% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Sobczak:904389,
      author       = {Sobczak, Filip and Pais-Roldán, Patricia and Takahashi,
                      Kengo and Yu, Xin},
      title        = {{D}ecoding the brain state-dependent relationship between
                      pupil dynamics and resting state f{MRI} signal fluctuation},
      journal      = {eLife},
      volume       = {10},
      issn         = {2050-084X},
      address      = {Cambridge},
      publisher    = {eLife Sciences Publications},
      reportid     = {FZJ-2021-05959},
      pages        = {e68980},
      year         = {2021},
      abstract     = {Pupil dynamics serve as a physiological indicator of
                      cognitive processes and arousal states of the brain across a
                      diverse range of behavioral experiments. Pupil diameter
                      changes reflect brain state fluctuations driven by
                      neuromodulatory systems. Resting-state fMRI (rs-fMRI) has
                      been used to identify global patterns of neuronal
                      correlation with pupil diameter changes; however, the
                      linkage between distinct brain state-dependent activation
                      patterns of neuromodulatory nuclei with pupil dynamics
                      remains to be explored. Here, we identified four clusters of
                      trials with unique activity patterns related to pupil
                      diameter changes in anesthetized rat brains. Going beyond
                      the typical rs-fMRI correlation analysis with pupil
                      dynamics, we decomposed spatiotemporal patterns of rs-fMRI
                      with principal component analysis (PCA) and characterized
                      the cluster-specific pupil–fMRI relationships by
                      optimizing the PCA component weighting via decoding methods.
                      This work shows that pupil dynamics are tightly coupled with
                      different neuromodulatory centers in different trials,
                      presenting a novel PCA-based decoding method to study the
                      brain state-dependent pupil–fMRI relationship.},
      cin          = {INM-4},
      ddc          = {600},
      cid          = {I:(DE-Juel1)INM-4-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34463612},
      UT           = {WOS:000700424200001},
      doi          = {10.7554/eLife.68980},
      url          = {https://juser.fz-juelich.de/record/904389},
}