See also my Google Scholar page.

  1. Richie-Halford, A.,Yeatman, J.D.,Simon, N., and Rokem, A. (2021). Multidimensional analysis and detection of informative features in diffusion MRI measurements of human white matter. PLoS Computational Biology, in press. link.
  2. Henriques, R., Correia, M., Maralle, M., Huber, E., Kruper, J., Koudoro, S., Yeatman, J. D., Garyfallidis, E., and Rokem, A. (2021). Diffusion Kurtosis Imaging in the Diffusion Imaging in Python project. Frontiers in Human Neuroscience, in press. link.
  3. Kruper, J., Yeatman, J. D., Richie-Halford, A., Bloom, D., Grotheer, M., Caffarra, S., Kiar, G., Karipidis, I. I., Roy, E., Chandio, B. Q., Garyfallidis, E., and Rokem, A (2021). Evaluating the reliability of human brain white matter tractometry. Aperture, in press. preprint.
  4. Cieslak, M., Cook, P. A., He, X., Yeh, F.-C., Dhollander, T., Adebimpe, A., Aguirre, G. K., Bassett, D. S., Betzel, R. F., Bourque, J., Cabral, L. M., Davatzikos, C., Detre, J., Earl, E., Elliott, M. A., Fadnavis, S., Fair, D. A., Foran, W., Fotiadis, P., Garyfallidis, E., Giesbrecht, B., Gur, R. C., Gur, R. E., Kelz, M., Keshavan, A., Larsen, B. S., Luna, B., Mackey, A. P., Milham, M., Oathes, D. J., Perrone, A., Pines, A. R., Roalf, D. R., Richie-Halford, A., Rokem, A, Sydnor, V. J., Tapera, T. M., Tooley, U. A, Vettel, J. M., Yeatman, J. D., Grafton, S. T., and Satterthwaite, T. D. (2021). QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI. Nature Methods, in press. pdf.
  5. Gau, R., Noble, S., Heuer, K., Bottenhorn, K.L., Bilgin, I.P., Yang, Y.-F. , Huntenburg, J. M., Bayer, J. M. M., Bethlehem, R. A. I., Rhoads, S. A. , Vogelbacher, C., Borghesani, V., Levitis, E., Wang, H.-T., Van Den Bossche, S., Kobeleva, X., Legarreta, J. H., Guay, S., Atay, S. M., Varoquaux, G. P., Huijser, D. C., Sandström, M. S., Herholz, P., Nastase, S. A., Badhwar, A., Dumas, G., Schwab, S., Moia, S., Dayan, M., Bassil, Y., Brooks, P. P., Mancini, M., Shine, J. M., O’Connor, D., Xie, X., Poggiali, D., Friedrich, P., Heinsfeld, A. S., Riedl, L., Toro, R., Caballero-Gaudes, C., Eklund, A., Garner, K. G., Nolan, C. R., Demeter, D. V., Barrios, F. A., Merchant, J. S., McDevitt, E. A., Oostenveld, R., Craddock, R. C., Rokem, A, Doyle, A., Ghosh, S. S., Nikolaidis, A., Stanley, O. W., Uruñuela, E., and The Brainhack Community (2021). Brainhack: Developing a culture of open inclusive, community-driven neuroscience. Neuron, 109. pdf.
  6. Mehta, P., Petersen, C. A., Wen, J. C., Bannit, M. R., Chen, P. P., Bojikian, K. D., Egan, C., Lee, S.- I., Balazinska, M., Lee, A. Y., and Rokem, A. (in press). Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images. American Journal of Ophthalmology preprint.
  7. A. Richie-Halford, M. Narayan, J. Yeatman, N. Simon, A Rokem (2021) Groupyr: Sparse Group Lasso in Python. Journal of Open Source Software, 6 (58), 3024. pdf, software
  8. A. Rokem , K. Kay (2020). Fractional ridge regression: a fast, interpretable reparameterization of ridge regression. GigaScience, GigaScience, Volume 9, Issue 12, December 2020, giaa133. pdf, software
  9. D. Bressler, A. Rokem, Silver, M. A. (2020). Slow endogenous fluctuations in cortical fMRI signals correlate with reduced performance in a visual detection task and are suppressed by spatial attention. Journal of Cognitive Neuroscience 32:85-99. full text.
