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Lima MMS, Targa ADS, Dos Santos Lima GZ, Cavarsan CF, Torterolo P. Macro and micro-sleep dysfunctions as translational biomarkers for Parkinson's disease. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2023; 174:187-209. [PMID: 38341229 DOI: 10.1016/bs.irn.2023.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2024]
Abstract
Sleep disturbances are highly prevalent among patients with Parkinson's disease (PD) and often appear from the early-phase disease or prodromal stages. In this chapter, we will discuss the current evidence addressing the links between sleep dysfunctions in PD, focusing most closely on those data from animal and mathematical/computational models, as well as in human-based studies that explore the electrophysiological and molecular mechanisms by which PD and sleep may be intertwined, whether as predictors or consequences of the disease. It is possible to clearly state that leucine-rich repeat kinase 2 gene (LRRK2) is significantly related to alterations in sleep architecture, particularly affecting rapid eye movement (REM) sleep and non-REM sleep, thus impacting sleep quality. Also, decreases in gamma power, observed after dopaminergic lesions, correlates negatively with the degree of injury, which brings other levels of understanding the impacts of the disease. Besides, abnormal synchronized oscillations among basal ganglia nuclei can be detrimental for information processing considering both motor and sleep-related processes. Altogether, despite clear advances in the field, it is still difficult to definitely establish a comprehensive understanding of causality among all the sleep dysfunctions with the disease itself. Although, certainly, the search for biomarkers is helping in shortening this road towards a better and faster diagnosis, as well as looking for more efficient treatments.
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Affiliation(s)
- Marcelo M S Lima
- Neurophysiology Laboratory, Department of Physiology, Federal University of Paraná, Curitiba, Paraná, Brazil.
| | - Adriano D S Targa
- CIBER of Respiratory diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova-Santa Maria, Biomedical Research Institute of Lleida (IRBLleida), Lleida, Spain
| | - Gustavo Z Dos Santos Lima
- Science and Technology School, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Clarissa F Cavarsan
- College of Pharmacy, University of Rhode Island, Kingston, RI, United States
| | - Pablo Torterolo
- Laboratory of Sleep Neurobiology, Department of Physiology, School of Medicine, Universidad de la República, Montevideo, Uruguay
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Wyszogrodzka-Gaweł G, Shuklinova O, Lisowski B, Wiśniowska B, Polak S. 3D printing combined with biopredictive dissolution and PBPK/PD modeling optimization and personalization of pharmacotherapy: Are we there yet? Drug Discov Today 2023; 28:103731. [PMID: 37541422 DOI: 10.1016/j.drudis.2023.103731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/25/2023] [Accepted: 07/28/2023] [Indexed: 08/06/2023]
Abstract
Precision medicine requires selecting the appropriate dosage regimen for a patient using the right drug, at the right time. Model-Informed Precision Dosing (MIPD) is a concept suggesting utilization of model-based prediction methods for optimizing the treatment benefit-harm balance, based on individual characteristics of the patient, disease, treatment method, and other factors. Here, we discuss a theoretical workflow comprising several elements, beginning from the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, through 3D printed tablets with the model proposed dose, information range and flow, and the patient themselves. We also describe each of these elements, and the connection between them, highlighting challenges and potential obstacles.
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Affiliation(s)
- Gabriela Wyszogrodzka-Gaweł
- Department of Social Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9, 30-688 Kraków, Poland.
| | - Olha Shuklinova
- Chair of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy. Jagiellonian University Medical College, Medyczna 9, 30-688 Kraków, Poland
| | - Bartek Lisowski
- Chair of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy. Jagiellonian University Medical College, Medyczna 9, 30-688 Kraków, Poland.
| | - Barbara Wiśniowska
- Chair of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy. Jagiellonian University Medical College, Medyczna 9, 30-688 Kraków, Poland.
| | - Sebastian Polak
- Chair of Pharmaceutical Technology and Biopharmaceutics, Faculty of Pharmacy. Jagiellonian University Medical College, Medyczna 9, 30-688 Kraków, Poland.
