Predictive analysis of brain imaging data based on deep learning algorithms

[1] Turner, B. M., Forstmann, B. U., Love, B. C., Palmeri, T. J., & Maanen, L. V. (2017). Approaches to analysis in model-based cognitive neuroscience. Journal of Mathematical Psychology, 76, 65-79. Search in Google Scholar

[2] Gilmore, R. O., Diaz, M. T., Wyble, B. A., & Yarkoni, T. (2017). Progress toward openness, transparency, and reproducibility in cognitive neuroscience. Annals of the New York Academy of ences, 1396(1). Search in Google Scholar

[3] Metzger, F. G., Ehlis, A. C., Haeussinger, F. B., Schneeweiss, P., Hudak, J., & Fallgatter, A. J., et al. (2017). Functional brain imaging of walking while talking – an fnirs study. Neuroence, 343, 85. Search in Google Scholar

[4] Paol, P., Ilias, T., Antonia, H., Joy, H., Clarisse, A., & Sam, G., et al. (2018). The present and future use of functional near‐infrared spectroscopy (fnirs) for cognitive neuroscience. Annals of the New York Academy of Sciences. Search in Google Scholar

[5] Bovetti, S., Moretti, C., Zucca, S., Dal Maschio, M., Bonifazi, P., & Fellin, T. (2017). Simultaneous high-speed imaging and optogenetic inhibition in the intact mouse brain. Scientific Reports, 7, 40041. Search in Google Scholar

[6] David, R., & Ravi, N. (2017). 89. glutamate imaging (glucest) reveals lower brain glucest contrast in patients on the psychosis spectrum. Schizophrenia Bulletin(suppl_1), S49-S49. Search in Google Scholar

[7] Ullah, Z., Farooq, M. U., Lee, S. H., & An, D. (2020). A hybrid image enhancement based brain mri images classification technique. Medical Hypotheses, 143, 109922. Search in Google Scholar

[8] Zhang, J. (2018). Application of diffusion weighted imaging with background body signal suppression in brain neurography. NeuroQuantology, 16(3). Search in Google Scholar

[9] George, J., Green, T., O’Brien, H., Dolores Vazquezloganroman, & Gignac, P. (2021). Detectability of rat brain structures using dicect imaging as compared to traditional atlas visualizations. The FASEB Journal, 35(S1). Search in Google Scholar

[10] A, H. S., B, H. H., B, T. F., & B, K. I. (2021). Classification of type of brain magnetic resonance images with deep learning technique. Magnetic Resonance Imaging, 77, 180-185. Search in Google Scholar

[11] Arizono, M., Stéphane Bancelin, Bethge, P., Ronan Chéreau, & Ngerl, U. V. (2021). Nanoscale imaging of the functional anatomy of the brain. Neuroforum. Search in Google Scholar

[12] Tang, Y., Chen, D., & Li, X. (2021). Dimensionality reduction methods for brain imaging data analysis. ACM Computing Surveys (CSUR). Search in Google Scholar

[13] Glaab, E., Trezzi, J. P., Greuel, A., Jäger, Christian, Hodak, Z., & Drzezga, A., et al. (2019). Integrative analysis of blood metabolomics and pet brain neuroimaging data for parkinson’s disease. Neurobiology of Disease. Search in Google Scholar

[14] AtsushiKawaguchiFumioYamashita. (2017). Supervised multiblock sparse multivariable analysis with application to multimodal brain imaging genetics. Biostatistics, 18(4). Search in Google Scholar

[15] Gurler, Z., & Rekik, I. (2022). Federated brain graph evolution prediction using decentralized connectivity datasets with temporally-varying acquisitions. IEEE transactions on medical imaging, P.P. Search in Google Scholar

[16] Ning, K., Chen, B., Sun, F., Hobel, Z., Zhao, L., & Matloff, W., et al. (2018). Classifying alzheimer’s disease with brain imaging and genetic data using a neural network framework. Neurobiology of Aging, S0197458018301313. Search in Google Scholar

[17] Menzel, M., Reuter, J. A., Grel, D., Huwer, M., & Axer, M. (2021). Scattered light imaging: resolving the substructure of nerve fiber crossings in whole brain sections with micrometer resolution. NeuroImage, 233(1), 117952. Search in Google Scholar

[18] Ben, Ewell, Urban, Lei, Xiao, & Siyu, et al. (2017). In vivosuperresolution imaging of neuronal structure in the mouse brain. IEEE Transactions on Biomedical Engineering. Search in Google Scholar

[19] Vranic, J. E., Cross, N. M., Wang, Y., Hippe, D. S., De Weerdt, E., & Mossa-Basha, M. (2018). Compressed sensing–sensitivity encoding (cs-sense) accelerated brain imaging: reduced scan time without reduced image quality. American Journal of Neuroradiology. Search in Google Scholar

[20] Alexander, D. C., Dyrby, T. B., Nilsson, M., & Zhang, H. (2017). Imaging brain microstructure with diffusion mri: practicality and applications. Nmr in Biomedicine, 32(4). Search in Google Scholar