A connectome-based approach to assess motor outcome after neonatal arterial ischemic stroke Article - 2021

Mariam Al Harrach, Pablo Pretzel, Samuel Groeschel, François Rousseau, Thijs Dhollander, Lucie Hertz-Pannier, Julien Lefevre, Stéphane Chabrier, Mickaël Dinomais

Mariam Al Harrach, Pablo Pretzel, Samuel Groeschel, François Rousseau, Thijs Dhollander, Lucie Hertz-Pannier, Julien Lefevre, Stéphane Chabrier, Mickaël Dinomais, « A connectome-based approach to assess motor outcome after neonatal arterial ischemic stroke  », Annals of Clinical and Translational Neurology, à paraître

Abstract

OBJECTIVE : Studies of motor outcome after Neonatal Arterial Ischemic Stroke (NAIS) often rely on lesion mapping using MRI. However, clinical measurements indicate that motor deficit can be different than what would solely be anticipated by the lesion extent and location. Because this may be explained by the cortical disconnections between motor areas due to necrosis following the stroke, the investigation of the motor network can help in the understanding of visual inspection and outcome discrepancy. In this study, we propose to examine the structural connectivity between motor areas in NAIS patients compared to healthy controls in order to define the cortical and subcortical connections that can reflect the motor outcome. METHODS : Thirty healthy controls and 32 NAIS patients with and without Cerebral Palsy (CP) underwent MRI acquisition and manual assessment. The connectome of all participants was obtained from T1-weighted and diffusion-weighted imaging. RESULTS : Significant disconnections in the lesioned and contra-lesioned hemispheres of patients were found. Furthermore, significant correlations were detected between the structural connectivity metric of specific motor areas and manuality assessed by the Box and Block Test (BBT) scores in patients. INTERPRETATION : Using the connectivity measures of these links, the BBT score can be estimated using a multiple linear regression model. In addition, the presence or not of CP can also be predicted using the KNN classification algorithm. According to our results, the structural connectome can be an asset in the estimation of gross manual dexterity and can help uncover structural changes between brain regions related to NAIS.

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