Aberrant learning in Parkinson's disease: A neurocomputational study on bradykinesia.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a progressive decline in motor functions, such as bradykinesia, caused by the pathological denervation of nigrostriatal dopaminergic neurons within the basal ganglia (BG). It is acknowledged that dopamine (DA) directly affects the modulatory role of BG towards the cortex. However, a growing body of literature is suggesting that DA-induced aberrant synaptic plasticity could play a role in the core symptoms of PD, thus recalling for a "reconceptualization" of the pathophysiology. The aim of this work was to investigate DA-driven aberrant learning as a concurrent cause of bradykinesia, using a comprehensive, biologically inspired neurocomputational model of action selection in the BG. The model includes the three main pathways operating in the BG circuitry, that is the direct, indirect and hyperdirect pathways, and use a two-term Hebb rule to train synapses in the striatum, based on previous history of rewards and punishments. Levodopa pharmacodynamics is also incorporated. Through model simulations of the Alternate Finger Tapping motor task, we assessed the role of aberrant learning on bradykinesia. The results show that training under drug medication (levodopa) provides not only immediate but also delayed benefit lasting in time. Conversely, if performed in conditions of vanishing levodopa efficacy, training may result in dysfunctional corticostriatal synaptic plasticity, further worsening motor performances in PD subjects. This suggests that bradykinesia may result from the concurrent effects of low DA levels and dysfunctional plasticity and that training can be exploited in medicated subjects to improve levodopa treatment.

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.

Effects of Levodopa on Postural Strategies in Parkinson's disease.

Altered postural control and balance are major disabling issues of Parkinson's disease (PD). Static and dynamic posturography have provided insight into PD's postural deficits; however, little is known about impairments in postural coordination. We hypothesized that subjects with PD would show more ankle strategy during quiet stance than healthy control subjects, who would include some hip strategy, and this stiffer postural strategy would increase with disease progression. We quantified postural strategy and sway dispersion with inertial sensors (one placed on the shank and one on the posterior trunk at L5 level) while subjects were standing still with their eyes open. A total of 70 subjects with PD, including a mild group (H&Y≤2, N=33) and a more severe group (H&Y≥3, N=37), were assessed while OFF and while ON levodopa medication. We also included a healthy control group (N=21). Results showed an overall preference of ankle strategy in all groups while maintaining balance. Postural strategy was significantly lower ON compared to OFF medication (indicating more hip strategy), but no effect of disease stage was found. Instead, sway dispersion was significantly larger in ON compared to OFF medication, and significantly larger in the more severe PD group compared to the mild. In addition, increased hip strategy during stance was associated with poorer self-perception of balance.

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.

A computational model of Dopamine and Acetylcholine aberrant learning in Basal Ganglia.

Basal Ganglia (BG) are implied in many motor and cognitive tasks, such as action selection, and have a central role in many pathologies, primarily Parkinson Disease. In the present work, we use a recently developed biologically inspired BG model to analyze how the dopamine (DA) level can affect the temporal response during action selection, and the capacity to learn new actions following rewards and punishments. The model incorporates the 3 main pathways (direct, indirect and hyperdirect) working in BG functioning. The behavior of 2 alternative networks (the first with normal DA levels, the second with reduced DA) is analyzed both in untrained conditions, and during training performed in different epochs. The results show that reduced DA causes delayed temporal responses in the untrained network, and difficult of learning during training, characterized by the necessity of much more epochs. The results provide interesting hints to understand the behavior of healthy and dopamine depleted subjects, such as parkinsonian patients.

Postural strategies assessed with inertial sensors in healthy and parkinsonian subjects.

UNLABELLED: The present study introduces a novel instrumented method to characterize postural movement strategies to maintain balance during stance (ankle and hip strategy), by means of inertial sensors, positioned on the legs and on the trunk. We evaluated postural strategies in subjects with 2 types of Parkinsonism: idiopathic Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP), and in age-matched control subjects standing under perturbed conditions implemented by the Sensory Organization Test (SOT). Coordination between the upper and lower segments of the body during postural sway was measured using a covariance index over time, by a sliding-window algorithm. Afterwards, a postural strategy index was computed. We also measured the amount of postural sway, as adjunctive information to characterize balance, by the root mean square of the horizontal trunk acceleration signal (RMS). RESULTS: showed that control subjects were able to change their postural strategy, whilst PSP and PD subjects persisted in use of an ankle strategy in all conditions. PD subjects had RMS values similar to control subjects even without changing postural strategy appropriately, whereas PSP subjects showed much larger RMS values than controls, resulting in several falls during the most challenging SOT conditions (5 and 6). Results are in accordance with the corresponding clinical literature describing postural behavior in the same kind of subjects. The proposed strategy index, based on the use of inertial sensors on the upper and lower body segments, is a promising and unobtrusive tool to characterize postural strategies performed to attain balance.