliability of thalamic relay in the parkinsonian network. It is shown that compared with the permanent (1:0 ON-OFF) CRS or the classic deep brain stimulation paradigm, the periodic m:n ON-OFF CRS (i.e., m ON-cycles stimulation followed by n OFF-cycles stimulation) can significantly desynchronize the neuronal network of Parkinson's disease, and evidently improve the fidelity of thalamic relay. In addition, the CRS-induced desynchronization can be greatly enhanced when the STN subpopulation within the pathologic network is subjected to the synaptic plasticity. Furthermore, the desynchronization and reliability can also be further improved as the closed-loop CRS strategy is introduced. The obtained results can be helpful for us to understand the pathophysiology mechanism of Parkinson's disease, even though the feasibility of CRS still needs to be explored in clinic.

Computational models of basal-ganglia pathway functions: focus on functional neuroanatomy

Over the past 15 years, computational models have had a considerable impact on basal-ganglia research. Most of these models implement multiple distinct basal-ganglia pathways and assume them to fulfill different functions. As there is now a multitude of different models, it has become complex to keep track of their various, sometimes just marginally different assumptions on pathway functions. Moreover, it has become a challenge to oversee to what extent individual assumptions are corroborated or challenged by empirical data. Focusing on computational, but also considering non-computational models, we review influential concepts of pathway functions and show to what extent they are compatible with or contradict each other. Moreover, we outline how empirical evidence favors or challenges specific model assumptions and propose experiments that allow testing assumptions against each other.