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

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

Integration of Mao-B Inhibitor Rasagiline into Computational Model of Levodopa for the Treatment of Parkinson\u27s Disease

Parkinson’s Disease (PD) is the second most common neurodegenerative disorder in the world after Alzheimer’s. The hallmark symptoms of PD are tremor and rigidity, which are caused by the death of dopaminergic neurons, specifically within the substantia nigra of the basal ganglia. These symptoms are often treated by Levodopa (L-DOPA), MAO-B inhibitors, and other pharmaceuticals with the goal of increasing the dopamine concentration in the brain. To better understand how L-DOPA impacts the brain’s dopamine dynamics, various computational models have been developed. One model, by Véronneau-Veilleux et al. (Chaos 30, 093146, 2020), integrates L-DOPA pharmacokinetics, dopamine dynamics, and a neurocomputational model of the basal ganglia to predict the impact of L-DOPA regimens on a patient\u27s motor function. In this study, we extended the model to investigate an adjunct therapy of L-DOPA with the MAO-B inhibitor Rasagiline utilizing an enzyme inhibition model, which showed a 1.67% increase of dopamine concentration in the brain when compared to L-DOPA therapy alone. Our model provides a foundation for optimizing treatment strategies using both L-DOPA and an adjunct

Rule-Based Categorization Deficits in Focal Basal Ganglia Lesion and Parkinson’s Disease Patients

Patients with basal ganglia (BG) pathology are consistently found to be impaired on rule-based category learning tasks in which learning is thought to depend upon the use of an explicit, hypothesis-guided strategy. The factors that influence this impairment remain unclear. Moreover, it remains unknown if the impairments observed in patients with degenerative disorders such as Parkinson’s disease (PD) are also observed in those with focal BG lesions. In the present study, we tested patients with either focal BG lesions or PD on two categorization tasks that varied in terms of their demands on selective attention and working memory. Individuals with focal BG lesions were impaired on the task in which working memory demand was high and performed similarly to healthy controls on the task in which selective-attention demand was high. In contrast, individuals with PD were impaired on both tasks, and accuracy rates did not differ between on and off medication states for a subset of patients who were also tested after abstaining from dopaminergic medication. Quantitative, model-based analyses attributed the performance deficit for both groups in the task with high working memory demand to the utilization of suboptimal strategies, whereas the PD-specific impairment on the task with high selective-attention demand was driven by the inconsistent use of an optimal strategy. These data suggest that the demands on selective attention and working memory affect the presence of impairment in patients with focal BG lesions and the nature of the impairment in patients with PD

A mechanistic model of ADHD as resulting from dopamine phasic/tonic imbalance during reinforcement learning

Attention deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in children. Although the involvement of dopamine in this disorder seems to be established, the nature of dopaminergic dysfunction remains controversial. The purpose of this study was to test whether the key response characteristics of ADHD could be simulated by a mechanistic model that combines a decrease in tonic dopaminergic activity with an increase in phasic responses in cortical-striatal loops during learning reinforcement. To this end, we combined a dynamic model of dopamine with a neurocomputational model of the basal ganglia with multiple action channels. We also included a dynamic model of tonic and phasic dopamine release and control, and a learning procedure driven by tonic and phasic dopamine levels. In the model, the dopamine imbalance is the result of impaired presynaptic regulation of dopamine at the terminal level. Using this model, virtual individuals from a dopamine imbalance group and a control group were trained to associate four stimuli with four actions with fully informative reinforcement feedback. In a second phase, they were tested without feedback. Subjects in the dopamine imbalance group showed poorer performance with more variable reaction times due to the presence of fast and very slow responses, difficulty in choosing between stimuli even when they were of high intensity, and greater sensitivity to noise. Learning history was also significantly more variable in the dopamine imbalance group, explaining 75% of the variability in reaction time using quadratic regression. The response profile of the virtual subjects varied as a function of the learning history variability index to produce increasingly severe impairment, beginning with an increase in response variability alone, then accumulating a decrease in performance and finally a learning deficit. Although ADHD is certainly a heterogeneous disorder, these results suggest that typical features of ADHD can be explained by a phasic/tonic imbalance in dopaminergic activity alone

Deep Brain Stimulation of the Subthalamic Nucleus Improves Reward-Based Decision-Learning in Parkinson's Disease

Recently, the subthalamic nucleus (STN) has been shown to be critically involved in decision-making, action selection, and motor control. Here we investigate the effect of deep brain stimulation (DBS) of the STN on reward-based decision-learning in patients diagnosed with Parkinson's disease (PD). We determined computational measures of outcome evaluation and reward prediction from PD patients who performed a probabilistic reward-based decision-learning task. In previous work, these measures covaried with activation in the nucleus caudatus (outcome evaluation during the early phases of learning) and the putamen (reward prediction during later phases of learning). We observed that stimulation of the STN motor regions in PD patients served to improve reward-based decision-learning, probably through its effect on activity in frontostriatal motor loops (prominently involving the putamen and, hence, reward prediction). In a subset of relatively younger patients with relatively shorter disease duration, the effects of DBS appeared to spread to more cognitive regions of the STN, benefiting loops that connect the caudate to various prefrontal areas importantfor outcome evaluation. These results highlight positive effects of STN stimulation on cognitive functions that may benefit PD patients in daily-life association-learning situations

