Structural connectome disruption at baseline predicts 6-months post-stroke outcome Academic Article uri icon


MeSH Major

  • Brain Ischemia
  • Cerebral Hemorrhage
  • Hypertension
  • Hypertensive Encephalopathy
  • Stroke


  • In this study, models based on quantitative imaging biomarkers of post-stroke structural connectome disruption were used to predict six-month outcomes in various domains. Demographic information and clinical MRIs were collected from 40 ischemic stroke subjects (age: 68.1 ± 13.2 years, 17 female, NIHSS: 6.8 ± 5.6). Diffusion-weighted images were used to create lesion masks, which were uploaded to the Network Modification (NeMo) Tool. The NeMo Tool, using only clinical MRIs, allows estimation of connectome disruption at three levels: whole brain, individual gray matter regions and between pairs of gray matter regions. Partial Least Squares Regression models were constructed for each level of connectome disruption and for each of the three six-month outcomes: applied cognitive, basic mobility and daily activity. Models based on lesion volume were created for comparison. Cross-validation, bootstrapping and multiple comparisons corrections were implemented to minimize over-fitting and Type I errors. The regional disconnection model best predicted applied cognitive (R(2)  = 0.56) and basic mobility outcomes (R(2)  = 0.70), while the pairwise disconnection model best predicted the daily activity measure (R(2)  = 0.72). These results demonstrate that models based on connectome disruption metrics were more accurate than ones based on lesion volume and that increasing anatomical specificity of disconnection metrics does not always increase model accuracy, likely due to statistical adjustments for concomitant increases in data dimensionality. This work establishes that the NeMo Tool's measures of baseline connectome disruption, acquired using only routinely collected MRI scans, can predict 6-month post-stroke outcomes in various functional domains including cognition, motor function and daily activities. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

publication date

  • July 2016



  • Academic Article



  • eng

PubMed Central ID

  • PMC4905801

Digital Object Identifier (DOI)

  • 10.1002/hbm.23198

PubMed ID

  • 27016287

Additional Document Info

start page

  • 2587

end page

  • 601


  • 37


  • 7