  10. M. Beyeler, G.M. Boynton, I. Fine, A. Rokem (2019). Model-based recommendations for optimal surgical placement of epiretinal implants. The MICCAI 2019, Shenzhen, China, Oct. 13-17, 2019 preprint
  11. M. Beyeler, D. Nanduri, J.D. Weiland, A Rokem, G.M. Boynton and I. Fine (2019) A model of ganglion axon pathways accounts for percepts elicited by retinal implants. Scientific Reports 9: 9199. paper.
  12. J. Bain, J. Yeatman, R. Schurr, A. Rokem, and Mezer, A. (2019). Evaluating arcuate fasciculus laterality measurements across dataset and tractography pipelines. Human Brain Mapping. full text.
  13. C.S. Lee, A.J. Tyring, Y. Wu, S. Xiao, A. Rokem, N.P. Deruyter, Q. Zhang, A. Tufail, R.K. Wang, A.Y. Lee (2019). Generating retinal flow maps from structural optical coherence tomography with artificial intelligence. Scientific Reports 9:5694. paper.
  14. A. Keshavan, J. Yeatman, A. Rokem (2019). Combining citizen science and deep learning to amplify expertise in neuroimaging. Frontiers in Neuroinformatics 13:29. full text interactive version.
  15. Huber, E., Neto Henriques, R., Owen, J. O., Rokem, A., and Yeatman, J. (2019). Applying microstructural models to understand the role of white matter in cognitive development. Developmental Cognitive Neuroscience 36: 100624.
  16. Tian, Q., Yang, G., Leuze, C., Rokem, A., Edlow, B., and McNab, J. A. (2019). Generalized diffusion spectrum magnetic resonance imaging (GDSI) for model-free reconstruction of the ensemble average propagator. NeuroImage 189:497–515.
  17. Huppenkothen, D., Arendt, A., Hogg, D. W., Ram, K., VanderPlas, J. T., and Rokem, A (2018). Hack weeks as a model for data science education and collaboration. Proceedings of the National Academy of Sciences, 115(36):8872–8877. paper.
  18. A. Richie-Halford, A. Rokem (2018). Cloudknot: A Python Library to Run your Existing Code on AWS Batch. Proceedings of the 17th Python in Science Conference (SciPy 2018) F. Akici, D. Lippa, D. Niederhut, M. Pacer (Eds.). paper software
  19. A. Rokem (2018). A short course about fitting models with the scipy.optimize module. Journal of Open Source Education 1(2): 16. pdf.
  20. E. Huber, P.M. Donnelly, A. Rokem, J.D. Yeatman (2018). Rapid and widespread white matter plasticity during an intensive reading intervention. Nature Communications 9: 2260. pdf
  21. J.D. Yeatman, A. Richie-Halford, J.K. Smith, A. Keshavan, A. Rokem (2018). AFQ-Browser: Supporting reproducible human neuroscience research through browser-based visualization tools. Nature Communications 9: 940 pdf, software.
  22. A.M. Smith, K.E. Niemeyer, D.S. Katz, L.A. Barba, G. Githinji, M. Gymrek, K.D. Huff, C.R. Madan, A.C. Mayes, K.M. Moerman, P. Prins, K. Ram, A. Rokem, T.K. Teal, R.V. Guimera, J.T. Vanderplas (2018). Journal of Open Source Software (JOSS): design and first-year review. PeerJ Computer Science 4:e147 pdf.
  23. S. Xiao, F. Bucher, Y. Wu, A. Rokem, C.S. Lee, K.V. Marra, R. Fallon,6 S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A.Y. Lee (2017). Fully automated, deep learning segmentation of oxygen-induced retinopathy images. JCI Insight 2: e97585. pdf
  24. K. Polimis, A. Rokem, B. Hazelton (2017). Confidence Intervals for Random Forests in Python. Journal of Open Source Software, 2: 124. pdf software
  25. M. Beyeler, A. Rokem, G. Boynton, I. Fine (2017). Learning to see again: Biological constraints on cortical plasticity and the implications for sight restoration technologies. Journal of Neural Engineering preprint.
  26. C.S. Lee, A.J. Tyring, N.P. Deruyter, Y. Wu, A. Rokem, A.Y. Lee (2017). Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomedical Optics Express preprint, software.
