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

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.

Keywords: MRI; Neonatal arterial ischemic stroke; box and block test; cerebral palsy; connectome; diffusion weighted imaging; structural connectivity.

Figure 1

Overview of the methodology. The creation of the structural connectivity matrix consists of different steps. These steps include the processing of T1‐weighted images (second row) with FreeSurfer and FSL as well as diffusion‐weighted images with MRtrix3 (first row). The obtained connectivity matrix consists of 379 × 379 connections weights.

Figure 2

General process of connection selection. (A) Extracting the motor SC matrix from the whole brain 379 × 379 matrix. With 24 motor areas in each hemisphere 52 nodes were obtained. (B) The mean motor SC for the control group. (C) The connections of interest were chosen for this study. (D) Illustration of the motor connectome for the left hemisphere.

Figure 3

Circular representation of the significantly different structural connectivity tracts between patients (LLP and RLP) and controls for the different motor areas defined in Table 2.