Abdallah, M., Farrugia, N., Chirokoff, V., & Chanraud, S. (2020). Static and dynamic aspects of cerebro-cerebellar functional connectivity are associated with self-reported measures of impulsivity: A resting-state fmri study. Network Neuroscience, 4(3), 891–909.
Article PubMed PubMed Central Google Scholar
Al-Ezzi, A., Yahya, N., Kamel, N., Faye, I., Alsaih, K., & Gunaseli, E. (2021). Severity assessment of social anxiety disorder using deep learning models on brain effective connectivity. IEEE Access, 9, 86899–86913.
Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2014). Tracking whole-brain connectivity dynamics in the resting state. Cerebral Cortex, 24(3), 663–676.
Andersson, J. L., Skare, S., & Ashburner, J. (2003). How to correct susceptibility distortions in spin-echo echo-planar images: Application to diffusion tensor imaging. NeuroImage, 20(2), 870–888.
Barlow, D. H., Curreri, A. J., & Woodard, L. S. (2021). Neuroticism and disorders of emotion: A new synthesis. Current Directions in Psychological Science, 30(5), 410–417.
Bispo Júnior, J. P. (2022). Social desirability bias in qualitative health research. Revista de Saúde Pública, 56.
Bridle, J. S. (1990). Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. Neurocomputing: Algorithms, architectures and applications (pp. 227–236).
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321–357.
Cheng, W., Zhao, M., Xie, X., Chen, Y., Huang, M., & Tang, Y. (2022). Abnormal intrinsic connectivity of resting-state networks in bipolar disorder: A machine learning study. Frontiers in Psychiatry, 13, 782698.
Chen, G., Liu, S., Zhu, J., Gu, J., & Wang, Y. (2021). Machine learning-based prediction of cognitive decline using resting-state fmri. Frontiers in Aging Neuroscience, 13, 692102.
Clemens, B., Wagels, L., Bauchmüller, M., Bergs, R., Habel, U., & Kohn, N. (2017). Alerted default mode: Functional connectivity changes in the aftermath of social stress. Scientific Reports, 7(1), 1–9.
Craddock, R. C., Holtzheimer, P. E., III., Hu, X. P., & Mayberg, H. S. (2009). Disease state prediction from resting state functional connectivity. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 62(6), 1619–1628.
Cwiek, A., Rajtmajer, S. M., Wyble, B., Honavar, V., Grossner, E., & Hillary, F. G. (2022). Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics. Network Neuroscience, 6(1), 29–48.
PubMed PubMed Central Google Scholar
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1–22.
Du, Y., Fu, Z., Sui, J., Gao, S., Xing, Y., Lin, D., . . . others (2020). Neuromark: An automated and adaptive ica based pipeline to identify reproducible fmri markers of brain disorders. NeuroImage: Clinical, 28, 102375.
Du, Y., & Fan, Y. (2013). Group information guided ica for fmri data analysis. NeuroImage, 69, 157–197.
Dutt, R. K., Hannon, K., Easley, T. O., Griffis, J. C., Zhang, W., & Bijsterbosch, J. D. (2022). Mental health in the uk biobank: A roadmap to self-report measures and neuroimaging correlates. Human Brain Mapping, 43(2), 816–832.
Fraley, C., & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.
Article MathSciNet Google Scholar
Goulas, A., & Margulies, D. S. (2021). Resting-state fmri: A window into human brain plasticity. Neuroscience and Biobehavioral Reviews, 129, 38–55.
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825–841.
Kaiser, R. H., Andrews-Hanna, J. R., Wager, T. D., & Pizzagalli, D. A. (2015). Large-scale network dysfunction in major depressive disorder: A metaanalysis of resting-state functional connectivity. JAMA Psychiatry, 72(6), 603–611.
Article PubMed PubMed Central Google Scholar
Kawahara, J., Brown, C. J., Miller, S. P., Booth, B. G., Chau, V., Grunau, R. E., & Hamarneh, G. (2017). Brainnetcnn: Convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage, 146, 1038–1049.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprintarXiv:1412.6980
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Article ADS CAS PubMed Google Scholar
Li, X., Yang, Z., Zhang, Q., Wei, D., Zhang, Y., Liu, C., . . . Hu, D. (2021). Exploring the potential of deep learning on rs-fmri data for accurate diagnosis of mental disorders. NeuroImage: Clinical, 32, 102804.
Lin, Q.-H., Liu, J., Zheng, Y.-R., Liang, H., & Calhoun, V. D. (2010). Semiblind spatial ica of fmri using spatial constraints. Human Brain Mapping, 31(7), 1076–1088.
Liu, Z., Cui, Y., Du, Y., Gao, J., Yin, H., Sun, H., & Li, X. (2022). Exploring the topological organization of resting-state functional connectivity networks in schizophrenia. Schizophrenia Research, 238, 23–29.
