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@ARTICLE{Varikuti:844359,
      author       = {Varikuti, Deepthi and Genon, Sarah and Sotiras, Aristeidis
                      and Schwender, Holger and Hoffstaedter, Felix and Patil,
                      Kaustubh and Jockwitz, Christiane and Caspers, Svenja and
                      Moebus, Susanne and Amunts, Katrin and Davatzikos, Christos
                      and Eickhoff, Simon},
      title        = {{E}valuation of non-negative matrix factorization of grey
                      matter in age prediction},
      journal      = {NeuroImage},
      volume       = {173},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2018-01790},
      pages        = {394-410},
      year         = {2018},
      abstract     = {The relationship between grey matter volume (GMV) patterns
                      and age can be captured by multivariate pattern analysis,
                      allowing prediction of individuals' age based on structural
                      imaging. Raw data, voxel-wise GMV and non-sparse
                      factorization (with Principal Component Analysis, PCA) show
                      good performance but do not promote relatively localized
                      brain components for post-hoc examinations. Here we
                      evaluated a non-negative matrix factorization (NNMF)
                      approach to provide a reduced, but also interpretable
                      representation of GMV data in age prediction frameworks in
                      healthy and clinical populations.This examination was
                      performed using three datasets: a multi-site cohort of
                      life-span healthy adults, a single site cohort of older
                      adults and clinical samples from the ADNI dataset with
                      healthy subjects, participants with Mild Cognitive
                      Impairment and patients with Alzheimer's disease (AD)
                      subsamples. T1-weighted images were preprocessed with VBM8
                      standard settings to compute GMV values after normalization,
                      segmentation and modulation for non-linear transformations
                      only. Non-negative matrix factorization was computed on the
                      GM voxel-wise values for a range of granularities (50–690
                      components) and LASSO (Least Absolute Shrinkage and
                      Selection Operator) regression were used for age prediction.
                      First, we compared the performance of our data compression
                      procedure (i.e., NNMF) to various other approaches (i.e.,
                      uncompressed VBM data, PCA-based factorization and
                      parcellation-based compression). We then investigated the
                      impact of the granularity on the accuracy of age prediction,
                      as well as the transferability of the factorization and
                      model generalization across datasets. We finally validated
                      our framework by examining age prediction in ADNI samples.},
      cin          = {INM-1 / INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-1-20090406 / I:(DE-Juel1)INM-7-20090406},
      pnm          = {571 - Connectivity and Activity (POF3-571) / HBP SGA1 -
                      Human Brain Project Specific Grant Agreement 1 (720270) /
                      SMHB - Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017)},
      pid          = {G:(DE-HGF)POF3-571 / G:(EU-Grant)720270 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29518572},
      UT           = {WOS:000430366000033},
      doi          = {10.1016/j.neuroimage.2018.03.007},
      url          = {https://juser.fz-juelich.de/record/844359},
}