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Liu Y, Zhu R, Zhou Y, Lü J, Chai Y. Improved control effect of pathological oscillations by using delayed feedback stimulation in neural mass model with pedunculopontine nucleus. Brain Behav 2023; 13:e3183. [PMID: 37533306 PMCID: PMC10570496 DOI: 10.1002/brb3.3183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/14/2023] [Accepted: 07/15/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND The role of delayed feedback stimulation in the discussion of Parkinson's disease (PD) has recently received increasing attention. Stimulation of pedunculopontine nucleus (PPN) is an emerging treatment for PD. However, the effect of PPN in regulating PD is ignored, and the delayed feedback stimulation algorithm is facing some problems in parameter selection. METHODS On the basis of a neural mass model, we established a new network for PPN. Four types of delayed feedback stimulation schemes were designed, such as stimulating subthalamic nucleus (STN) with the local field potentials (LFPs) of STN nucleus, globus pallidus (GPe) with the LFPs of Gpe nucleus, PPN with the LFPs of Gpe nucleus, and STN with the LFPs of PPN nucleus. RESULTS In this study, we found that all four kinds of delayed feedback schemes are effective, suggesting that the algorithm is simple and more effective in experiments. More specifically, the other three control schemes improved the control performance and reduced the stimulation energy expenditure compared with traditional stimulating STN itself only. CONCLUSION PPN stimulation can affect the new network and help to suppress pathological oscillations for each neuron. We hope that our results can gain an insight into the future clinical treatment.
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Affiliation(s)
- Yingpeng Liu
- School of Mathematics and PhysicsShanghai University of Electric PowerShanghaiChina
| | - Rui Zhu
- School of Mathematics and PhysicsShanghai University of Electric PowerShanghaiChina
| | - Ye Zhou
- School of Mathematics and PhysicsShanghai University of Electric PowerShanghaiChina
| | - Jiali Lü
- School of Mathematics and PhysicsShanghai University of Electric PowerShanghaiChina
| | - Yuan Chai
- School of Mathematics and PhysicsShanghai University of Electric PowerShanghaiChina
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di Biase L, Pecoraro PM, Carbone SP, Caminiti ML, Di Lazzaro V. Levodopa-Induced Dyskinesias in Parkinson's Disease: An Overview on Pathophysiology, Clinical Manifestations, Therapy Management Strategies and Future Directions. J Clin Med 2023; 12:4427. [PMID: 37445461 DOI: 10.3390/jcm12134427] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 06/18/2023] [Accepted: 06/27/2023] [Indexed: 07/15/2023] Open
Abstract
Since its first introduction, levodopa has become the cornerstone for the treatment of Parkinson's disease and remains the leading therapeutic choice for motor control therapy so far. Unfortunately, the subsequent appearance of abnormal involuntary movements, known as dyskinesias, is a frequent drawback. Despite the deep knowledge of this complication, in terms of clinical phenomenology and the temporal relationship during a levodopa regimen, less is clear about the pathophysiological mechanisms underpinning it. As the disease progresses, specific oscillatory activities of both motor cortical and basal ganglia neurons and variation in levodopa metabolism, in terms of the dopamine receptor stimulation pattern and turnover rate, underlie dyskinesia onset. This review aims to provide a global overview on levodopa-induced dyskinesias, focusing on pathophysiology, clinical manifestations, therapy management strategies and future directions.
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Affiliation(s)
- Lazzaro di Biase
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Brain Innovations Lab, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Rome, Italy
| | - Pasquale Maria Pecoraro
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Simona Paola Carbone
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Maria Letizia Caminiti
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Vincenzo Di Lazzaro
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
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Rose R, Mitchell E, Van Der Graaf P, Takaichi D, Hosogi J, Geerts H. A quantitative systems pharmacology model for simulating OFF-Time in augmentation trials for Parkinson’s disease: application to preladenant. J Pharmacokinet Pharmacodyn 2022; 49:593-606. [DOI: 10.1007/s10928-022-09825-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 09/13/2022] [Indexed: 11/29/2022]
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Véronneau-Veilleux F, Robaey P, Ursino M, Nekka F. A mechanistic model of ADHD as resulting from dopamine phasic/tonic imbalance during reinforcement learning. Front Comput Neurosci 2022; 16:849323. [PMID: 35923915 PMCID: PMC9342605 DOI: 10.3389/fncom.2022.849323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 06/28/2022] [Indexed: 11/25/2022] Open
Abstract
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.
