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Disrupted topological organization of resting-state functional brain networks in Parkinson’s disease patients with glucocerebrosidase gene mutations

  • Functional Neuroradiology
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Abstract

Purpose

The mutations of the glucocerebrosidase (GBA) gene are the greatest genetic risk factor for Parkinson’s disease (PD). The mechanism underlying the association between GBA mutations and PD has not been fully elucidated.

Methods

Using resting-state functional magnetic resonance imaging and graph theory analysis to investigate the disrupted topological organization in PD patients with GBA mutation (GBA-PD). Eleven GBA-PD patients, 11 noncarriers with PD, and 18 healthy controls (HCs) with a similar age and sex distribution were recruited. Individual whole-brain functional connectome was constructed, and the global and nodal topological disruptions were calculated among groups. Partial correlation analyses between the clinical features of patients with PD and topological alterations were performed.

Results

The GBA-PD group showed prominently decreased characteristic path length (Lp) and increased global efficiency (Eg) compared to HCs at the global level; a significantly increased nodal betweenness centrality in the medial prefrontal cortex (mPFC) and precuneus within the default mode network, and precentral gyrus within the sensorimotor network, while a significantly decreased betweenness centrality in nodes within the cingulo-opercular network compared to the noncarrier group at the regional level. The altered nodal betweenness centrality of mPFC was positively correlated with fatigue severity scale scores in all patients with PD.

Conclusion

The preliminary pilot study found that GBA-PD patients had a higher functional integration at the global level. The nodal result of the mPFC is congruent with the potential fatigue pathology in PD and is suggestive of a profound effect of GBA mutations on the clinical fatigue in patients with PD.

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Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

References

  1. Bloem BR, Okun MS, Klein C (2021) Parkinson’s disease. Lancet 397:2284–2303. https://doi.org/10.1016/S0140-6736(21)00218-X

    Article  CAS  Google Scholar 

  2. Sidransky E, Nalls MA, Aasly JO et al (2009) Multicenter analysis of glucocerebrosidase mutations in Parkinson’s disease. N Engl J Med 361:1651–1661. https://doi.org/10.1056/NEJMoa0901281

    Article  CAS  Google Scholar 

  3. Balestrino R, Schapira AHV (2018) Glucocerebrosidase and Parkinson disease: molecular, clinical, and therapeutic implications. Neuroscientist 24:540–559. https://doi.org/10.1177/1073858417748875

    Article  CAS  Google Scholar 

  4. Blandini F, Cilia R, Cerri S et al (2019) Glucocerebrosidase mutations and synucleinopathies: toward a model of precision medicine. Mov Disord 34:9–21. https://doi.org/10.1002/mds.27583

    Article  Google Scholar 

  5. Saeed U, Lang AE, Masellis M (2020) Neuroimaging advances in Parkinson’s disease and atypical Parkinsonian syndromes. Front Neurol 11:572976. https://doi.org/10.3389/fneur.2020.572976

    Article  Google Scholar 

  6. Filippi M, Balestrino R, Basaia S, Agosta F (2022) Neuroimaging in glucocerebrosidase-associated Parkinsonism: a systematic review. Mov Disord 37:1375–1393. https://doi.org/10.1002/mds.29047

    Article  CAS  Google Scholar 

  7. Leocadi M, Canu E, Donzuso G et al (2022) Longitudinal clinical, cognitive, and neuroanatomical changes over 5 years in GBA-positive Parkinson’s disease patients. J Neurol 269:1485–1500. https://doi.org/10.1007/s00415-021-10713-4

    Article  Google Scholar 

  8. Agosta F, Kostic VS, Davidovic K et al (2013) White matter abnormalities in Parkinson’s disease patients with glucocerebrosidase gene mutations. Mov Disord 28:772–778. https://doi.org/10.1002/mds.25397

    Article  CAS  Google Scholar 

  9. Thaler A (2018) Structural and functional MRI in familial Parkinson’s disease. Int Rev Neurobiol 142:261–287. https://doi.org/10.1016/bs.irn.2018.09.005

