This work is licensed under the Creative Commons Attribution 4.0 International License.
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
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
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
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
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
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
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
Zhang, J. (2018). Application of diffusion weighted imaging with background body signal suppression in brain neurography. NeuroQuantology, 16(3).Search in Google Scholar
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
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
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
Tang, Y., Chen, D., & Li, X. (2021). Dimensionality reduction methods for brain imaging data analysis. ACM Computing Surveys (CSUR).Search in Google Scholar
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
AtsushiKawaguchiFumioYamashita. (2017). Supervised multiblock sparse multivariable analysis with application to multimodal brain imaging genetics. Biostatistics, 18(4).Search in Google Scholar
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
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
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
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
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
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