  27. M. Beyeler, G. M. Boynton, I. Fine, A Rokem (2017). pulse2percept: A Python-based simulation framework for bionic vision. Proceedings of the 16th Python in Science Conference (SciPy 2018) K. Huff, D. Lippa, D. Niederhut, M. Pacer(Eds.) pdf, software.
  28. P. Mehta, S. Dorkenwald, D. Zhao, T. Kaftan, A. Cheung, M. Balazinska, A. Rokem, A. Connolly, J. Vanderplas, Y. AlSayyad (2017). Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads. Proceedings of the VLDB Endowment, Volume 10. preprint
  29. C. Holdgraf, A. Culich, A Rokem, F. Deniz, M. Alegro, D. Ushizima (2017) Portable learning environments for hands-on computational instruction: Using container- and cloud-based technology to teach data science. Practice & Experience in Advanced Research Computing (PEARC) 2017. preprint
  30. R. Neto Henriques, A. Rokem, E. Garyfallidis, S. St-Jean, E.T. Peterson, M. Correia (2017). [Re] Optimization of a free water elimination two-compartment model for diffusion tensor imaging. ReScience pdf
  31. M. Lee, R. Dottle, C. Espino, I. Subkhan, A. Rokem, A. Mashhadi (2017). A Tool for Estimating and Visualizing Poverty Maps. Proceedings of the Netmob Conference pdf.
  32. U. Ferizi, B. Scherrer, T. Schneider, M. Alipoor, O. Eufracio, R.H.J. Fick, R. Deriche, M. Nilsson, A.K. Loya-Olivas, M. Rivera, D.H.J. Poot, A. Ramirez-Manzanares, J.L. Marroquin, A. Rokem, C. Pötter, R.F. Dougherty, K. Sakaie, C. Wheeler-Kingshott, S.K. Warfield, T. Witzel, L.L. Wald, J.G. Raya, D.C. Alexander (2017). Diffusion MRI microstructure models with in vivo human brain Connectom data: results from a multi-group comparison. NMR in Biomedicine pdf, software.
  33. A. Rokem, H. Takemura, A. Bock, K.S. Scherf, M. Behrmann, B.A. Wandell, I. Fine, H. Bridge, F. Pestilli (2017). The visual white matter: The application of diffusion MRI and fiber tractography to vision science. Journal of Vision 17:4. pdf.
  34. K. Desimone, A. Rokem, K. Schneider (2016). Popeye: a population receptive field esimation tool. Journal of Open Source Software: 10.21105/joss.00103. Paper. Software.
  35. A. Ahmadia, J. Allen, P. Banaszkiewicz, E. Becker, T. Bekolay, J. Blischak, A. Boughton, E. Bray, A. Cabunoc Mayes, S Crouch, N. Davis, M. Davis, J. Duckles, R. Emonet, F.-A. Fortin, I. Gonzalez, C.Hamm, M. Hansen, R. Harris, F. Henninger, K. Hinsen, A. Hodge, M. Jackson, W. T. King, J. Kitzes, C. Koch, T. Liversidge, F. Michonneau, B. Mills, L. Nederbragt, A. O’Leary, E. Patitsas, A. Pawlik, F. Perez, J. Pipitone, T. Poisot, A. Rokem, R. Silva, T. Teal, T. Tröndle, F. Tweedie, J.-J. Vie, J. Walker, A. Walsh, B. Weaver, E. White, C. Wilkerson, J. Williams, G. Wilson, A. van der Walt (2016). Software carpentry: Instructor training. Link.
  36. A. Mezer, A. Rokem, S. Berman, T. Hastie, and B. A. Wandell (2016). Evaluating quantitative proton-density-mapping methods. Human Brain Mapping. pdf
  37. K. Gorgolewski, R. Auer, , V. Calhoun, C. Craddock, S. Das, E. Duff, G. Flandin, S. Ghosh, T. Glatard, Y. Halchenko, D. Handwerker, M. Hanke, D. Keator, X. Li, S. Michael, C. Maumet, N.B. Nichols, T. Nichols J. Pellman, J.B. Poline, A. Rokem, G. Schaefer, V. Sochat, W. Triplett, J. Turner, G. Varoquaux, R. Poldrack (2016). The Brain Imaging Data Structure, a format for organizing and describing outputs of neuroimaging experiments Scientific Data pdf.
