Dynamic functional connectivity analysis reveals transiently increased segregation in patients with severe stroke

Anna K. Bonkhoff, Markus D. Schirmer, Martin Bretzner, Mark Etherton, Kathleen Donahue, Carissa Tuozzo, Marco Nardin, Anne Katrin Giese, Ona Wu, Vince Calhoun, Christian Grefkes, Natalia S. Rost

Research output: Contribution to journalArticlepeer-review

Abstract

Background and Purpose To explore the whole-brain dynamic functional network connectivity patterns in acute ischemic stroke (AIS) patients and their relation to stroke severity in the short and long term. Methods We investigated large-scale dynamic functional network connectivity of 41 AIS patients two to five days after symptom onset. Re-occurring dynamic connectivity configurations were obtained using a sliding window approach and k-means clustering. We evaluated differences in dynamic patterns between three NIHSS-stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we established correlation analyses between dynamic connectivity estimates and AIS severity as well as neurological recovery within the first 90 days after stroke (DNIHSS). Finally, we built Bayesian hierarchical models to predict acute ischemic stroke severity and examine the inter-relation of dynamic connectivity and clinical measures, with an emphasis on white matter hyperintensity lesion load. Results We identified three distinct dynamic connectivity configurations in the early post-acute stroke phase. More severely affected patients (NIHSS 10-21) spent significantly more time in a highly segregated dynamic connectivity configuration that was characterized by particularly strong connectivity (three-level ANOVA: p<0.05, post hoc t-tests: p<0.05, FDR-corrected for multiple comparisons). Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the acute dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson’s r=-0.68, p<0.05, FDR-corrected). Increasing dwell times, particularly those in a very segregated connectivity configuration, predicted higher acute stroke severity in our Bayesian modelling framework. Conclusions Our findings demonstrate transiently increased segregation between multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first three months post-stroke.

Original languageEnglish (US)
JournalUnknown Journal
DOIs
StatePublished - Jun 3 2020

Keywords

  • Bayesian hierarchical modelling
  • Dynamic functional network connectivity
  • Ischemic stroke
  • Stroke recovery
  • Stroke severity

ASJC Scopus subject areas

  • Medicine(all)

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