McGee Ng, S. A., Bagby, R. M., Goodwin, B. E., Burchett, D., Sellbom, M., Ayearst, L. E., & Baker, S. (2016). The effect of response bias on the personality inventory for dsm-5 (pid-5). Journal of Personality Assessment, 98(1), 51–61.
Miller, K., Alfaro-Almagro, F., Bangerter, N., Thomas, D., Yacoub, E., Xu, J., & Smith, S. (2016). Multimodal population brain imaging in the uk biobank prospective epidemiological study. Nature Neuroscience, 19(11), 1523–1536.
Article CAS PubMed PubMed Central Google Scholar
Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th international conference on machine learning (icml-10) (pp. 807–814).
Riaz, A., Asad, M., Al Arif, S. M. R., Alonso, E., Dima, D., Corr, P., & Slabaugh, G. (2018). Deep fmri: An end-to-end deep network for classification of fmri data. 2018 ieee 15th international symposium on biomedical imaging (isbi 2018) (pp. 1419–1422).
Saba, T., Rehman, A., Shahzad, M. N., Latif, R., Bahaj, S. A., & Alyami, J. (2022). Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging. Microscopy Research and Technique, 85(6), 2083–2094.
Salimi-Khorshidi, G., Douaud, G., Beckmann, C. F., Glasser, M. F., Griffanti, L., & Smith, S. M. (2014). Automatic denoising of functional mri data: Combining independent component analysis and hierarchical fusion of classifiers. NeuroImage, 90, 449–468.
Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 461–464.
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-cam: Visual explanations from deep networks via gradientbased localization. Proceedings of the ieee international conference on computer vision (pp. 618–626).
Shen, X., Finn, E. S., Scheinost, D., Rosenberg, M. D., Chun, M. M., Papademetris, X., & Constable, R. T. (2017). Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols, 12(3), 506–518.
Article CAS PubMed PubMed Central Google Scholar
Smith, D. J., Nicholl, B. I., Cullen, B., Martin, D., Ul-Haq, Z., Evans, J., ... others (2013). Prevalence and characteristics of probable major depression and bipolar disorder within uk biobank: Cross-sectional study of 172,751 participants. PLoS ONE, 8(11), e75362.
Snyder, H. R. (2013). Major depressive disorder is associated with broad impairments on neuropsychological measures of executive function: A meta-analysis and review. Psychological Bulletin, 139(1), 81.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929–1958.
Su, C., Xu, Z., Pathak, J., & Wang, F. (2020). Deep learning in mental health outcome research: A scoping review. Translational Psychiatry, 10(1), 116.
Article PubMed PubMed Central Google Scholar
Uyulan, C., Ergüzel, T. T., Unubol, H., Cebi, M., Sayar, G. H., Nezhad Asad, M., & Tarhan, N. (2021). Major depressive disorder classification based on different convolutional neural network models: Deep learning approach. Clinical EEG and Neuroscience, 52(1), 38–51.
Wang, W., Peng, Z., Wang, X., Wang, P., Li, Q., Wang, G., ... Liu, S. (2019). Disrupted interhemispheric resting-state functional connectivity and structural connectivity in first-episode, treatment-naive generalized anxiety disorder. Journal of Affective Disorders, 251, 280–286.
Yoshida, K., Shimizu, Y., Yoshimoto, J., Takamura, M., Okada, G., Okamoto, Y., ... Doya, K. (2017). Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional mri data with partial least squares regression. PLoS ONE, 12(7), e0179638.
Zeng, L.-L., Shen, H., Liu, L., Wang, L., Li, B., Fang, P., ... Hu, D. (2012). Identifying major depression using whole-brain functional connectivity: A multivariate pattern analysis. Brain, 135(5), 1498–1507.
Zhang, J.-T., Yao, Y.-W., Li, C.-S.R., Zang, Y.-F., Shen, Z.-J., Liu, L., ... Fang, X.-Y. (2016). Altered resting-state functional connectivity of the insula in young adults with i nternet gaming disorder. Addiction Biology, 21(3), 743–751.
Zhang, W., Hashemi, M. M., Kaldewaij, R., Koch, S. B., Beckmann, C., Klumpers, F., & Roelofs, K. (2019). Acute stress alters the ‘default’ brain processing. NeuroImage, 189, 870–877.
Zhang, X., Huettel, S. A., O’Dhaniel, A., Guo, H., & Wang, L. (2019). Exploring common changes after acute mental stress and acute tryptophan depletion: Resting-state fmri studies. Journal of Psychiatric Research, 113, 172–180.
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. Proceedings of the ieee conference on computer vision and pattern recognition (pp. 2921–2929).
留言 (0)