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@ARTICLE{Bonkhoff:905340,
      author       = {Bonkhoff, Anna K and Rehme, Anne K and Hensel, Lukas and
                      Tscherpel, Caroline and Volz, Lukas J and Espinoza, Flor A
                      and Gazula, Harshvardhan and Vergara, Victor M and Fink,
                      Gereon R and Calhoun, Vince D and Rost, Natalia S and
                      Grefkes, Christian},
      title        = {{D}ynamic connectivity predicts acute motor impairment and
                      recovery post-stroke},
      journal      = {Brain communications},
      volume       = {3},
      number       = {4},
      issn         = {2632-1297},
      address      = {[Großbritannien]},
      publisher    = {Guarantors of Brain},
      reportid     = {FZJ-2022-00608},
      pages        = {fcab227},
      year         = {2021},
      abstract     = {Thorough assessment of cerebral dysfunction after acute
                      lesions is paramount to optimize predicting clinical
                      outcomes. We herebuilt random forest classifier-based
                      prediction models of acute motor impairment and recovery
                      post-stroke. Predictions relied onstructural and
                      resting-state fMRI data from 54 stroke patients scanned
                      within the first days of symptom onset. Functional
                      connectivitywas estimated via static and dynamic approaches.
                      Motor performance was phenotyped in the acute phase and 6
                      months later.A model based on the time spent in specific
                      dynamic connectivity configurations achieved the best
                      discrimination between patientswith and without motor
                      impairments (out-of-sample area under the curve, $95\%$
                      confidence interval: 0.6760.01). In contrast,patients with
                      moderate-to-severe impairments could be differentiated from
                      patients with mild deficits using a model based on
                      thevariability of dynamic connectivity (0.8360.01). Here,
                      the variability of the connectivity between ipsilesional
                      sensorimotor cortexand putamen discriminated the most
                      between patients. Finally, motor recovery was best predicted
                      by the time spent in specific connectivityconfigurations
                      (0.8960.01) in combination with the initial impairment.
                      Here, better recovery was linked to a shortertime spent in a
                      functionally integrated configuration. Dynamic
                      connectivity-derived parameters constitute potent predictors
                      of acuteimpairment and recovery, which, in the future, might
                      inform personalized therapy regimens to promote stroke
                      recovery.},
      cin          = {INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34778761},
      UT           = {WOS:000804791900052},
      doi          = {10.1093/braincomms/fcab227},
      url          = {https://juser.fz-juelich.de/record/905340},
}