journal article·2024-11Open access

Unraveling the Complexity of Parkinson's Disease: Insights into Pathogenesis and Precision Interventions

Han Yan, Cole Coughlin, Lee Smolin, Jin Wang

Advanced Science· Volume 11· Issue 42

Abstract

Abstract Parkinson's disease (PD) is a neurodegenerative disorder characterized by dopaminergic neuron loss, leading to motor and non‐motor symptoms. Early detection before symptom onset is crucial but challenging. This study presents a framework integrating circuit modeling, non‐equilibrium dynamics, and optimization to understand PD pathogenesis and enable precision interventions. Neuronal firing patterns, particularly oscillatory activity, play a critical role in PD pathology. The basal ganglia network, specifically the subthalamic nucleus‐external globus pallidus (STN‐GPe) circuitry, exhibits abnormal activity associated with motor dysfunction. The framework leverages the non‐equilibrium landscape and flux theory to identify key connections generating pathological activity, providing insights into disease progression and potential intervention points. The intricate STN‐GPe interplay is highlighted, shedding light on compensatory mechanisms within this circuitry may initially counteract changes but later contribute to pathological alterations as disease progresses. The framework addresses the need for comprehensive evaluation methods to assess intervention outcomes. Cross‐correlations between state variables provide superior early warning signals compared to traditional indicators relying on critical slowing down. By elucidating compensatory mechanisms and circuit dynamics, the framework contributes to improved management, early detection, risk assessment, and potential prevention/delay of PD development. This pioneering research paves the way for precision medicine in neurodegenerative disorders.
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Research context

OpenAlex topics: Neurological disorders and treatments · Parkinson's Disease Mechanisms and Treatments · Genetic Neurodegenerative Diseases

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Wenzhou Institute University of Chinese Academy of Sciences Wenzhou 325001 P. R. China

Perimeter Institute for Theoretical Physics 31 Caroline Street North, Waterloo Ontario N2J 2Y5 Canada

Department of Chemistry and Physics State University of New York at Stony Brook Stony Brook NY 11790 USA

Funding

National Natural Science Foundation of China

References (65)

