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BrainSort: a Machine Learning Toolkit for Brain Connectome Data Analysis and Visualization

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Abstract

In recent years, applying machine learning methods to neurological and psychiatric disorder diagnoses has grasped the interest of many researchers; however, currently available machine learning toolboxes usually require somewhat intermediate programming knowledge. In order to use machine learning methods more quickly and conveniently, we developed an intuitive toolbox named BrainSort. BrainSort used Python as the main programming languages and employed a hospitable Graphical User Interface (GUI). The toolbox is user-friendly for researchers and clinical doctors with little to no prior programming skills. It enables the client to choose from multiple machine learning methods, such as support vector machine (SVM), k-nearest neighbors (k-NN), and convolutional neural network (CNN) for data processing and training. Using BrainSort, doctors and researchers can calculate and visualize the correlation between brain connectome topology parameters and the symptom in question without prolonged programming training, empowering them to use the powerful tool of machine learning in their studies and practices.

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Data Availability

Raw data were generated at Xuan Wu Hospital. Derived data and materials supporting the findings of this study are available from the corresponding author on request.

Abbreviations

AAL:

Automated Anatomical Labeling

AD:

Alzheimer’s disease

AUC:

area under curve

CNN:

convolutional neural network

EEG:

Electroencephalography

GUI:

graphical user interface

HC:

healthy control

k-NN:

k-nearest neighbors

MRI:

magnetic resonance imaging

ROC:

receiver operating characteristic

ROI:

region of interest

SVM:

support vector machine

3D:

Three dimensional

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Acknowledgments

We gratefully acknowledge all the participants, clinical doctors and researchers at the Department of Neurology, XuanWu Hospital of Capital Medical University. We thank Tianyu Zhang for his help and contribution during MR image data processing.

Funding

This work was supported by the National Key Research and Development Program of China under grant 2018YFC0115400, the National Natural Science Foundation of China (Grant No. 81671776, 61727807, 81601454), the Beijing Municipal Science and Technology Commission (Z191100010618004).

Author information

Author notes

  1. Miaomiao Liu and Tiantian Liu contributed equally to this work.

Authors and Affiliations

  1. Graduate School of Natural Science and Technology, Okayama University, Okayama, Japan

    Miaomiao Liu & Jinglong Wu

  2. School of Life Science, Beijing Institute of Technology, Beijing, China

    Tiantian Liu, Yonghao Wang, Yuan Feng & Tianyi Yan

  3. Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China

    Yunyan Xie

  4. Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China

    Jinglong Wu

Authors
  1. Miaomiao Liu
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  4. Yuan Feng
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  5. Yunyan Xie
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Contributions

ML and TL contribute equally to this study. ML wrote the manuscript. TL verified the analytical methods. YW and YF contributed the toolkit coding. YX performed the data collection. TY conceived of the presented idea. JW supervised the project.

Corresponding author

Correspondence to Tianyi Yan.

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Conflict of Interest

The authors declare no conflict of interest.

Ethics Approval

This study was approved by the Medical Research Ethics Committee and the Institutional Review Board of Xuan Wu Hospital (Clinical Trials.gov identifier: NCT02353884 and NCT02225964).

Consent to Participate

All participants were provided with written informed consent, which they signed prior to any experimental procedures.

Consent to Publication

All authors discussed the study, read the manuscript, and approved its submission to your journal. The manuscript has not been published previously, and it is not under consideration for publication elsewhere.

Code Availability

The code that support the findings of this study are openly available at https://github.com/BIT-Brain-Lab/Brain-Sort.

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Liu, M., Liu, T., Wang, Y. et al. BrainSort: a Machine Learning Toolkit for Brain Connectome Data Analysis and Visualization. J Sign Process Syst 94, 485–495 (2022). https://doi.org/10.1007/s11265-020-01583-6

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