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Classification of Parkinson’s disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach

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Abstract

To investigate the value of combining amplitude of low-frequency fluctuations-based radiomics and the support vector machine classifier method in distinguishing patients with Parkinson’s disease from healthy controls. A total of 123 patients with Parkinson’s disease and 90 healthy controls from three centers with functional and structural MRI images were included in this study. We extracted radiomics features using the Brainnetome 246 atlas from the mean amplitude of low-frequency fluctuations maps. Two-sample t-tests and recursive feature elimination combined with support vector machine method were applied for feature selection and dimensionality reduction. We used support vector machine classifier to construct model and identify the discriminative features. The automated anatomical labeling 90 atlas and fivefold cross-validation were used to evaluate the robustness and generalization of the classifier. We found our model obtained a high classification performance with an accuracy of 78.07%, and AUC, sensitivity, and specificity of 0.8597, 78.80%, and 76.08%, respectively. We detected 7 discriminative brain subregions. The fivefold cross-validation and automated anatomical labeling 90 atlas also got high classification accuracy, and we found Brainnetome 246 atlas achieved a higher classification performance than the automated anatomical labeling 90 atlas both with tenfold and fivefold cross-validation. Our findings may help the early diagnosis of Parkinson’s disease and provide support for research on Parkinson’s disease mechanisms and clinical evaluation.

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Availability of data and material

Data used in this study were obtained from three independent publicly available databases. (1) Dataset 1: Downloaded from http://dx.doi.org/10.6084/m9.figshare.1433996. (2) Dataset 2: Downloaded from http://fcon_1000.projects.nitrc.org/indi/retro/parkinsons.html. (3) Dataset 3: Downloaded from https://doi.org/10.1371/journal.pone.0200623.

Abbreviations

AAL:

Automated anatomical labeling

ALFF:

Amplitude of low-frequency fluctuations

AUC:

Area under curve

CERR:

Computational Environment for Radiotherapy Research

BOLD:

Blood oxygen level-dependent

DARTEL:

Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra

EPI:

Echo-planar imaging

HC:

Healthy control

IFG:

Inferior frontal gyrus

MFG:

Middle frontal gyrus

MNI:

Montreal Neurologic Institute

MRI:

Magnetic resonance imaging

PD:

Parkinson’s disease

PhG:

Parahippocampal gyrus

PoG:

Postcentral gyrus

PrG:

Precentral gyrus

ROI:

Region of interest

rs-fMRI:

Resting-state functional MRI

SFG:

Superior frontal gyrus

STG:

Superior temporal gyrus

SVM:

Support vector machine

SVM-RFE:

Recursive feature elimination combined with support vector machine

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Acknowledgements

The authors thank the data authors for providing access to the data used in our study. This work is supported by Scientific Research Foundation for Advanced Talents, Xiang'an Hospital of Xiamen University (NO. PM201809170011). We thank Editage Academic Editing for assistance with English editing that greatly improved the manuscript.

Funding

This work is supported by Scientific Research Foundation for Advanced Talents, Xiang'an Hospital of Xiamen University (NO. PM201809170011).

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Authors and Affiliations

  1. Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361002, China

    Dafa Shi, Xiang Yao, Yanfei Li, Haoran Zhang, Guangsong Wang, Siyuan Wang & Ke Ren

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  1. Dafa Shi
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  2. Xiang Yao
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  3. Yanfei Li
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  4. Haoran Zhang
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  5. Guangsong Wang
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  6. Siyuan Wang
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  7. Ke Ren
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Contributions

DS conducted the experiment, performed the data processing and analysis, and wrote the manuscript. XY, YL, HZ, GW and SW collected the data and performed the data processing and analysis.KR supervised the whole including experiments and manuscript writing. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Ke Ren.

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Ethical approval

This study was performed in line with the principles of the Declaration of Helsinki. The data of this study were obtained from independent publicly available databases, and the data was approved by the ethics committee of the data collection agency.

Consent to participate

All subjects agreed to participate in this study. Written informed consent was obtained from all subjects in this study.

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All authors approved the publication of this manuscript.

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None of the authors have a conflict of interest to declare.

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Shi, D., Yao, X., Li, Y. et al. Classification of Parkinson’s disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach. Brain Imaging and Behavior 16, 2150–2163 (2022). https://doi.org/10.1007/s11682-022-00685-y

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  • DOI: https://doi.org/10.1007/s11682-022-00685-y

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