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Affiliation(s)
- Florence Véronneau-Veilleux
- Faculté de Pharmacie, Université de Montréal, Montreal, QC, Canada
- *Correspondence: Florence Véronneau-Veilleux
| | - Philippe Robaey
- Children's Hospital of Eastern Ontario, University of Ottawa, Ottawa, ON, Canada
| | - Mauro Ursino
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi,” University of Bologna, Bologna, Italy
| | - Fahima Nekka
- Faculté de Pharmacie, Université de Montréal, Montreal, QC, Canada
- Centre de Recherches Mathématiques, Université de Montréal, Montreal, QC, Canada
- Centre for Applied Mathematics in Bioscience and Medicine, McGill University, Montreal, QC, Canada
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Nair SS, Muddapu VR, Chakravarthy VS. A Multiscale, Systems-Level, Neuropharmacological Model of Cortico-Basal Ganglia System for Arm Reaching Under Normal, Parkinsonian, and Levodopa Medication Conditions. Front Comput Neurosci 2022; 15:756881. [PMID: 35046787 PMCID: PMC8762321 DOI: 10.3389/fncom.2021.756881] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 11/30/2021] [Indexed: 12/13/2022] Open
Abstract
In order to understand the link between substantia nigra pars compacta (SNc) cell loss and Parkinson's disease (PD) symptoms, we developed a multiscale computational model that can replicate the symptoms at the behavioural level by incorporating the key cellular and molecular mechanisms underlying PD pathology. There is a modelling tradition that links dopamine to reward and uses reinforcement learning (RL) concepts to model the basal ganglia. In our model, we replace the abstract representations of reward with the realistic variable of extracellular DA released by a network of SNc cells and incorporate it in the RL-based behavioural model, which simulates the arm reaching task. Our results successfully replicated the impact of SNc cell loss and levodopa (L-DOPA) medication on reaching performance. It also shows the side effects of medication, such as wearing off and peak dosage dyskinesias. The model demonstrates how differential dopaminergic axonal degeneration in basal ganglia results in various cardinal symptoms of PD. It was able to predict the optimum L-DOPA medication dosage for varying degrees of cell loss. The proposed model has a potential clinical application where drug dosage can be optimised as per patient characteristics.
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Affiliation(s)
- Sandeep Sathyanandan Nair
- Laboratory for Computational Neuroscience, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Vignayanandam Ravindernath Muddapu
- Laboratory for Computational Neuroscience, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - V. Srinivasa Chakravarthy
- Laboratory for Computational Neuroscience, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
- Center for Complex Systems and Dynamics, Indian Institute of Technology Madras, Chennai, India
- *Correspondence: V. Srinivasa Chakravarthy
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di Biase L, Tinkhauser G, Martin Moraud E, Caminiti ML, Pecoraro PM, Di Lazzaro V. Adaptive, personalized closed-loop therapy for Parkinson's disease: biochemical, neurophysiological, and wearable sensing systems. Expert Rev Neurother 2021; 21:1371-1388. [PMID: 34736368 DOI: 10.1080/14737175.2021.2000392] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Motor complication management is one of the main unmet needs in Parkinson's disease patients. AREAS COVERED Among the most promising emerging approaches for handling motor complications in Parkinson's disease, adaptive deep brain stimulation strategies operating in closed-loop have emerged as pivotal to deliver sustained, near-to-physiological inputs to dysfunctional basal ganglia-cortical circuits over time. Existing sensing systems that can provide feedback signals to close the loop include biochemical-, neurophysiological- or wearable-sensors. Biochemical sensing allows to directly monitor the pharmacokinetic and pharmacodynamic of antiparkinsonian drugs and metabolites. Neurophysiological sensing relies on neurotechnologies to sense cortical or subcortical brain activity and extract real-time correlates of symptom intensity or symptom control during DBS. A more direct representation of the symptom state, particularly the phenomenological differentiation and quantification of motor symptoms, can be realized via wearable sensor technology. EXPERT OPINION Biochemical, neurophysiologic, and wearable-based biomarkers are promising technological tools that either individually or in combination could guide adaptive therapy for Parkinson's disease motor symptoms in the future.