    Article  Google Scholar 

  10. Greuel A, Trezzi JP, Glaab E et al (2020) GBA Variants in Parkinson’s disease: clinical, metabolomic, and multimodal neuroimaging phenotypes. Mov Disord 35:2201–2210. https://doi.org/10.1002/mds.28225

    Article  CAS  Google Scholar 

  11. Luo CY, Guo XY, Song W et al (2015) Functional connectome assessed using graph theory in drug-naive Parkinson’s disease. J Neurol 262:1557–1567. https://doi.org/10.1007/s00415-015-7750-3

    Article  CAS  Google Scholar 

  12. Hughes AJ, Daniel SE, Kilford L, Lees AJ (1992) Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry 55:181–184

    Article  CAS  Google Scholar 

  13. Emre M, Aarsland D, Brown R et al (2007) Clinical diagnostic criteria for dementia associated with Parkinson’s disease. Mov Disord 22:1689–1707. https://doi.org/10.1002/mds.21507 (quiz 1837)

    Article  Google Scholar 

  14. Tomlinson CL, Stowe R, Patel S, Rick C, Gray R, Clarke CE (2010) Systematic review of levodopa dose equivalency reporting in Parkinson’s disease. Mov Disord 25:2649–2653. https://doi.org/10.1002/mds.23429

    Article  Google Scholar 

  15. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59:2142–2154. https://doi.org/10.1016/j.neuroimage.2011.10.018

    Article  Google Scholar 

  16. Hou Y, Wei Q, Ou R, Yang J, Gong Q, Shang H (2020) Impaired topographic organization in Parkinson’s disease with mild cognitive impairment. J Neurol Sci 414:116861. https://doi.org/10.1016/j.jns.2020.116861

    Article  Google Scholar 

  17. Medaglia JD (2017) Graph Theoretic analysis of resting state functional MR imaging. Neuroimaging Clin N Am 27:593–607. https://doi.org/10.1016/j.nic.2017.06.008

    Article  Google Scholar 

  18. Dosenbach NU, Nardos B, Cohen AL et al (2010) Prediction of individual brain maturity using fMRI. Science 329:1358–1361. https://doi.org/10.1126/science.1194144

    Article  CAS  Google Scholar 

  19. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003

    Article  Google Scholar 

  20. Sreenivasan K, Mishra V, Bird C et al (2019) Altered functional network topology correlates with clinical measures in very early-stage, drug-naive Parkinson’s disease. Parkinsonism Relat Disord 62:3–9. https://doi.org/10.1016/j.parkreldis.2019.02.001

    Article  Google Scholar 

  21. Suo X, Lei D, Li N et al (2017) Functional brain connectome and its relation to Hoehn and Yahr stage in Parkinson disease. Radiology 285:904–913. https://doi.org/10.1148/radiol.2017162929

    Article  Google Scholar 

  22. Braak H, Del Tredici K, Rub U, de Vos RA, Jansen Steur EN, Braak E (2003) Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol Aging 24:197–211. https://doi.org/10.1016/s0197-4580(02)00065-9

    Article  Google Scholar 

  23. Meppelink AM, de Jong BM, Renken R, Leenders KL, Cornelissen FW, van Laar T (2009) Impaired visual processing preceding image recognition in Parkinson’s disease patients with visual hallucinations. Brain 132:2980–2993. https://doi.org/10.1093/brain/awp223

    Article  Google Scholar 

  24. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL (2001) A default mode of brain function. Proc Natl Acad Sci U S A 98:676–682. https://doi.org/10.1073/pnas.98.2.676

    Article  CAS  Google Scholar 

  25. Xu P, Chen A, Li Y, Xing X, Lu H (2019) Medial prefrontal cortex in neurological diseases. Physiol Genomics 51:432–442. https://doi.org/10.1152/physiolgenomics.00006.2019

    Article  CAS  Google Scholar 

  26. Cavanna AE, Trimble MR (2006) The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129:564–583. https://doi.org/10.1093/brain/awl004

