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The exploration of Parkinson’s disease: a multi-modal data analysis of resting functional magnetic resonance imaging and gene data

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Abstract

Parkinson’s disease (PD) is the most universal chronic degenerative neurological dyskinesia and an important threat to elderly health. At present, the researches of PD are mainly based on single-modal data analysis, while the fusion research of multi-modal data may provide more meaningful information in the aspect of comprehending the pathogenesis of PD. In this paper, 104 samples having resting functional magnetic resonance imaging (rfMRI) and gene data are from Parkinson’s Progression Markers Initiative (PPMI) and Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to predict pathological brain areas and risk genes related to PD. In the experiment, Pearson correlation analysis is adopted to conduct fusion analysis from the data of genes and brain areas as multi-modal sample characteristics, and the clustering evolution random forest (CERF) method is applied to detect the discriminative genes and brain areas. The experimental results indicate that compared with several existing advanced methods, the CERF method can further improve the diagnosis of PD and healthy control, and can achieve a significant effect. More importantly, we find that there are some interesting associations between brain areas and genes in PD patients. Based on these associations, we notice that PD-related brain areas include angular gyrus, thalamus, posterior cingulate gyrus and paracentral lobule, and risk genes mainly include C6orf10, HLA-DPB1 and HLA-DOA. These discoveries have a significant contribution to the early prevention and clinical treatments of PD.

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Acknowledgements

This work was supported by the Natural Science Foundation of Hunan Province, China (Grant No. 2020JJ4432, the Degree & Postgraduate Education Reform Project of Hunan Province (Grant No. 2019JGYB091), the Hunan Provincial Science and Technology Project Foundation (Grant No. 2018TP1018), the National Natural Science Foundation of China (Grant No. 61502167).

PPMI – a public-private partnership–funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbvie, Avid Radiopharmaceuticals, Biogen, BioLegend, Bristol-Myers Squibb, General Electric Healthcare, Genentech, GlaxoSmithKline, Lilly, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal Imaging, Roche, Sanofi-Genzyme, Servier, Takeda, Teva and UCB.

Data collection and sharing for this project were also funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-20012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F.Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. Private sector contributions were facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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  1. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, People’s Republic of China

    Xia-an Bi, Hao Wu, Yiming Xie, Lixia Zhang, Xun Luo & Yu Fu

  2. College of Information Science and Engineering, Hunan Normal University, Changsha, People’s Republic of China

    Xia-an Bi, Hao Wu, Yiming Xie, Lixia Zhang, Xun Luo & Yu Fu

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  1. Xia-an Bi
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Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI)- database (adni.loni.usc.edu) and the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information of PPMI on the study, visit www.ppmi-info.org.As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

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Bi, Xa., Wu, H., Xie, Y. et al. The exploration of Parkinson’s disease: a multi-modal data analysis of resting functional magnetic resonance imaging and gene data. Brain Imaging and Behavior 15, 1986–1996 (2021). https://doi.org/10.1007/s11682-020-00392-6

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