The onset of motor learning impairments in Parkinson’s disease: a computational investigation
Abstract The basal ganglia (BG) is part of a basic feedback circuit regulating cortical function, such as voluntary movements control, via their influence on thalamocortical projections. BG disorders, namely Parkinson’s disease (PD), characterized by the loss of neurons in the substantia nigra, invo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-01-01
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| Series: | Brain Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40708-023-00215-6 |
| _version_ | 1797272738988032000 |
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| author | Ilaria Gigi Rosa Senatore Angelo Marcelli |
| author_facet | Ilaria Gigi Rosa Senatore Angelo Marcelli |
| author_sort | Ilaria Gigi |
| collection | DOAJ |
| description | Abstract The basal ganglia (BG) is part of a basic feedback circuit regulating cortical function, such as voluntary movements control, via their influence on thalamocortical projections. BG disorders, namely Parkinson’s disease (PD), characterized by the loss of neurons in the substantia nigra, involve the progressive loss of motor functions. At the present, PD is incurable. Converging evidences suggest the onset of PD-specific pathology prior to the appearance of classical motor signs. This latent phase of neurodegeneration in PD is of particular relevance in developing more effective therapies by intervening at the earliest stages of the disease. Therefore, a key challenge in PD research is to identify and validate markers for the preclinical and prodromal stages of the illness. We propose a mechanistic neurocomputational model of the BG at a mesoscopic scale to investigate the behavior of the simulated neural system after several degrees of lesion of the substantia nigra, with the aim of possibly evaluating which is the smallest lesion compromising motor learning. In other words, we developed a working framework for the analysis of theoretical early-stage PD. While simulations in healthy conditions confirm the key role of dopamine in learning, in pathological conditions the network predicts that there may exist abnormalities of the motor learning process, for physiological alterations in the BG, that do not yet involve the presence of symptoms typical of the clinical diagnosis. |
| first_indexed | 2024-03-07T14:33:41Z |
| format | Article |
| id | doaj.art-1e36916e0ead4e6cbec3622f1835141e |
| institution | Directory Open Access Journal |
| issn | 2198-4018 2198-4026 |
| language | English |
| last_indexed | 2024-03-07T14:33:41Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj.art-1e36916e0ead4e6cbec3622f1835141e2024-03-05T20:46:19ZengSpringerOpenBrain Informatics2198-40182198-40262024-01-0111112010.1186/s40708-023-00215-6The onset of motor learning impairments in Parkinson’s disease: a computational investigationIlaria Gigi0Rosa Senatore1Angelo Marcelli2Institute of Cognitive Sciences and Technologies (ISTC), National Research Council of Italy (CNR)Natural Intelligent Technologies Ltd, Piazza Vittorio Emanuele 10Department of Information Engineering, Electrical Engineering, and Applied Mathematics (DIEM), University of SalernoAbstract The basal ganglia (BG) is part of a basic feedback circuit regulating cortical function, such as voluntary movements control, via their influence on thalamocortical projections. BG disorders, namely Parkinson’s disease (PD), characterized by the loss of neurons in the substantia nigra, involve the progressive loss of motor functions. At the present, PD is incurable. Converging evidences suggest the onset of PD-specific pathology prior to the appearance of classical motor signs. This latent phase of neurodegeneration in PD is of particular relevance in developing more effective therapies by intervening at the earliest stages of the disease. Therefore, a key challenge in PD research is to identify and validate markers for the preclinical and prodromal stages of the illness. We propose a mechanistic neurocomputational model of the BG at a mesoscopic scale to investigate the behavior of the simulated neural system after several degrees of lesion of the substantia nigra, with the aim of possibly evaluating which is the smallest lesion compromising motor learning. In other words, we developed a working framework for the analysis of theoretical early-stage PD. While simulations in healthy conditions confirm the key role of dopamine in learning, in pathological conditions the network predicts that there may exist abnormalities of the motor learning process, for physiological alterations in the BG, that do not yet involve the presence of symptoms typical of the clinical diagnosis.https://doi.org/10.1186/s40708-023-00215-6Basal gangliaMotor learningNeural network modelParkinson’s diseaseReinforcement learning |
| spellingShingle | Ilaria Gigi Rosa Senatore Angelo Marcelli The onset of motor learning impairments in Parkinson’s disease: a computational investigation Brain Informatics Basal ganglia Motor learning Neural network model Parkinson’s disease Reinforcement learning |
| title | The onset of motor learning impairments in Parkinson’s disease: a computational investigation |
| title_full | The onset of motor learning impairments in Parkinson’s disease: a computational investigation |
| title_fullStr | The onset of motor learning impairments in Parkinson’s disease: a computational investigation |
| title_full_unstemmed | The onset of motor learning impairments in Parkinson’s disease: a computational investigation |
| title_short | The onset of motor learning impairments in Parkinson’s disease: a computational investigation |
| title_sort | onset of motor learning impairments in parkinson s disease a computational investigation |
| topic | Basal ganglia Motor learning Neural network model Parkinson’s disease Reinforcement learning |
| url | https://doi.org/10.1186/s40708-023-00215-6 |
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