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Affiliation(s)
- Lazzaro di Biase
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico Di Roma, Rome, Italy.,Brain Innovations Lab, Università Campus Bio-Medico Di Roma, Rome, Italy
| | - Gerd Tinkhauser
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Eduardo Martin Moraud
- Department of Clinical Neurosciences, Lausanne University Hospital (Chuv) and University of Lausanne (Unil), Lausanne, Switzerland.,Defitech Center for Interventional Neurotherapies (.neurorestore), Lausanne University Hospital and Swiss Federal Institute of Technology (Epfl), Lausanne, Switzerland
| | - Maria Letizia Caminiti
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico Di Roma, Rome, Italy
| | - Pasquale Maria Pecoraro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico Di Roma, Rome, Italy
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico Di Roma, Rome, Italy
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Bélair J, Nekka F, Milton JG. Introduction to Focus Issue: Dynamical disease: A translational approach. CHAOS (WOODBURY, N.Y.) 2021; 31:060401. [PMID: 34241319 DOI: 10.1063/5.0058345] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 05/28/2021] [Indexed: 06/13/2023]
Abstract
The concept of Dynamical Diseases provides a framework to understand physiological control systems in pathological states due to their operating in an abnormal range of control parameters: this allows for the possibility of a return to normal condition by a redress of the values of the governing parameters. The analogy with bifurcations in dynamical systems opens the possibility of mathematically modeling clinical conditions and investigating possible parameter changes that lead to avoidance of their pathological states. Since its introduction, this concept has been applied to a number of physiological systems, most notably cardiac, hematological, and neurological. A quarter century after the inaugural meeting on dynamical diseases held in Mont Tremblant, Québec [Bélair et al., Dynamical Diseases: Mathematical Analysis of Human Illness (American Institute of Physics, Woodbury, NY, 1995)], this Focus Issue offers an opportunity to reflect on the evolution of the field in traditional areas as well as contemporary data-based methods.
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Affiliation(s)
- Jacques Bélair
- Département de Mathématiques et de Statistique, Université de Montréal, Montréal, Québec H3C 3J7, Canada
| | - Fahima Nekka
- Centre de Recherches Mathématiques (CRM), Université de Montréal, Montréal, Québec H3C 3J7, Canada
| | - John G Milton
- W. M. Keck Science Department, The Claremont Colleges, Claremont, California 91711, USA
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Gait speed in clinical and daily living assessments in Parkinson's disease patients: performance versus capacity. NPJ Parkinsons Dis 2021; 7:24. [PMID: 33674597 PMCID: PMC7935857 DOI: 10.1038/s41531-021-00171-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 01/25/2021] [Indexed: 01/31/2023] Open
Abstract
Gait speed often referred as the sixth vital sign is the most powerful biomarker of mobility. While a clinical setting allows the estimation of gait speed under controlled conditions that present functional capacity, gait speed in real-life conditions provides the actual performance of the patient. The goal of this study was to investigate objectively under what conditions during daily activities, patients perform as well as or better than in the clinic. To this end, we recruited 27 Parkinson's disease (PD) patients and measured their gait speed by inertial measurement units through several walking tests in the clinic as well as their daily activities at home. By fitting a bimodal Gaussian model to their gait speed distribution, we found that on average, patients had similar modes in the clinic and during daily activities. Furthermore, we observed that the number of medication doses taken throughout the day had a moderate correlation with the difference between clinic and home. Performing a cycle-by-cycle analysis on gait speed during the home assessment, overall only about 3% of the strides had equal or greater gait speeds than the patients' capacity in the clinic. These strides were during long walking bouts (>1 min) and happened before noon, around 26 min after medication intake, reaching their maximum occurrence probability 3 h after Levodopa intake. These results open the possibility of better control of medication intake in PD by considering both functional capacity and continuous monitoring of gait speed during real-life conditions.