La bradicinesia nei soggetti parkinsoniani attraverso i modelli neurali

Un’alterazione del sistema dopaminergico induce il malfunzionamento del circuito neuronale dei gangli della base (BG), determinando malattie cognitive quali il Parkinson (PD). Uno dei principali deficit indotti dal Parkinson è la bradicinesia, ovvero il progressivo rallentamento delle funzioni motorie. Attraverso l’utilizzo di un modello neurocomputazionale si è cercato di riprodurre il circuito neuronale dei gangli della base e il ruolo della dopamina (DA) al suo interno. Il modello comprende tre vie principali (diretta, indiretta e iperdiretta) e l’uso della regola di Hebb per simulare l’attività sinaptica nello striato. Il peso sinaptico nel modello dipende dall’apprendimento, caratterizzato da punizioni e ricompense. Si mostrerà l’effetto benefico della levodopa basandoci, soprattutto, sulle performance del ‘finger tapping task’. Infatti i pazienti addestrati sotto questo trattamento, mostrano la capacità di migliorare le prestazioni motorie; viceversa, in condizioni in cui viene attenuata la somministrazione di levodopa, i soggetti in esame sono inclini ad una plasticità sinaptica corticostriatale disfunzionale che causa un peggioramento delle prestazioni motorie. Tutti questi fattori, suggeriscono che la bradicinesia dipenda non soltanto da bassi valori di dopamina ma anche da una disfunzione della plasticità sinaptica, e che è possibile sfruttare l’apprendimento sinaptico dei soggetti sotto cura per migliorare il trattamento attraverso la levodopa

A mechanistic model of ADHD as resulting from dopamine phasic/tonic imbalance during reinforcement learning

Attention deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in children. Although the involvement of dopamine in this disorder seems to be established, the nature of dopaminergic dysfunction remains controversial. The purpose of this study was to test whether the key response characteristics of ADHD could be simulated by a mechanistic model that combines a decrease in tonic dopaminergic activity with an increase in phasic responses in cortical-striatal loops during learning reinforcement. To this end, we combined a dynamic model of dopamine with a neurocomputational model of the basal ganglia with multiple action channels. We also included a dynamic model of tonic and phasic dopamine release and control, and a learning procedure driven by tonic and phasic dopamine levels. In the model, the dopamine imbalance is the result of impaired presynaptic regulation of dopamine at the terminal level. Using this model, virtual individuals from a dopamine imbalance group and a control group were trained to associate four stimuli with four actions with fully informative reinforcement feedback. In a second phase, they were tested without feedback. Subjects in the dopamine imbalance group showed poorer performance with more variable reaction times due to the presence of fast and very slow responses, difficulty in choosing between stimuli even when they were of high intensity, and greater sensitivity to noise. Learning history was also significantly more variable in the dopamine imbalance group, explaining 75% of the variability in reaction time using quadratic regression. The response profile of the virtual subjects varied as a function of the learning history variability index to produce increasingly severe impairment, beginning with an increase in response variability alone, then accumulating a decrease in performance and finally a learning deficit. Although ADHD is certainly a heterogeneous disorder, these results suggest that typical features of ADHD can be explained by a phasic/tonic imbalance in dopaminergic activity alone

A computational approach to motivated behaviour and apathy

The loss of motivation and goal-directed behaviour is characteristic of apathy. Across a wide range of neuropsychiatric disorders, including Huntington’s disease (HD), apathy is poorly understood, associated with significant morbidity, and is hard to treat. One of the challenges in understanding the neural basis of apathy is moving from phenomenology and behavioural dysfunction to neural circuits in a principled manner. The computational framework offers one such approach. I adopt this framework to better understand motivated behaviour and apathy in four complementary projects. At the heart of many apathy formulations is impaired self-initiation of goal-directed behaviour. An influential computational theory proposes that “opportunity cost”, the amount of reward we stand to lose by not taking actions per unit time, is a key variable in governing the timing of self-initiated behaviour. Using a novel task, I found that free-operant behaviour in healthy participants both in laboratory conditions and in online testing, conforms to predictions of this computational model. Furthermore, in both studies I found that in younger adults sensitivity to opportunity cost predicted behavioural apathy scores. Similar pilot results were found in a cohort of patients with HD. These data suggest that opportunity cost may be an important computational variable relevant for understanding a core feature of apathy – the timing of self-initiated behaviour. In my second project, I used a reinforcement learning paradigm to probe for early dysfunction in a cohort of HD gene carriers approximately 25 years from clinical onset. Based on empirical data and computational models of basal ganglia function I predicted that asymmetry in learning from gains and losses may be an early feature of carrying the HD gene. As predicted, in this task fMRI study, HD gene carriers demonstrated an exaggerated neural response to gains as compared to losses. Gene carriers also differed in the neural response to expected value suggesting that carrying the HD gene is associated with altered processing of valence and value decades from onset. Finally, based on neurocomputational models of basal ganglia pathway function, I tested the hypothesis that apathy in HD would be associated with the involvement of the direct pathway. Support for this hypothesis was found in two related projects. Firstly, using data from a large international HD cohort study, I found that apathy was associated with motor features of the disease thought to represent direct pathway involvement. Secondly, I tested this hypothesis in vivo using resting state fMRI data and a model of basal ganglia connectivity in a large peri-manifest HD cohort. In keeping with my predictions, whilst emerging motor signs were associated with changes in the indirect pathway, apathy scores were associated with connectivity changes in the direct pathway connectivity within my model. For patients with apathy across neuropsychiatry there is an urgent need to understand the neural basis of motivated behaviour in order to develop novel therapies. In this thesis, I have used a computational framework to develop and test a range of hypotheses to advance this understanding. In particular, I have focussed on the computational factors which drive us to self-initiate, their potential neural underpinnings and the relevance of these models for apathy in patients with HD. The data I present supports the hypothesis that opportunity cost and basal ganglia pathway connectivity may be two important components necessary to generate motivated behaviour and contribute to the development of apathy in HD