Show source-provided references
1. 10.1016/S1471-1931(00)00028-810.1016/S1471-1931(00)00028-8 ↗
2. 10.1016/j.tins.2003.12.00610.1016/j.tins.2003.12.006 ↗
3. 10.1002/mds.2206210.1002/mds.22062 ↗
4. 10.1038/s41467-020-15352-310.1038/s41467-020-15352-3 ↗
5. 10.1016/j.neuroscience.2011.06.04810.1016/j.neuroscience.2011.06.048 ↗
6. 10.1002/mds.2774110.1002/mds.27741 ↗
7. 10.1016/j.neuron.2019.03.00410.1016/j.neuron.2019.03.004 ↗
8. 10.1212/WNL.0b013e31819908ce10.1212/WNL.0b013e31819908ce ↗
9. 10.1111/cns.1342910.1111/cns.13429 ↗
10. 10.1212/WNL.0b013e318198e0e910.1212/WNL.0b013e318198e0e9 ↗
11. 10.1016/j.expneurol.2017.10.00210.1016/j.expneurol.2017.10.002 ↗
12. 10.1038/nature0822710.1038/nature08227 ↗
13. 10.1126/science.122524410.1126/science.1225244 ↗
14. 10.1073/pnas.210377911810.1073/pnas.2103779118 ↗
15. 10.1073/pnas.221866312010.1073/pnas.2218663120 ↗
16. 10.1073/pnas.131069211010.1073/pnas.1310692110 ↗
17. 10.1371/journal.pone.017436410.1371/journal.pone.0174364 ↗
18. Yan H. · J. R. Soc., Interface · 2019 · 16 · 153
19. 10.1038/s42005-023-01210-310.1038/s42005-023-01210-3 ↗
20. 10.1016/0166-2236(90)90110-V10.1016/0166-2236(90)90110-V ↗
21. 10.1016/S0959-4388(96)80024-910.1016/S0959-4388(96)80024-9 ↗
22. 10.1002/mds.2369510.1002/mds.23695 ↗
23. 10.1523/JNEUROSCI.4199-08.200810.1523/JNEUROSCI.4199-08.2008 ↗
24. 10.1111/j.1460-9568.2011.07865.x10.1111/j.1460-9568.2011.07865.x ↗
25. 10.1371/journal.pcbi.100460910.1371/journal.pcbi.1004609 ↗
26. 10.1142/S012906571850021110.1142/S0129065718500211 ↗
27. 10.14814/phy2.1561210.14814/phy2.15612 ↗
28. 10.1016/j.neuron.2014.12.02210.1016/j.neuron.2014.12.022 ↗
29. 10.3389/fnana.2017.0002110.3389/fnana.2017.00021 ↗
30. 10.1038/nn.269210.1038/nn.2692 ↗
31. 10.1002/mds.2202710.1002/mds.22027 ↗
32. 10.1073/pnas.110774810810.1073/pnas.1107748108 ↗
33. 10.1038/nature1764310.1038/nature17643 ↗
34. 10.1136/jnnp-2017-31592210.1136/jnnp-2017-315922 ↗
35. 10.1073/pnas.181997511610.1073/pnas.1819975116 ↗
36. D. J.Amit Modeling Brain Function: The World of Attractor Neural Networks Cambridge University Press Cambridge1992.
37. 10.1016/j.neuron.2008.09.03410.1016/j.neuron.2008.09.034 ↗
38. 10.1016/B978-008045046-9.01397-810.1016/B978-008045046-9.01397-8 ↗
39. 10.1523/JNEUROSCI.0343-17.201710.1523/JNEUROSCI.0343-17.2017 ↗
40. 10.1080/00018732.2015.103706810.1080/00018732.2015.1037068 ↗
41. 10.1523/JNEUROSCI.5750-11.201210.1523/JNEUROSCI.5750-11.2012 ↗
42. 10.1113/jphysiol.2012.24133110.1113/jphysiol.2012.241331 ↗
43. 10.1016/j.cobme.2018.08.00810.1016/j.cobme.2018.08.008 ↗
44. 10.1523/JNEUROSCI.2277-18.201910.1523/JNEUROSCI.2277-18.2019 ↗
45. 10.1523/JNEUROSCI.3071-19.202010.1523/JNEUROSCI.3071-19.2020 ↗
46. 10.1002/mds.2736010.1002/mds.27360 ↗
47. 10.1038/s41467-024-49949-910.1038/s41467-024-49949-9 ↗
48. 10.1038/s43587-023-00478-y10.1038/s43587-023-00478-y ↗
49. 10.1002/mdc3.1371010.1002/mdc3.13710 ↗
50. 10.1002/adbi.20200022310.1002/adbi.202000223 ↗
51. 10.1038/micronano.2017.7010.1038/micronano.2017.70 ↗
52. 10.1016/j.jneumeth.2008.06.03610.1016/j.jneumeth.2008.06.036 ↗
53. 10.3390/cells809100510.3390/cells8091005 ↗
54. 10.1016/j.neuroimage.2013.05.08410.1016/j.neuroimage.2013.05.084 ↗
55. 10.1152/jn.00055.202310.1152/jn.00055.2023 ↗
56. 10.1038/s41467-023-41128-610.1038/s41467-023-41128-6 ↗
57. 10.1111/j.1460-9568.2006.04717.x10.1111/j.1460-9568.2006.04717.x ↗
58. 10.1523/JNEUROSCI.1913-18.201810.1523/JNEUROSCI.1913-18.2018 ↗
59. 10.1016/j.expneurol.2008.11.00810.1016/j.expneurol.2008.11.008 ↗
60. 10.1016/S1353-8020(13)70013-010.1016/S1353-8020(13)70013-0 ↗
61. 10.1093/brain/awad20610.1093/brain/awad206 ↗
62. 10.1038/2328110.1038/23281 ↗
63. 10.3389/fnana.2015.0000510.3389/fnana.2015.00005 ↗
64. 10.1016/S0006-3495(72)86068-510.1016/S0006-3495(72)86068-5 ↗
65. 10.1162/08989290332110789110.1162/089892903321107891 ↗