A computational model‐based analysis of basal ganglia pathway changes in Parkinson’s disease inferred from resting‐state fMRI
Previous computational model‐based approaches for understanding the dynamic changes related to Parkinson's disease made particular assumptions about Parkinson's disease‐related activity changes or specified dopamine‐dependent activation or learning rules. Inspired by recent model‐based ana...
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| Published in | The European journal of neuroscience Vol. 53; no. 7; pp. 2278 - 2295 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
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01.04.2021
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| Abstract | Previous computational model‐based approaches for understanding the dynamic changes related to Parkinson's disease made particular assumptions about Parkinson's disease‐related activity changes or specified dopamine‐dependent activation or learning rules. Inspired by recent model‐based analysis of resting‐state fMRI, we have taken a data‐driven approach. We fit the free parameters of a spiking neuro‐computational model to match correlations of blood oxygen level‐dependent signals between different basal ganglia nuclei and obtain subject‐specific neuro‐computational models of two subject groups: Parkinson patients and matched controls. When comparing mean firing rates at rest and connectivity strengths between the control and Parkinsonian model groups, several significant differences were found that are consistent with previous experimental observations. We discuss the implications of our approach and compare its results also with the popular “rate model” of the basal ganglia. Our study suggests that a model‐based analysis of imaging data from healthy and Parkinsonian subjects is a promising approach for the future to better understand Parkinson‐related changes in the basal ganglia and corresponding treatments.
We fit connectivity parameters of a spiking neuro‐computational basal ganglia (BG) model to replicate correlations of rs‐fMRI in Parkinson patients and control subjects and obtained data‐driven models of both groups. Our results (differences in connectivity, firing rates at rest and heterogeneity) show agreements with experimental findings and suggest that a model‐based analysis of imaging data from controls and patients is a promising approach to understand Parkinson induced changes in the BG. |
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| AbstractList | Previous computational model-based approaches for understanding the dynamic changes related to Parkinson's disease made particular assumptions about Parkinson's disease-related activity changes or specified dopamine-dependent activation or learning rules. Inspired by recent model-based analysis of resting-state fMRI, we have taken a data-driven approach. We fit the free parameters of a spiking neuro-computational model to match correlations of blood oxygen level-dependent signals between different basal ganglia nuclei and obtain subject-specific neuro-computational models of two subject groups: Parkinson patients and matched controls. When comparing mean firing rates at rest and connectivity strengths between the control and Parkinsonian model groups, several significant differences were found that are consistent with previous experimental observations. We discuss the implications of our approach and compare its results also with the popular "rate model" of the basal ganglia. Our study suggests that a model-based analysis of imaging data from healthy and Parkinsonian subjects is a promising approach for the future to better understand Parkinson-related changes in the basal ganglia and corresponding treatments. Previous computational model‐based approaches for understanding the dynamic changes related to Parkinson's disease made particular assumptions about Parkinson's disease‐related activity changes or specified dopamine‐dependent activation or learning rules. Inspired by recent model‐based analysis of resting‐state fMRI, we have taken a data‐driven approach. We fit the free parameters of a spiking neuro‐computational model to match correlations of blood oxygen level‐dependent signals between different basal ganglia nuclei and obtain subject‐specific neuro‐computational models of two subject groups: Parkinson patients and matched controls. When comparing mean firing rates at rest and connectivity strengths between the control and Parkinsonian model groups, several significant differences were found that are consistent with previous experimental observations. We discuss the implications of our approach and compare its results also with the popular “rate model” of the basal ganglia. Our study suggests that a model‐based analysis of imaging data from healthy and Parkinsonian subjects is a promising approach for the future to better understand Parkinson‐related changes in the basal ganglia and corresponding treatments. We fit connectivity parameters of a spiking neuro‐computational basal ganglia (BG) model to replicate correlations of rs‐fMRI in Parkinson patients and control subjects and obtained data‐driven models of both groups. Our results (differences in connectivity, firing rates at rest and heterogeneity) show agreements with