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@ARTICLE{Omidvarnia:1022086,
      author       = {Omidvarnia, Amir and Sasse, Leonard and Larabi, Daouia I.
                      and Raimondo, Federico and Hoffstaedter, Felix and Kasper,
                      Jan and Dukart, Juergen and Petersen, Marvin and Cheng,
                      Bastian and Thomalla, Götz and Eickhoff, Simon B. and
                      Patil, Kaustubh R.},
      title        = {{I}s resting state f{MRI} better than individual
                      characteristics at predicting cognition?},
      reportid     = {FZJ-2024-01223},
      year         = {2023},
      abstract     = {Resting state fMRI versus confounds for behavioral
                      phenotypic predictionIs resting state fMRI better than
                      individualcharacteristics at predicting cognition?Amir
                      Omidvarnia1,2*, Leonard Sasse1,2, Daouia I. Larabi1,2,
                      FedericoRaimondo1,2, Felix Hoffstaedter1,2, Jan Kasper1,2,
                      Juergen Dukart1,2,Marvin Petersen3, Bastian Cheng3, Götz
                      Thomalla3, Simon B. Eickhoff1,2,Kaustubh R.
                      Patil1,21Institute of Neuroscience and Medicine, Brain $\&$
                      Behavior (INM-7),Research Center Jülich,
                      Wilhelm-Johnen-Straße, Jülich, 52428, Germany2Institute of
                      Systems Neuroscience, Medical Faculty, Heinrich
                      HeineUniversity Düsseldorf, Moorenstr. 5, Düsseldorf,
                      40225, Germany3Klinik und Poliklinik für Neurologie, Kopf-
                      und Neurozentrum, UniversityMedical Center
                      Hamburg-Eppendorf, Hamburg, Germany*Corresponding author(s).
                      Email(s): a.omidvarnia@fz-juelich.deAbstractChanges in
                      spontaneous brain activity at rest provide rich
                      informationabout behavior and cognition. The mathematical
                      properties of resting-statefunctional magnetic resonance
                      imaging (rsfMRI) are a depiction of brainfunction and are
                      frequently used to predict cognitive phenotypes.Individual
                      characteristics such as age, gender, and total intracranial
                      volume(TIV) play an important role in predictive modeling of
                      rsfMRI (for example,as "confounders" in many cases). It is
                      unclear, however, to what extentrsfMRI carries independent
                      information from the individual characteristicsthat is able
                      to predict cognitive phenotypes. Here, we used
                      predictivemodeling to thoroughly examine the predictability
                      of four cognitivephenotypes in 20,000 healthy UK Biobank
                      subjects. We extracted commonrsfMRI features of functional
                      brain connectivity (FC) and temporalcomplexity (TC). We
                      assessed the ability of these features to predictoutcomes in
                      the presence and absence of age, gender, and TIV.
                      Additionally,we assessed the predictiveness of age, gender,
                      and TIV only. We find TC andFC features to perform
                      comparably with regard to predicting cognitivephenotypes. As
                      compared to rsfMRI features, individual
                      characteristicsprovide systematically better predictions
                      with smaller sample sizes and, tosome extent, in larger
                      cohorts. It is also consistent across different levels
                      ofinherent temporal noise in rsfMRI. Our results suggest
                      that when theobjective is to perform cognitive predictions
                      as opposed to understandingthe relationship between brain
                      and behavior, individual characteristics aremore applicable
                      than rsfMRI features.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525) / 5254 - Neuroscientific
                      Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253 / G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2023.02.18.529076},
      url          = {https://juser.fz-juelich.de/record/1022086},
}