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.
Similar content being viewed by others
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
References
Pei, G. Y., Guo, G. X., Chen, D. D., Yang, R. S., Shi, Z. Y., Wang, S. J., et al. (2020). BrainKilter: a real-time EEG analysis platform for neurofeedback design and training. Ieee Access, 8, 57661–57673. https://doi.org/10.1109/access.2020.2967903.
Wang, B., Niu, Y., Miao, L. W., Cao, R., Yan, P. F., Guo, H., et al. (2017). Decreased complexity in Alzheimer's disease: Resting-state fMRI evidence of brain entropy mapping. Frontiers in Aging Neuroscience, 9, 11. https://doi.org/10.3389/fnagi.2017.00378.
Yan, T., Wang, W., Yang, L., Chen, K., Chen, R., & Han, Y. (2018). Rich club disturbances of the human connectome from subjective cognitive decline to Alzheimer's disease. Theranostics, 8(12), 3237–3255. https://doi.org/10.7150/thno.23772.
Bassett, D. S., & Bullmore, E. T. (2006). Small-world brain networks. Neuroscientist, 12(6), 512–523. https://doi.org/10.1177/1073858406293182.
Bassett, D. S., & Bullmore, E. T. (2009). Human brain networks in health and disease. Current Opinion in Neurology, 22(4), 340–347. https://doi.org/10.1097/WCO.0b013e32832d93dd.
Bassett, D. S., & Bullmore, E. T. (2017). Small-world brain networks revisited. Neuroscientist, 23(5), 499–516. https://doi.org/10.1177/1073858416667720.
Hu, X., Uhle, F., Fliessbach, K., Wagner, M., Han, Y., Weber, B., & Jessen, F. (2017). Reduced future-oriented decision making in individuals with subjective cognitive decline: A functional MRI study. Alzheimers Dement (Amst), 6, 222–231. https://doi.org/10.1016/j.dadm.2017.02.005.
Bai, F., Shu, N., Yuan, Y., Shi, Y., Yu, H., Wu, D., Wang, J., Xia, M., He, Y., & Zhang, Z. (2012). Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. The Journal of Neuroscience, 32(12), 4307–4318. https://doi.org/10.1523/jneurosci.5061-11.2012.
Zhao, X., Liu, Y., Wang, X., Liu, B., Xi, Q., Guo, Q., Jiang, H., Jiang, T., & Wang, P. (2012). Disrupted small-world brain networks in moderate Alzheimer's disease: A resting-state FMRI study. PLoS One, 7(3), e33540. https://doi.org/10.1371/journal.pone.0033540.
Kambeitz, J., Kambeitz-Ilankovic, L., Cabral, C., Dwyer, D. B., Calhoun, V. D., van den Heuvel, M. P., Falkai, P., Koutsouleris, N., & Malchow, B. (2016). Aberrant functional whole-brain network architecture in patients with schizophrenia: A meta-analysis. Schizophr Bull, 42 Suppl, 1(Suppl 1), S13–S21. https://doi.org/10.1093/schbul/sbv174.
Alexander-Bloch, A. F., Gogtay, N., Meunier, D., Birn, R., Clasen, L., Lalonde, F., Lenroot, R., Giedd, J., & Bullmore, E. T. (2010). Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia. Frontiers in Systems Neuroscience, 4, 147. https://doi.org/10.3389/fnsys.2010.00147.
Gong, Q., & He, Y. (2015). Depression, neuroimaging and connectomics: A selective overview. Biological Psychiatry, 77(3), 223���235. https://doi.org/10.1016/j.biopsych.2014.08.009.
Xie, Y. Y., Liu, T. T., Ai, J., Chen, D. D., Zhuo, Y. R., Zhao, G. L., et al. (2019). Changes in centrality frequency of the default mode network in individuals with subjective cognitive decline. [article]. Frontiers in Aging Neuroscience, 11, 11. https://doi.org/10.3389/fnagi.2019.00118.
Lu, Z. Q. J. (2010). The elements of statistical learning: Data mining, inference, and prediction, 2nd edition. Journal of the Royal Statistical Society Series a-Statistics in Society, 173, 693–694.
Long, Z., Jing, B., Yan, H., Dong, J., Liu, H., Mo, X., Han, Y., & Li, H. (2016). A support vector machine-based method to identify mild cognitive impairment with multi-level characteristics of magnetic resonance imaging. Neuroscience, 331, 169–176. https://doi.org/10.1016/j.neuroscience.2016.06.025.
Yan, T., Wang, Y., Weng, Z., Du, W., Liu, T., Chen, D., et al. (2019). Early-stage identification and pathological development of Alzheimer's disease using multimodal MRI. Journal of Alzheimer's Disease, 68(3), 1013–1027. https://doi.org/10.3233/jad-181049.
Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. Neuroimage, 45(1), S199–S209. https://doi.org/10.1016/j.neuroimage.2008.11.007.
