References
1. Babayan, A., Erbey, M., Kumral, D., Reinelt, R. D., Reiter, A. M. F., Röbbig, J. … Villringer, A. (2019). A Mind-Brain-Body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Scientific Data, 6, 180308.
2. Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173.
3. Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H. … Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23 Suppl 1, S208-219.
4. Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Segonne, F., Salat, D. H. … Dale, A. M. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14, 11–22.
5. Tournier, J. D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M. … Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualization. Neuroimage, 202, 116137.
6. Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage, 54(3), 2033–2044.
7. Pascual-Marqui, R. D., Lehmann, D., Koukkou, M., Kochi, K., Anderer, P., Saletu, B., Tanaka, H., Hirata, K., John, E. R., Prichep, L., Biscay-Lirio, R., & Kinoshita, T. (2011). Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philos Trans A Math Phys Eng Sci, 369(1952), 3768–3784. https://doi.org/10.1098/rsta.2011.0081.
[doi]
8. Desikan, R. S., Segonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D. … Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31, 968–980.
9. Kwon, H.; Kim, J.; Son, S.; Jang, Y.; Kim, B.; Lee, H.; Lee, J. (2022). Sparse Hierarchical Representation Learning on Functional Brain Networks for Prediction of Autism Severity Levels. Front. Neurosci, 16, 935431.
10. C.J. Honey, O. Sporns, L. Cammoun, X. Gigandet, J.P. Thiran, R. Meuli, & P. Hagmann, Predicting human resting-state functional connectivity from structural connectivity, Proc. Natl. Acad. Sci. U.S.A. 106 (6) 2035-2040, https://doi.org/10.1073/pnas.0811168106 (2009).
[doi]
11. Zhang, Y.D.; Dong, Z.; Wang, S.H.; Yu, X.; Yao, X.; Zhou, Q.; Hu, H.; Li, M.; Jiménez-Mesa, C.; Ramirez, J.; Martinez, F.J.; Gorriz, J.M. (2020). Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation. Information Fusion, 64, 149–187. https://doi.org/10.1016/j.inffus.2020.07.006
[doi]