Anderson, T. J. & MacAskill, M. R. Eye movements in patients with neurodegenerative disorders. Nat. Rev. Neurol. 9, 74–85 (2013).
Morrone, M. C., Ross, J. & Burr, D. Saccadic eye movements cause compression of time as well as space. Nat. Neurosci. 8, 950–954 (2005).
Berman, R. A. et al. Cortical networks subserving pursuit and saccadic eye movements in humans: an fMRI study. Hum. Brain Mapp. 8, 209–225 (1999).
Petit, L. & Haxby, J. V. Functional anatomy of pursuit eye movements in humans as revealed by fMRI. J. Neurophysiol. 82, 463–471 (1999).
McNabb, C. B. et al. Inter-slice leakage and intra-slice aliasing in simultaneous multi-slice echo-planar images. Brain Struct. Funct. 225, 1153–1158 (2020).
Voss, J. L., Bridge, D. J., Cohen, N. J. & Walker, J. A. A closer look at the hippocampus and memory. Trends Cogn. Sci. 21, 577–588 (2017).
Tregellas, J. R., Tanabe, J. L., Miller, D. E. & Freedman, R. Monitoring eye movements during fMRI tasks with echo planar images. Hum. Brain Mapp. 17, 237–243 (2002).
Beauchamp, M. S. Detection of eye movements from fMRI data. Magn. Reson. Med. 49, 376–380 (2003).
Heberlein, K., Hu, X., Peltier, S. & LaConte, S. Predictive eye estimation regression (PEER) for simultaneous eye tracking and fMRI. In Proc. 14th Scientific Meeting, International Society for Magnetic Resonance in Medicine 14, 2808 (2006).
Son, J. et al. Evaluating fMRI-based estimation of eye gaze during naturalistic viewing. Cereb. Cortex 30, 1171–1184 (2020).
Alexander, L. M. et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci. Data 4, 170181 (2017).
Nau, M., Schindler, A. & Bartels, A. Real-motion signals in human early visual cortex. Neuroimage 175, 379–387 (2018).
Polti, I., Nau, M., Kaplan, R., van Wassenhove, V. & Doeller, C. F. Hippocampus and striatum encode distinct task regularities that guide human timing behavior. Preprint at bioRxiv https://doi.org/10.1101/2021.08.03.454928 (2021).
Nau, M., Navarro Schröder, T., Bellmund, J. L. & Doeller, C. F. Hexadirectional coding of visual space in human entorhinal cortex. Nat. Neurosci. 21, 188–190 (2018).
Julian, J. B., Keinath, A. T., Frazzetta, G. & Epstein, R. A. Human entorhinal cortex represents visual space using a boundary-anchored grid. Nat. Neurosci. 21, 191–194 (2018).
Ehinger, K. A., Hidalgo-Sotelo, B., Torralba, A. & Oliva, A. Modelling search for people in 900 scenes: a combined source model of eye guidance. Vis. Cogn. 17, 945–978 (2009).
Wolfe, J. M. Visual search: how do we find what we are looking for? Annu. Rev. Vis. Sci. 6, 539–562 (2020).
Hebart, M. N. et al. THINGS: a database of 1,854 object concepts and more than 26,000 naturalistic object images. PLoS ONE 14, e0223792 (2019).
Duchowski, A. T Eye Tracking Methodology: Theory and Practice 3rd edn (Springer International Publishing, 2017).
Brodoehl, S., Witte, O. W. & Klingner, C. M. Measuring eye states in functional MRI. BMC Neurosci. 17, 48 (2016).
Coiner, B. et al. Functional neuroanatomy of the human eye movement network: a review and atlas. Brain Struct. Funct. 224, 2603–2617 (2019).
Keck, I. R., Fischer, V., Puntonet, C. G. & Lang, E. W. Eye Movement Quantification in Functional MRI Data by Spatial Independent Component Analysis. In International Conference on Independent Component Analysis and Signal Separation Vol. 5441 (eds Adali, T., Jutten, C., Romano, J. M. T. & Barros, A. K.) 435-442 (Springer Berlin Heidelberg, 2009).
Franceschiello, B. et al. 3-Dimensional magnetic resonance imaging of the freely moving human eye. Prog. Neurobiol. 194, 101885 (2020).
LaConte, S. M. & Glielmi, C. B. Verifying visual fixation to improve fMRI with predictive eye estimation regression (PEER). In Proc. 15th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Berlin 3438 (2007).
Sathian, K. et al. Dual pathways for haptic and visual perception of spatial and texture information. Neuroimage 57, 462–475 (2011).
O’Connell, T. P. & Chun, M. M. Predicting eye movement patterns from fMRI responses to natural scenes. Nat. Commun. 9, 5159 (2018).
Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16, 111–116 (2019).
Tagliazucchi, E. & Laufs, H. Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep. Neuron 82, 695–708 (2014).
Naselaris, T., Kay, K. N., Nishimoto, S. & Gallant, J. L. Encoding and decoding in fMRI. Neuroimage 56, 400–410 (2011).
Kriegeskorte, N. & Douglas, P. K. Interpreting encoding and decoding models. Curr.Opin. Neurobiol. 55, 167–179 (2019).
Sonkusare, S., Breakspear, M. & Guo, C. Naturalistic stimuli in neuroscience: critically acclaimed. Trends Cogn. Sci. 23, 699–714 (2019).
Lim, S.-L., O’Doherty, J. P. & Rangel, A. The decision value computations in the vmPFC and striatum use a relative value code that is guided by visual attention. J. Neurosci. 31, 13214–13223 (2011).
Koba, C., Notaro, G., Tamm, S., Nilsonne, G. & Hasson, U. Spontaneous eye movements during eyes-open rest reduce resting-state-network modularity by increasing visual-sensorimotor connectivity. Netw. Neurosci. 5, 451–476 (2021).
Murphy, K., Birn, R. M. & Bandettini, P. A. Resting-state fMRI confounds and cleanup. Neuroimage 80, 349–359 (2013).
Frey, M. et al. Interpreting wide-band neural activity using convolutional neural networks. eLife 10, e66551 (2021).
Shen, D., Wu, G. & Suk, H. I. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221–248 (2017).
Misra, D. Mish: a self regularized non-monotonic neural activation function. Preprint available at https://arxiv.org/abs/1908.08681 (2019).
Biewald, L. Experiment tracking with weights & biases. http://wandb.com/ (2020).
Kingma, D. P. & Ba, J. L. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).