References
==========

Voxelwise modeling (VM) is a framework to perform functional magnetic resonance
imaging (fMRI) data analysis. Over the years, VM has led to many high profile
publications :ref:`[1]<kay2008>` :ref:`[2]<nas2009>` :ref:`[3]<nis2011>`
:ref:`[4]<hut2012>` :ref:`[5]<cuk2013>` :ref:`[6]<cuk2013b>`
:ref:`[7]<sta2013>` :ref:`[8]<hut2016>` :ref:`[9]<deh2017>`
:ref:`[10]<les2019>` :ref:`[11]<den2019>` :ref:`[12]<nun2019>`
:ref:`[13]<pop2021>` :ref:`[14]<leb2021>` :ref:`[15]<dup2022>`.


.. _kay2008:

[1] Kay, K. N., Naselaris, T., Prenger, R. J., & Gallant, J. L. (2008).
    Identifying natural images from human brain activity.
    Nature, 452(7185), 352-355.

.. _nas2009:

[2] Naselaris, T., Prenger, R. J., Kay, K. N., Oliver, M., & Gallant, J. L. (2009).
    Bayesian reconstruction of natural images from human brain activity.
    Neuron, 63(6), 902-915.

.. _nis2011:

[3] Nishimoto, S., Vu, A. T., Naselaris, T., Benjamini, Y., Yu, B., & Gallant, J. L. (2011).
    Reconstructing visual experiences from brain activity evoked by natural movies.
    Current Biology, 21(19), 1641-1646.

.. _hut2012:

[4] Huth, A. G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2012).
    A continuous semantic space describes the representation of thousands of
    object and action categories across the human brain.
    Neuron, 76(6), 1210-1224.

.. _cuk2013:

[5] Çukur, T., Nishimoto, S., Huth, A. G., & Gallant, J. L. (2013).
    Attention during natural vision warps semantic representation across the human brain.
    Nature neuroscience, 16(6), 763-770.

.. _cuk2013b:

[6] Çukur, T., Huth, A. G., Nishimoto, S., & Gallant, J. L. (2013).
    Functional subdomains within human FFA.
    Journal of Neuroscience, 33(42), 16748-16766.

.. _sta2013:

[7] Stansbury, D. E., Naselaris, T., & Gallant, J. L. (2013).
    Natural scene statistics account for the representation of scene categories
    in human visual cortex.
    Neuron, 79(5), 1025-1034

.. _hut2016:

[8] Huth, A. G., De Heer, W. A., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L. (2016).
    Natural speech reveals the semantic maps that tile human cerebral cortex.
    Nature, 532(7600), 453-458.

.. _deh2017:

[9] de Heer, W. A., Huth, A. G., Griffiths, T. L., Gallant, J. L., & Theunissen, F. E. (2017).
    The hierarchical cortical organization of human speech processing.
    Journal of Neuroscience, 37(27), 6539-6557.

.. _les2019:

[10] Lescroart, M. D., & Gallant, J. L. (2019).
    Human scene-selective areas represent 3D configurations of surfaces.
    Neuron, 101(1), 178-192.

.. _den2019:

[11] Deniz, F., Nunez-Elizalde, A. O., Huth, A. G., & Gallant, J. L. (2019).
    The representation of semantic information across human cerebral cortex
    during listening versus reading is invariant to stimulus modality.
    Journal of Neuroscience, 39(39), 7722-7736.

.. _nun2019:

[12] Nunez-Elizalde, A. O., Huth, A. G., & Gallant, J. L. (2019).
    Voxelwise encoding models with non-spherical multivariate normal priors.
    Neuroimage, 197, 482-492.

.. _pop2021:

[13] Popham, S. F., Huth, A. G., Bilenko, N. Y., Deniz, F., Gao, J. S.,
    Nunez-Elizalde, A. O., & Gallant, J. L. (2021).
    Visual and linguistic semantic representations are aligned at the border of
    human visual cortex.
    Nature Neuroscience, 24(11), 1628-1636.

.. _leb2021:

[14] LeBel, A., Jain, S., & Huth, A. G. (2021).
    Voxelwise encoding models show that cerebellar language representations are
    highly conceptual.
    Journal of Neuroscience, 41(50), 10341-10355.

.. _dup2022:

[15] Dupré La Tour, T., Eickenberg, M., Nunez-Elizalde, A.O., & Gallant, J. L. (2022).
    Feature-space selection with banded ridge regression.
    NeuroImage. https://doi.org/10.1016/j.neuroimage.2022.119728

Datasets
--------

.. _nis2011data:

[3b] Nishimoto, S., Vu, A. T., Naselaris, T., Benjamini, Y., Yu, B., & Gallant, J. L. (2014).
    Gallant Lab Natural Movie 4T fMRI Data.
    CRCNS.org. http://dx.doi.org/10.6080/K00Z715X

.. _hut2012data:

[4b] Huth, A. G., Nishimoto, S., Vu, A. T., Dupré la Tour, T., & Gallant, J. L. (2022).
    Gallant Lab Natural Short Clips 3T fMRI Data.
    GIN. http://dx.doi.org/10.12751/g-node.vy1zjd

Packages
--------

.. _dup2023:

[p1] Dupré La Tour, T., Visconti di Oleggio Castello, M., & Gallant, J. L. (2024).
   The Voxelwise Modeling framework: a tutorial introduction to fitting encoding models to fMRI data.
   https://doi.org/10.31234/osf.io/t975e

[p2] Dupré La Tour, T., Eickenberg, M., Nunez-Elizalde, A.O., & Gallant, J. L. (2022).
    Feature-space selection with banded ridge regression.
    NeuroImage. https://doi.org/10.1016/j.neuroimage.2022.119728

.. _gao2015:

[p3] Gao, J. S., Huth, A. G., Lescroart, M. D., & Gallant, J. L. (2015).
    Pycortex: an interactive surface visualizer for fMRI.
    Frontiers in Neuroinformatics, 23. https://doi.org/10.3389/fninf.2015.00023

.. _nun2021:

[p4] Nunez-Elizalde, A.O., Deniz, F., Dupré la Tour, T., Visconti di Oleggio Castello, M., and Gallant, J.L. (2021).
    pymoten: scientific python package for computing motion energy features from video.
    Zenodo. https://doi.org/10.5281/zenodo.6349625