experimental findings and suggest that a model‐based analysis of imaging data from controls and patients is a promising approach to understand Parkinson induced changes in the BG. Previous computational model-based approaches for understanding the dynamic changes related to Parkinson's disease made particular assumptions about Parkinson's disease-related activity changes or specified dopamine-dependent activation or learning rules. Inspired by recent model-based analysis of resting-state fMRI, we have taken a data-driven approach. We fit the free parameters of a spiking neuro-computational model to match correlations of blood oxygen level-dependent signals between different basal ganglia nuclei and obtain subject-specific neuro-computational models of two subject groups: Parkinson patients and matched controls. When comparing mean firing rates at rest and connectivity strengths between the control and Parkinsonian model groups, several significant differences were found that are consistent with previous experimental observations. We discuss the implications of our approach and compare its results also with the popular "rate model" of the basal ganglia. Our study suggests that a model-based analysis of imaging data from healthy and Parkinsonian subjects is a promising approach for the future to better understand Parkinson-related changes in the basal ganglia and corresponding treatments.Previous computational model-based approaches for understanding the dynamic changes related to Parkinson's disease made particular assumptions about Parkinson's disease-related activity changes or specified dopamine-dependent activation or learning rules. Inspired by recent model-based analysis of resting-state fMRI, we have taken a data-driven approach. We fit the free parameters of a spiking neuro-computational model to match correlations of blood oxygen level-dependent signals between different basal ganglia nuclei and obtain subject-specific neuro-computational models of two subject groups: Parkinson patients and matched controls. When comparing mean firing rates at rest and connectivity strengths between the control and Parkinsonian model groups, several significant differences were found that are consistent with previous experimental observations. We discuss the implications of our approach and compare its results also with the popular "rate model" of the basal ganglia. Our study suggests that a model-based analysis of imaging data from healthy and Parkinsonian subjects is a promising approach for the future to better understand Parkinson-related changes in the basal ganglia and corresponding treatments. |
| Author | Maith, Oliver Horn, Andreas Kühn, Andrea A. Hamker, Fred H. Villagrasa Escudero, Francesc Baladron, Javier Dinkelbach, Helge Ülo Irmen, Friederike |
| Author_xml | – sequence: 1 givenname: Oliver orcidid: 0000-0002-6446-9110 surname: Maith fullname: Maith, Oliver organization: Chemnitz University of Technology – sequence: 2 givenname: Francesc orcidid: 0000-0003-0149-6231 surname: Villagrasa Escudero fullname: Villagrasa Escudero, Francesc organization: Chemnitz University of Technology – sequence: 3 givenname: Helge Ülo orcidid: 0000-0002-8871-8177 surname: Dinkelbach fullname: Dinkelbach, Helge Ülo organization: Chemnitz University of Technology – sequence: 4 givenname: Javier surname: Baladron fullname: Baladron, Javier organization: Chemnitz University of Technology – sequence: 5 givenname: Andreas orcidid: 0000-0002-0695-6025 surname: Horn fullname: Horn, Andreas organization: Charité–University Medicine Berlin – sequence: 6 givenname: Friederike orcidid: 0000-0001-5574-3790 surname: Irmen fullname: Irmen, Friederike organization: Charité–University Medicine Berlin – sequence: 7 givenname: Andrea A. orcidid: 0000-0002-4134-9060 surname: Kühn fullname: Kühn, Andrea A. organization: Charité–University Medicine Berlin – sequence: 8 givenname: Fred H. orcidid: 0000-0001-9104-7143 surname: Hamker fullname: Hamker, Fred H. email: fred.hamker@informatik.tu-chemnitz.de organization: Chemnitz University of Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32558966$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3389_fninf_2022_790966 crossref_primary_10_1016_j_cnsns_2022_106614 crossref_primary_10_1016_j_brain_2022_100058 crossref_primary_10_1007_s11571_024_10119_8 crossref_primary_10_1111_ejn_15204 crossref_primary_10_1038_s41598_024_80630_9 crossref_primary_10_1007_s11571_024_10125_w crossref_primary_10_1038_s41531_024_00835_7 crossref_primary_10_1093_nsr_nwae079 crossref_primary_10_1016_j_expneurol_2022_114111 crossref_primary_10_1371_journal_pcbi_1011024 crossref_primary_10_1088_1741_2552_ad5406 crossref_primary_10_1371_journal_pone_0310367 crossref_primary_10_1007_s11571_023_09951_1 |
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| SubjectTerms | Basal ganglia BOLD correlations Central nervous system diseases Computational neuroscience data fitting Dopamine Firing pattern firing rate Functional magnetic resonance imaging Movement disorders Neural networks Neurodegenerative diseases Parkinson's disease spiking neuron model |
| Title | A computational model‐based analysis of basal ganglia pathway changes in Parkinson’s disease inferred from resting‐state fMRI |
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