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@ARTICLE{Larabi:904684,
      author       = {Larabi, Daouia I. and Gell, Martin and Amico, Enrico and
                      Eickhoff, Simon B. and Patil, Kaustubh R.},
      title        = {{H}ighly accurate local functional fingerprints and their
                      stability},
      reportid     = {FZJ-2022-00033},
      year         = {2021},
      note         = {This work was supported by The Helmholtz Portfolio Theme
                      ‘Supercomputing and Modelling for the Human Brain’ and
                      the European Union's Horizon 2020 Research and Innovation
                      Programme (HBP SGA2; Grant No. 785907 to SBE).},
      abstract     = {The neural underpinnings of individual identity reflected
                      in cognition, behavior, and disease remain elusive.
                      Functional connectivity profiles have been used as a
                      “fingerprint” with which an individual can be identified
                      in a dataset. These established functional connectivity
                      fingerprints generally show high accuracy but are still
                      sensitive to mental states. A truly unique, and especially
                      state-independent, neural fingerprint will shed light on
                      fundamental intra-individual brain organization. Moreover, a
                      fingerprint that also captures inter-individual differences
                      in brain-behavior associations will provide the necessary
                      ingredients for the development of biomarkers for precision
                      medicine. With resting-state and task fMRI-data of the Human
                      Connectome Project and the enhanced Nathan Kline Institute
                      sample, we show that the local functional fingerprint, and
                      especially regional homogeneity (ReHo), is 1) a highly
                      accurate neural fingerprint, 2) more stable within an
                      individual regardless of their mental state (compared to the
                      baseline functional connectome fingerprint), and 3) captures
                      specific inter-individual differences. Our findings are
                      replicable across parcellations as well as resilient to
                      confounding effects. Further analyses showed that the
                      attention networks and the Default Mode Network contributed
                      most to individual “uniqueness”. Moreover, with the
                      OpenNeuro.ds000115 sample, we show that ReHo is also stable
                      in individuals with schizophrenia and that its stability
                      relates to intelligence subtest scores. Altogether, our
                      findings show the potential of the application of local
                      functional fingerprints in precision medicine.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      HBP SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2021.08.03.454862},
      url          = {https://juser.fz-juelich.de/record/904684},
}