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A dynamical model for the basal ganglia-thalamo-cortical oscillatory activity and its implications in Parkinson's disease. Cogn Neurodyn 2020; 15:693-720. [PMID: 34367369 PMCID: PMC8286922 DOI: 10.1007/s11571-020-09653-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Revised: 10/27/2020] [Accepted: 11/09/2020] [Indexed: 12/27/2022] Open
Abstract
We propose to investigate brain electrophysiological alterations associated with Parkinson’s disease through a novel adaptive dynamical model of the network of the basal ganglia, the cortex and the thalamus. The model uniquely unifies the influence of dopamine in the regulation of the activity of all basal ganglia nuclei, the self-organised neuronal interdependent activity of basal ganglia-thalamo-cortical circuits and the generation of subcortical background oscillations. Variations in the amount of dopamine produced in the neurons of the substantia nigra pars compacta are key both in the onset of Parkinson’s disease and in the basal ganglia action selection. We model these dopamine-induced relationships, and Parkinsonian states are interpreted as spontaneous emergent behaviours associated with different rhythms of oscillatory activity patterns of the basal ganglia-thalamo-cortical network. These results are significant because: (1) the neural populations are built upon single-neuron models that have been robustly designed to have eletrophysiologically-realistic responses, and (2) our model distinctively links changes in the oscillatory activity in subcortical structures, dopamine levels in the basal ganglia and pathological synchronisation neuronal patterns compatible with Parkinsonian states, this still remains an open problem and is crucial to better understand the progression of the disease.
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An integrative model of Parkinson's disease treatment including levodopa pharmacokinetics, dopamine kinetics, basal ganglia neurotransmission and motor action throughout disease progression. J Pharmacokinet Pharmacodyn 2020; 48:133-148. [PMID: 33084988 DOI: 10.1007/s10928-020-09723-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 10/05/2020] [Indexed: 01/08/2023]
Abstract
Levodopa is considered the gold standard treatment of Parkinson's disease. Although very effective in alleviating symptoms at their onset, its chronic use with the progressive neuronal denervation in the basal ganglia leads to a decrease in levodopa's effect duration and to the appearance of motor complications. This evolution challenges the establishment of optimal regimens to manage the symptoms as the disease progresses. Based on up-to-date pathophysiological and pharmacological knowledge, we developed an integrative model for Parkinson's disease to evaluate motor function in response to levodopa treatment as the disease progresses. We combined a pharmacokinetic model of levodopa to a model of dopamine's kinetics and a neurocomputational model of basal ganglia. The parameter values were either measured directly or estimated from human and animal data. The concentrations and behaviors predicted by our model were compared to available information and data. Using this model, we were able to predict levodopa plasma concentration, its related dopamine concentration in the brain and the response performance of a motor task for different stages of disease.
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Véronneau-Veilleux F, Ursino M, Robaey P, Lévesque D, Nekka F. Nonlinear pharmacodynamics of levodopa through Parkinson's disease progression. CHAOS (WOODBURY, N.Y.) 2020; 30:093146. [PMID: 33003902 DOI: 10.1063/5.0014800] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
The effect of levodopa in alleviating the symptoms of Parkinson's disease is altered in a highly nonlinear manner as the disease progresses. This can be attributed to different compensation mechanisms taking place in the basal ganglia where the dopaminergic neurons are progressively lost. This alteration in the effect of levodopa complicates the optimization of a drug regimen. The present work aims at investigating the nonlinear dynamics of Parkinson's disease and its therapy through mechanistic mathematical modeling. Using a holistic approach, a pharmacokinetic model of levodopa was combined to a dopamine dynamics and a neurocomputational model of basal ganglia. The influence of neuronal death on these different mechanisms was also integrated. Using this model, we were able to investigate the nonlinear relationships between the levodopa plasma concentration, the dopamine brain concentration, and a response to a motor task. Variations in dopamine concentrations in the brain for different levodopa doses were also studied. Finally, we investigated the narrowing of a levodopa therapeutic index with the progression of the disease as a result of these nonlinearities. In conclusion, various consequences of nonlinear dynamics in Parkinson's disease treatment were studied by developing an integrative model. This model paves the way toward individualization of a dosing regimen. Using sensor based information, the parameters of the model could be fitted to individual data to propose optimal individual regimens.
