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Related Experiment Video

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Post-stroke outcome prediction based on lesion-derived features.

Maedeh Khalilian1, Olivier Godefroy2, Martine Roussel1

  • 1Laboratory of Functional Neuroscience and Pathologies (UR UPJV 4559), University Research Center (CURS), University of Picardy Jules Verne, Amiens, France.

Neuroimage. Clinical
|February 6, 2025
PubMed
Summary

Structural disconnection maps and lesion masks effectively predict stroke-related motor, executive, and processing speed deficits. Thresholded disconnection maps significantly improved prediction accuracy, highlighting their utility in understanding stroke outcomes.

Keywords:
LesionNetwork connectivityStrokeStructural disconnection map

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Stroke causes focal damage and widespread network disruption, leading to functional impairments.
  • Predicting post-stroke deficits is crucial for patient management and rehabilitation.

Purpose of the Study:

  • To investigate if simulated network disruption measures from structural lesions can predict functional impairments in stroke patients.
  • To compare the predictive power of different lesion-derived features.

Main Methods:

  • Extracted lesion masks, probabilistic structural disconnection maps (pSDMs), connectivity strengths, and network topology from 340 stroke patients.
  • Employed PCA-based ridge regression with leave-one-out cross-validation to predict motor, executive, and processing speed deficits.
  • Applied probability thresholds to pSDMs to create thresholded SDMs (tSDMs).

Main Results:

  • Both lesion masks and pSDMs strongly predicted functional deficits.
  • Thresholded SDMs (tSDMs) significantly improved predictive performance compared to unthresholded pSDMs.
  • tSDMs achieved high R² values for motor (up to 0.95), executive (up to 0.58), and processing speed (up to 0.64) deficits, outperforming connectome-based features.

Conclusions:

  • Lesion masks and thresholded SDMs are effective tools for predicting post-stroke functional deficits.
  • Simulated structural networks offer a reliable, complementary approach to lesion patterns and structural disconnection for predicting stroke outcomes.