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Structural connectome disruption at baseline predicts 6-months post-stroke outcome.

Amy Kuceyeski1,2, Babak B Navi2,3, Hooman Kamel2,3

  • 1Department of Radiology, Weill Cornell Medical College, New York, New York.

Human Brain Mapping
|March 27, 2016
PubMed
Summary

Predicting stroke recovery is possible using brain imaging. New models analyzing brain network disruption accurately forecast six-month outcomes in cognition, mobility, and daily activities after stroke.

Keywords:
connectomeimaging biomarkersmagnetic resonance imagingoutcome assessmentstatistical modelingstroke

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Biomarkers

Background:

  • Stroke significantly disrupts structural brain networks, impacting functional recovery.
  • Predicting post-stroke functional outcomes is crucial for personalized rehabilitation strategies.

Purpose of the Study:

  • To evaluate the efficacy of quantitative imaging biomarkers of post-stroke connectome disruption in predicting six-month functional outcomes.
  • To compare the predictive accuracy of connectome disruption models with traditional lesion volume models.

Main Methods:

  • Utilized diffusion-weighted MRI to create lesion masks for 40 ischemic stroke subjects.
  • Employed the Network Modification (NeMo) Tool to quantify connectome disruption at whole-brain, regional, and pairwise levels.
  • Developed Partial Least Squares Regression models to predict cognitive, mobility, and daily activity outcomes at six months post-stroke.

Main Results:

  • Connectome disruption models demonstrated higher accuracy than lesion volume models.
  • The regional disconnection model best predicted applied cognitive (R²=0.56) and basic mobility (R²=0.70) outcomes.
  • The pairwise disconnection model showed the highest accuracy for predicting daily activity (R²=0.72).

Conclusions:

  • Quantitative imaging biomarkers of baseline connectome disruption can reliably predict six-month post-stroke functional outcomes.
  • The NeMo Tool, using routine MRI, offers a valuable approach for assessing stroke recovery potential.
  • Model accuracy is influenced by the anatomical specificity of disconnection metrics and data dimensionality.