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Affiliation(s)
| | - Mauro Ursino
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi," University of Bologna, 40136 Bologna, Italy
| | - Philippe Robaey
- Children's Hospital of Eastern Ontario, University of Ottawa, Ottawa, Ontario K1H 8L1, Canada
| | - Daniel Lévesque
- Faculté de Pharmacie, Université de Montréal, Montréal, Québec H3C 3J7, Canada
| | - Fahima Nekka
- Faculté de Pharmacie, Université de Montréal, Montréal, Québec H3C 3J7, Canada
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Ursino M, Véronneau-Veilleux F, Nekka F. A non-linear deterministic model of action selection in the basal ganglia to simulate motor fluctuations in Parkinson's disease. CHAOS (WOODBURY, N.Y.) 2020; 30:083139. [PMID: 32872807 DOI: 10.1063/5.0013666] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 07/27/2020] [Indexed: 06/11/2023]
Abstract
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.
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Affiliation(s)
- Mauro Ursino
- Department of Electrical, Electronic and Information Engineering Guglielmo Marconi, University of Bologna, I 40136 Bologna, Italy
| | | | - Fahima Nekka
- Faculté de Pharmacie, Université de Montréal, Montréal, Québec H3T 1J4, Canada
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Ursino M, Magosso E, Lopane G, Calandra-Buonaura G, Cortelli P, Contin M. Mathematical modeling and parameter estimation of levodopa motor response in patients with parkinson disease. PLoS One 2020; 15:e0229729. [PMID: 32126124 PMCID: PMC7053720 DOI: 10.1371/journal.pone.0229729] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 02/12/2020] [Indexed: 11/19/2022] Open
Abstract
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.
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Affiliation(s)
- Mauro Ursino
- Department of Electrical, Electronic and Information Engineering, University of Bologna, Cesena, Italy
- * E-mail:
| | - Elisa Magosso
- Department of Electrical, Electronic and Information Engineering, University of Bologna, Cesena, Italy
| | - Giovanna Lopane
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Giovanna Calandra-Buonaura
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Pietro Cortelli
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Manuela Contin
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
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Zhao F, Zeng Y, Xu B. A Brain-Inspired Decision-Making Spiking Neural Network and Its Application in Unmanned Aerial Vehicle. Front Neurorobot 2018; 12:56. [PMID: 30258359 PMCID: PMC6143798 DOI: 10.3389/fnbot.2018.00056] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 08/20/2018] [Indexed: 11/13/2022] Open
Abstract
Decision-making is a crucial cognitive function for various animal species surviving in nature, and it is also a fundamental ability for intelligent agents. To make a step forward in the understanding of the computational mechanism of human-like decision-making, this paper proposes a brain-inspired decision-making spiking neural network (BDM-SNN) and applies it to decision-making tasks on intelligent agents. This paper makes the following contributions: (1) A spiking neural network (SNN) is used to model human decision-making neural circuit from both connectome and functional perspectives. (2) The proposed model combines dopamine and spike-timing-dependent plasticity (STDP) mechanisms to modulate the network learning process, which indicates more biological inspiration. (3) The model considers the effects of interactions among sub-areas in PFC on accelerating the learning process. (4) The proposed model can be easily applied to decision-making tasks in intelligent agents, such as an unmanned aerial vehicle (UAV) flying through a window and a UAV avoiding an obstacle. The experimental results support the effectiveness of the model. Compared with traditional reinforcement learning and existing biologically inspired methods, our method contains more biologically-inspired mechanistic principles, has greater accuracy and is faster.
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Affiliation(s)
- Feifei Zhao
- Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yi Zeng
- Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Bo Xu
- Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
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Ursino M, Baston C. Aberrant learning in Parkinson's disease: A neurocomputational study on bradykinesia. Eur J Neurosci 2018; 47:1563-1582. [DOI: 10.1111/ejn.13960] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 04/12/2018] [Accepted: 04/25/2018] [Indexed: 11/28/2022]
Affiliation(s)
- Mauro Ursino
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”; University of Bologna; Bologna Italy
| | - Chiara Baston
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”; University of Bologna; Bologna Italy
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