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@ARTICLE{Rubbert:862440,
      author       = {Rubbert, Christian and Mathys, Christian and Jockwitz,
                      Christiane and Hartmann, Christian J and Eickhoff, Simon and
                      Hoffstaedter, Felix and Caspers, Svenja and Eickhoff,
                      Claudia and Sigl, Benjamin and Teichert, Nikolas A and
                      Südmeyer, Martin and Turowski, Bernd and Schnitzler, Alfons
                      and Caspers, Julian},
      title        = {{M}achine-learning identifies parkinson's disease patients
                      based on resting-state between-network functional
                      connectivity},
      journal      = {The British journal of radiology},
      volume       = {92},
      number       = {1101},
      issn         = {1748-880X},
      address      = {London},
      publisher    = {Inst.},
      reportid     = {FZJ-2019-02752},
      pages        = {20180886},
      year         = {2019},
      note         = {Data were provided in part by the Human Connectome Project,
                      WU-Minn Consortium(Principal Investigators: David Van Essen
                      and Kamil Ugurbil; 1U54MH091657) funded by the16 NIH
                      Institutes and Centers that support the NIH Blueprint for
                      Neuroscience Research; andby the McDonnell Center for
                      Systems Neuroscience at Washington University.},
      abstract     = {OBJECTIVES:Evaluation of a data-driven, model-based
                      classification approach to discriminate idiopathic
                      Parkinson's disease (PD) patients from healthy controls (HC)
                      based on between-network connectivity in whole-brain
                      resting-state functional MRI (rs-fMRI).METHODS:Whole-brain
                      rs-fMRI (EPI/TR = 2.2  s/TE = 30  ms/flip angle =
                      90°/resolution = 3.1 × 3.1 × 3.1  mm/acquisition
                      time≈11  min) was assessed in 42 PD patients (medical
                      OFF) and 47 HC matched for age and gender. Between-network
                      connectivity based on full and L2-regularized partial
                      correlation measures were computed for each subject based on
                      canonical functional network architectures of two cohorts at
                      different levels of granularity (Human Connectome Project:
                      15/25/50/100/200 networks; 1000BRAINS: 15/25/50/70
                      networks). A Boosted Logistic Regression model was trained
                      on the correlation matrices using a nested cross-validation
                      (CV) with 10 outer and 10 inner folds for an unbiased
                      performance estimate, treating the canonical functional
                      network architecture and the type of correlation as
                      hyperparameters. The number of boosting iterations was fixed
                      at 100. The model with the highest mean accuracy over the
                      inner folds was trained using an non-nested 10- fold
                      20-repeats CV over the whole dataset to determine feature
                      importance.RESULTS:Over the outer folds the mean accuracy
                      was found to be 76.2 $\%$ (median $77.8\%,$ SD 18.2, IQR
                      69.4 - 87.1 $\%).$ Mean sensitivity was 81 $\%$ (median
                      $80\%,$ SD 21.1, IQR 75 - 100 $\%)$ and mean specificity was
                      72.7 $\%$ (median $75\%,$ SD 20.4, IQR 66.7 - 80 $\%).$ The
                      1000BRAINS 50-network-parcellation, using full correlations,
                      performed best over the inner folds. The top features
                      predominantly included sensorimotor as well as sensory
                      networks.CONCLUSIONS:A rs-fMRI whole-brain-connectivity,
                      data-driven, model-based approach to discriminate PD
                      patients from healthy controls shows a very good accuracy
                      and a high sensitivity. Given the high sensitivity of the
                      approach, it may be of use in a screening setting.ADVANCES
                      IN KNOWLEDGE:Resting-state functional MRI could prove to be
                      a valuable, non-invasive neuroimaging biomarker for
                      neurodegenerative diseases. The current model-based,
                      data-driven approach on whole-brain between-network
                      connectivity to discriminate Parkinson's disease patients
                      from healthy controls shows promising results with a very
                      good accuracy and a very high sensitivity.},
      cin          = {INM-7 / INM-1},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406 / I:(DE-Juel1)INM-1-20090406},
      pnm          = {571 - Connectivity and Activity (POF3-571)},
      pid          = {G:(DE-HGF)POF3-571},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30994036},
      UT           = {WOS:000482099400005},
      doi          = {10.1259/bjr.20180886},
      url          = {https://juser.fz-juelich.de/record/862440},
}