  38. R.C. Craddock, D.S. Margulies, P. Bellec, B.N. Nichols, S. Alcauter, F.A. Barrios, Y. Burnod, C.J. Cannistraci, J. Cohen-Adad, B. De Leener, S. Dery, J. Downar, K. Dunlop, A.R. Franco, C. S. Froehlich, A.J. Gerber, S.S. Ghosh, T.J. Grabowski, S. Hill, A.S. Heinsfeld, R. M. Hutchison, P. Kundu, A.R. Laird, S.-L. Liew, D.J. Lurie, D.G. McLaren, F. Meneguzzi, M. Mennes, S. Mesmoudi, D. O’Connor, E.H. Pasaye, S. Peltier, J.-B. Poline, G. Prasad, R.F. Pereira, P.-O. Quirion, A. Rokem, Z.S. Saad, Y. Shi, S.C. Strother, R. Toro, L.Q. Uddin, J.D. Van Horn , J.W. Van Meter, R.C. Welsh, T. Xu (2016). Brainhack: a collaborative workshop for the open neuroscience community. Gigascience, 5:16. Paper
  39. Q. Tian, A. Rokem, R.D. Folkreth, A. Nummenmaa, F. Qiuyun, B.L. Edlow, J.A. McNab (2016). Postmortem diffusion spectrum imaging of whole, ex vivo human brains using 300 mT/m gradients: A study of effects of q-space sampling. Magnetic Resonance in Medicine pdf.
  40. S. Ajina, F. Pestilli, A. Rokem, C. Kennard, H. Bridge (2015). Human blindsight is mediated by an intact geniculo-extrastriate pathway. eLife 4:e08935, pdf.
  41. A. Rokem, C. Aragon, A. Arendt, B. Fiore-Gartland, B. Hazelton, J. Hellerstein, B. Herman, B. Howe, E. Lazowska, M. Parker, V. Staneva, S. Stone, A, Tanweer, J. Vanderplas (2015). Building an urban data science summer program at the University of Washington eScience Institute. The Bloomberg Data Science 4 Good Exchange pdf
  42. A. Rokem, J. Yeatman, F. Pestilli, K.N. Kay, A. Mezer, S. van der Walt, B.A. Wandell (2015). Evaluating the accuracy of diffusion MRI models in white matter. PLoS One, DOI: 10.1371/journal.pone.0123272. pdf.
  43. H. Takemura, A. Rokem, J. Winawer, J.D. Yeatman, B.A. Wandell, F. Pestilli (2015) A Major Human White Matter Pathway Between Dorsal and Ventral Visual Cortex. Cerebral Cortex, DOI: 10.1093/cercor/bhv064 pdf.
  44. B.A. Wandell, A. Rokem, L.M. Perry, G. Schaefer, R.F. Dougherty (2015). Data management to support reproducible research. arXiv 1502.06900. pdf
  45. C. Zheng, F. Pestilli, A. Rokem (2014). Quantifying error in estimates of human brain fiber directions using Earth Mover’s Distance. NIPS OTML workshop 2014. pdf.
  46. J. Yeatman, K.S. Weiner, F. Pestilli, A. Rokem, A. Mezer, B.A. Wandell (2014). The vertical occipital fasciculus: A century of controversy resolved by in vivo measurements. PNAS , 111:E5214–E5223. pdf.
  47. C. Zheng, F. Pestilli, A. Rokem (2014). Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain. Advances in Neural Information Processing 27 (NIPS 2014). pdf.
