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@ARTICLE{Akdeniz:282908,
      author       = {Akdeniz, Aslı and Ríos, Ana Sofía and Temuulen, Uchralt
                      and Fiebach, Jochen B and Villringer, Kersten and Ali, Huma
                      Fatima and Khalil, Ahmed and Grittner, Ulrike and Liman,
                      Thomas G. and Endres, Matthias and Kufner, Anna},
      title        = {{L}esion {N}etwork {M}apping of {A}cute {N}eurological
                      {D}eficits and {I}ts {P}rognostic {V}alue {A}fter {I}schemic
                      {S}troke.},
      journal      = {NeuroImage: Clinical},
      volume       = {48},
      issn         = {2213-1582},
      address      = {[Amsterdam u.a.]},
      publisher    = {Elsevier},
      reportid     = {DZNE-2025-01369},
      pages        = {103895},
      year         = {2025},
      abstract     = {Predicting functional recovery after ischemic stroke is
                      vital for guiding clinical care. This study investigated
                      whether lesion network mapping (LNM), a technique for
                      modeling symptom-specific brain networks, can improve
                      outcome prediction of functional recovery up to one-year
                      post-stroke.We pooled data from two prospective stroke
                      cohorts (1000Plus and PROSCIS-B; N = 565). Seven
                      NIHSS-derived symptom networks were generated using LNM
                      based on NIHSS sub-scores on admission (i.e., consciousness,
                      language, motor, sensory, vision, neglect and ataxia).
                      Lesion masks derived from MRI (within 7 days) were
                      intersected with each symptom network to calculate
                      individual network damage scores. Functional outcome was
                      defined by the modified Rankin Scale (mRS) at 3 months
                      (1000Plus) or 12 months (PROSCIS-B). Ordinal logistic
                      regression models were performed to evaluate additional
                      predictive value of LNM: Model 1 included age, lesion
                      volume, and presence of selected neurological deficits;
                      Model 2 included age, lesion volume, and NIHSS-derived
                      network damage scores. Models were compared using pseudo-R2
                      and AIC.Patients had a mean age of 68 years and a median
                      NIHSS of 3 (IQR 1-5). LNM revealed distinct,
                      symptom-specific networks, with corresponding damage scores
                      that were higher in patients exhibiting the respective
                      deficits compared to those without. However, inclusion of
                      these scores did not enhance the predictive accuracy of
                      functional outcomes beyond that achieved with clinical
                      variables alone (Model 1 vs. Model 2: pseudo-R2: 0.0468 vs.
                      0.0159; AIC:1730.598 vs. 1769.222).LNM-derived scores
                      reflected symptom topography but did not enhance prediction
                      of functional recovery. While promising as a mechanistic
                      tool, the clinical utility of LNM-based damage metrics for
                      prognostication remains limited and requires further
                      validation.},
      keywords     = {Humans / Male / Female / Ischemic Stroke: diagnostic
                      imaging / Ischemic Stroke: physiopathology / Ischemic
                      Stroke: complications / Ischemic Stroke: pathology / Aged /
                      Middle Aged / Prognosis / Magnetic Resonance Imaging:
                      methods / Recovery of Function: physiology / Nerve Net:
                      diagnostic imaging / Nerve Net: pathology / Nerve Net:
                      physiopathology / Prospective Studies / Aged, 80 and over /
                      Brain: diagnostic imaging / Brain: pathology / Connectome
                      (Other) / Functional outcome (Other) / Ischemic stroke
                      (Other) / Lesion-network mapping (Other) / Stroke severity
                      (Other)},
      cin          = {AG Endres},
      ddc          = {610},
      cid          = {I:(DE-2719)1811005},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41176879},
      pmc          = {pmc:PMC12621467},
      doi          = {10.1016/j.nicl.2025.103895},
      url          = {https://pub.dzne.de/record/282908},
}