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@INBOOK{Genon:1019297,
      author       = {Genon, Sarah and Li, Jingwei},
      title        = {3 - {B}rain networks atlases},
      address      = {Cambridge, Massachusetts},
      publisher    = {Academic Press},
      reportid     = {FZJ-2023-05273},
      pages        = {59-85},
      year         = {2023},
      comment      = {Advances in Resting-State Functional MRI: Methods,
                      Interpretation, and Applications},
      booktitle     = {Advances in Resting-State Functional
                       MRI: Methods, Interpretation, and
                       Applications},
      abstract     = {The human brain consists of multiple areas and networks
                      with distinct functions. To better understand the functional
                      organization of human brain, methods including independent
                      component analysis and graph theory have been applied to
                      resting-state fMRI (rs-fMRI) data to delineate functional
                      networks and parcellate the brain. An important discovery
                      that motivated the study of brain networks with rs-fMRI was
                      the so-called default mode network, referring to a set of
                      regions that tend to deactivate in response to a wide range
                      of goal-directed task conditions, and which was also
                      observed by decomposing rs-fMRI data. Following upon studies
                      that extracted additional, core brain networks from rs-fMRI,
                      several functional atlases were developed by partitioning
                      the brain into different numbers of regions or networks.
                      From these predefined brain atlases, rs-fMRI features can be
                      extracted for a range of applications, such as to study
                      functional organization across development and aging and for
                      predicting behavior from functional connectivity in healthy
                      populations. In clinical applications, brain atlases have
                      been used to facilitate the prediction of disease symptoms
                      and treatment outcomes, as well as to investigate
                      dysfunctions in patients. Nevertheless, several challenges
                      remain in building and applying brain atlases, in particular
                      with regard to interindividual variability, a topic that
                      will likely remain under investigation in the future.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)7},
      doi          = {10.1016/B978-0-323-91688-2.00001-1},
      url          = {https://juser.fz-juelich.de/record/1019297},
}