  48. F. Pestilli, J. Yeatman, A. Rokem, K. Kay, B.A. Wandell (2014). Evaluation and statistical inference for human connectomes. Nature Methods pdf , supplement
  49. E. Garyfallidis, M. Brett, B. Amirbekian, A. Rokem, S. van der Walt, M. Descoteaux, I. Nimmo-Smith and Dipy Contributors (2014). Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics 8:8. link
  50. K.N. Kay, A. Rokem, J. Winawer, R.F. Dougherty B.A Wandell (2013). GLMdenoise: a fast, automated technique for denoising task-based fMRI data. Frontiers in Brain Imaging Methods 7:247. link
  51. E.A. McDevitt*, A. Rokem*, M.A. Silver, and S.C. Mednick (2014). Sex differences in sleep-dependent perceptual learning. Vision Research 99:172-179. pdf (* equal contributors)
  52. J.H. Yoon, S.L. Sheremata, A. Rokem, and M.A. Silver (2013). Windows to the soul: vision science as a tool for studying biological mechanisms of information processing deficits in schizophrenia. Frontiers in Psychopathology. 4:681 pdf
  53. K.N. Kay, J. Winawer, A. Rokem, A. Mezer and B.A. Wandell (2013) A Two-Stage Cascade Model of BOLD Responses in Human Visual Cortex. PLoS Computational Biology 9: e1003079. pdf
  54. A. Rokem and M.A. Silver (2013). The benefits of cholinergic enhancement during perceptual learning are long-lasting. Frontiers in Computational Neuroscience: 7:66 link
  55. A.A. Kosovicheva, S.L. Sheremata, A. Rokem, A.N. Landau, and M.A. Silver(2012). Cholinergic enhancement reduces orientation-specific surround suppression but not visual crowding. Frontiers in Behavioral Neuroscience 6: 61. pdf
  56. A. Rokem, A. Landau, W. Prinzmetal, D. Wallace, M.A. Silver and M. D’Esposito (2012). Modulation of inhibition of return by the dopamine D2 receptor agonist bromocriptine depends on individual DAT1 genotype. Cerebral Cortex 22: 1133-8. pdf
  57. A. Rokem, J.H. Yoon, R.E. Ooms, R.J. Maddock, M. J. Minzenberg and M.A. Silver (2011). Broader visual orientation tuning in patients with schizophrenia. Frontiers in Human Neuroscience 5: 127 pdf
  58. A. Rokem and M.A. Silver (2010) Cholinergic enhancement augments magnitude and specificity of visual perceptual learning in healthy humans. Current Biology 20: 1723-1728 pdf; supplementary materials
  59. A. Rokem, A. N. Landau, D. Garg, W. Prinzmetal and M.A. Silver (2010). Effects of cholinergic enhancement on voluntary and involuntary visual spatia attention in healthy humans. Neuropsychopharmacology 35: 2538-44. pdf
  60. J.H. Yoon, R.J. Maddock, A. Rokem, M.A. Silver, M.J. Minzenberg, J.D. Ragland and C.S. Carter (2010). Gamma-aminobutyric acid concentration is reduced in visual cortex in schizophrenia and correlates with orientation-specific surround suppression. Journal of Neuroscience 30:3777-3781. pdf
  61. A. Rokem, M. Trumpis and F. Perez (2009). Nitime: time-series analysis for neuroimaging data. In Proceedings of the 8th Python in Science Conference (SciPy 2009) G. Varoquaux, S. van der Walt, J. Millman (Eds.) pdf
  62. J. Yoon, A. Rokem, M.A. Silver, M.J. Minzenberg and C.S. Carter (2009). Diminished Orientation-Specific Contextual Modulation of Visual Processing in Schizophrenia. Schizophrenia Bulletin 35: 1078-84 pdf
  63. A. Rokem and M.A. Silver (2009) A model of encoding and decoding in V1 and MT accounts accounts for motion perception anisotropies in the human visual system. Brain Research 1299: 3-16. pdf
  64. H. Eyherabide, A. Rokem, A.V.M. Herz, I. Samengo (2009) Bursts generate a non-reducible spike-pattern code. Frontiers in Neuroscience 3: 8-14. pdf
  65. A. Rokem and M. Ahissar (2009) Interactions of cognitive and auditory abilities in congenitally blind individuals. Neuropsychologia 47:843-8. pdf
  66. H.G. Eyherabide, A. Rokem, A.V.M. Herz and I. Samengo (2008). Burst Firing as a Neural Code in an Insect Auditory System. Frontiers in Computational Neuroscience 2:3. pdf
  67. A. Rokem, S. Watzl, T. Gollisch, M. Stemmler, A.V.M. Herz and I. Samengo (2006). Spike-Timing Precision Underlies the Coding Efficiency of Auditory Receptor Neurons. The Journal of Neurophysiology 95: 2541-2552. pdf