This title appears in the Scientific Report :
2024
Please use the identifier:
http://dx.doi.org/10.34734/FZJ-2024-01177 in citations.
Please use the identifier: http://dx.doi.org/10.1101/2024.01.12.574450 in citations.
Simulated brain networks reflecting progression of Parkinson’s disease
Simulated brain networks reflecting progression of Parkinson’s disease
Neurodegenerative progression of Parkinson’s disease affects brain structure and function and, concomitantly, alters topological properties of brain networks. The network alteration accompanied with motor impairment and duration of the disease is not yet clearly demonstrated in the disease progressi...
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Personal Name(s): | Jung, Kyesam (First author) |
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Eickhoff, Simon / Caspers, Julian / Popovych, Oleksandr (Corresponding author) | |
Contributing Institute: |
Gehirn & Verhalten; INM-7 |
Published in: | bioRxiv beta (2024) |
Imprint: |
Cold Spring Harbor
Cold Spring Harbor Laboratory, NY
2024
|
DOI: |
10.34734/FZJ-2024-01177 |
DOI: |
10.1101/2024.01.12.574450 |
Document Type: |
Preprint |
Research Program: |
Neuroimaging Personalized Recommendations for Neurodegenerative Disease Human Brain Project Specific Grant Agreement 2 Human Brain Project Specific Grant Agreement 3 Computational Principles |
Link: |
Get full text OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1101/2024.01.12.574450 in citations.
Neurodegenerative progression of Parkinson’s disease affects brain structure and function and, concomitantly, alters topological properties of brain networks. The network alteration accompanied with motor impairment and duration of the disease is not yet clearly demonstrated in the disease progression. In this study, we aim at resolving this problem with a modeling approach based on large-scale brain networks from cross-sectional MRI data. Optimizing whole-brain simulation models allows us to discover brain networks showing unexplored relationships with clinical variables. We observe that simulated brain networks exhibit significant differences between healthy controls (n=51) and patients with Parkinson’s disease (n=60) and strongly correlate with disease severity and disease duration of the patients. Moreover, the modeling results outperform the empirical brain networks in these clinical measures. Consequently, this study demonstrates that utilizing simulated brain networks provides an enhanced view on network alterations in the progression of motor impairment and potential biomarkers for clinical indices. |