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003.
He, B., Dai, Y. K., Astolfi, L., Babiloni, F., Yuan, H., & Yang, L. (2011). eConnectome: A MATLAB toolbox for mapping and imaging of brain functional connectivity. Journal of Neuroscience Methods, 195(2), 261–269. https://doi.org/10.1016/j.jneumeth.2010.11.015.
Hosseini, S. M., Hoeft, F., & Kesler, S. R. (2012). GAT: A graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks. PLoS One, 7(7), e40709. https://doi.org/10.1371/journal.pone.0040709.
Cui, Z., Zhong, S., Xu, P., He, Y., & Gong, G. (2013). PANDA: A pipeline toolbox for analyzing brain diffusion images. Frontiers in Human Neuroscience, 7, 42. https://doi.org/10.3389/fnhum.2013.00042.
Wang, J. H., Wang, X. D., Xia, M. R., Liao, X. H., Evans, A., & He, Y. (2015). GRETNA: A graph theoretical network analysis toolbox for imaging connectomics. Frontiers in Human Neuroscience, 9, 16. https://doi.org/10.3389/fnhum.2015.00386.
van der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The NumPy Array: A structure for efficient numerical computation. Computing in Science & Engineering, 13(2), 22–30.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics, 8, 14. https://doi.org/10.3389/fninf.2014.00014.
Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/Mcse.2007.55.
Ramachandran, P., & Varoquaux, G. (2011). Mayavi: 3D visualization of scientific data. Computing in Science & Engineering, 13(2), 40–50. https://doi.org/10.1109/Mcse.2011.35.
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273–289. https://doi.org/10.1006/nimg.2001.0978.
Taguchi, Y. H., Iwadate, M., & Umeyama, H. (2015). Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease. BMC Bioinformatics, 16, 139. https://doi.org/10.1186/s12859-015-0574-4.
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, 417–441. https://doi.org/10.1037/h0071325.
Ruxton, G. D. (2006). The unequal variance t-test is an underused alternative to Student's t-test and the Mann-Whitney U test. Behavioral Ecology, 17(4), 688–690. https://doi.org/10.1093/beheco/ark016.
Yilmaz, E. (2013). An expert system based on fisher score and LS-SVM for cardiac arrhythmia diagnosis. Computational and Mathematical Methods in Medicine, 2013, 1–6. https://doi.org/10.1155/2013/849674.
Khazaee, A., Ebrahimzadeh, A., & Babajani-Feremi, A. (2016). Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease. Brain Imaging and Behavior, 10(3), 799–817. https://doi.org/10.1007/s11682-015-9448-7.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B-Methodological, 58(1), 267–288.
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B-Statistical Methodology, 67, 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x.
Painsky, A., & Rosset, S. (2017). Cross-validated variable selection in tree-based methods improves predictive performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(11), 2142–2153. https://doi.org/10.1109/Tpami.2016.2636831.
Orru, G., Pettersson-Yeo, W., Marquand, A. F., Sartori, G., & Mechelli, A. (2012). Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review. Neuroscience and Biobehavioral Reviews, 36(4), 1140–1152. https://doi.org/10.1016/j.neubiorev.2012.01.004.
Coomans, D., & Massart, D. L. (1982). Alternative k-nearest neighbor rules in supervised pattern-recognition .3. Condensed nearest neighbor rules. Analytica Chimica Acta, 138(Jun), 167–176. https://doi.org/10.1016/S0003-2670(01)85299-5.
Matsugu, M., Mori, K., Mitari, Y., & Kaneda, Y. (2003). Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Networks, 16(5–6), 555–559. https://doi.org/10.1016/S0893-6080(03)00115-1.
Mehmood, A., Maqsood, M., Bashir, M., & Shuyuan, Y. (2020). A deep Siamese convolution neural network for multi-class classification of Alzheimer disease. Brain Sciences, 10(2). https://doi.org/10.3390/brainsci10020084.
Alves, G. S., Knochel, V. O., Knochel, C., Carvalho, A. F., Pantel, J., Engelhardt, E., et al. (2015). Integrating Retrogenesis theory to Alzheimer's disease Pathology: Insight from DTI-TBSS investigation of the white matter microstructural integrity. Biomed Research International, https://doi.org/10.1155/2015/291658.
Nir, T. M., Jahanshad, N., Villalon-Reina, J. E., Toga, A. W., Jack, C. R., Weiner, M. W., Thompson, P. M., & Alzheimer's Disease Neuroimaging Initiative (ADNI). (2013). Effectiveness of regional DTI measures in distinguishing Alzheimer's disease, MCI, and normal aging. Neuroimage-Clinical, 3, 180–195. https://doi.org/10.1016/j.nicl.2013.07.006.
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
Authors and Affiliations
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
Ethics declarations
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.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11265-020-01583-6