    Article  Google Scholar 

  27. Ramos BP, Arnsten AF (2007) Adrenergic pharmacology and cognition: focus on the prefrontal cortex. Pharmacol Ther 113:523–536. https://doi.org/10.1016/j.pharmthera.2006.11.006

    Article  CAS  Google Scholar 

  28. Friedman JH, Brown RG, Comella C et al (2007) Fatigue in Parkinson’s disease: a review. Mov Disord 22:297–308. https://doi.org/10.1002/mds.21240

    Article  Google Scholar 

  29. Chaudhuri A, Behan PO (2004) Fatigue in neurological disorders. Lancet 363:978–988. https://doi.org/10.1016/S0140-6736(04)15794-2

    Article  Google Scholar 

  30. Roelcke U, Kappos L, Lechner-Scott J et al (1997) Reduced glucose metabolism in the frontal cortex and basal ganglia of multiple sclerosis patients with fatigue: a 18F-fluorodeoxyglucose positron emission tomography study. Neurology 48:1566–1571. https://doi.org/10.1212/wnl.48.6.1566

    Article  CAS  Google Scholar 

  31. Strotzer QD, Kohl Z, Anthofer JM et al (2022) Structural connectivity patterns of side effects induced by subthalamic deep brain stimulation for Parkinson’s disease. Brain Connect 12:374–384. https://doi.org/10.1089/brain.2021.0051

    Article  Google Scholar 

  32. Menon V, Uddin LQ (2010) Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct 214:655–667. https://doi.org/10.1007/s00429-010-0262-0

    Article  Google Scholar 

  33. Wolters AF, van de Weijer SCF, Leentjens AFG, Duits AA, Jacobs HIL, Kuijf ML (2019) Resting-state fMRI in Parkinson’s disease patients with cognitive impairment: a meta-analysis. Parkinsonism Relat Disord 62:16–27. https://doi.org/10.1016/j.parkreldis.2018.12.016

    Article  Google Scholar 

  34. Pavese N, Metta V, Bose SK, Chaudhuri KR, Brooks DJ (2010) Fatigue in Parkinson’s disease is linked to striatal and limbic serotonergic dysfunction. Brain 133:3434–3443. https://doi.org/10.1093/brain/awq268

    Article  Google Scholar 

  35. Wang J, Wang L, Zang Y et al (2009) Parcellation-dependent small-world brain functional networks: a resting-state fMRI study. Hum Brain Mapp 30:1511–1523. https://doi.org/10.1002/hbm.20623

    Article  Google Scholar 

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Acknowledgements

The authors thank the patients and their families for their participation in the study.

Funding

The present study was supported by the National Key Research and Development Program of China (Grant No. 2021YFC2501203 to FHS); and the Sichuan Science and Technology Program (Grant No. 2022ZDZX0023 to FHS).

Author information

Authors and Affiliations

Authors

Contributions

Yanbing Hou: conception and design of the study, statistical analysis, interpretation of data, and drafting of manuscript

Fei Feng: data collection and statistical analysis

Lingyu Zhang: data collection and statistical analysis

Ruwei Ou: data collection

Junyu Lin: data collection

Qiyong Gong: study design, analysis and interpretation, and revision of the manuscript

Huifang Shang: study design, analysis and interpretation, and revision of the manuscript

Corresponding authors

Correspondence to Qiyong Gong or Huifang Shang.

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The authors declare no competing interests.

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Approval was received from the West China Hospital of Sichuan University Clinical trials and Biomedical ethics committee.

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Written informed consent was obtained from all participants in the study.

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Hou, Y., Feng, F., Zhang, L. et al. Disrupted topological organization of resting-state functional brain networks in Parkinson’s disease patients with glucocerebrosidase gene mutations. Neuroradiology 65, 361–370 (2023). https://doi.org/10.1007/s00234-022-03067-9

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  • DOI: https://doi.org/10.1007/s00234-022-03067-9

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