Global and Voxel-Wise Brain Age Prediction Analyses Following Perinatal Stroke

Perinatal stroke affects millions of individuals worldwide, often leading to lifelong complications for those who survive and who require targeted rehabilitation to limit disability. Although biological brain age prediction may be a valuable biomarker to analyze neurodevelopment following perinatal stroke and guide rehabilitation, there have been no prior studies exploring its utility. Therefore, in this work, we analyzed neurodevelopment in children with two forms of perinatal stroke, namely arterial ischemic stroke (AIS) and periventricular venous infarction (PVI), by using T1-weighted neuroimaging data and machine learning-based biological brain age prediction at a global and voxel level. Specifically, we analyzed trends in the brain age gap (BAG) at both global and voxel-wise levels in the contralesional hemisphere, alongside correlation analyses with motor scores in stroke cohorts. Global and voxel-wise biological brain age prediction machine learning models were developed and trained using 5969 T1-weighted MRI scans of typically-developing children (mean age: 12.11 ± 2.72 years). These trained models were then applied to T1-weighted MRI data from N = 105 subjects with perinatal stroke (mean age: 11.41 ± 3.27 years) and N = 105 age- and sex-matched controls. The Wilcoxon signed-rank test and Mann-Whitney U-test were used to identify differences in BAGs in the stroke vs. controls subgroups, and AIS vs. PVI subgroups, respectively. Spearman's correlation coefficient was used to identify relationships between BAGs and motor scores. Lastly, sex-specific analyses were performed to identify sexually dimorphic characteristics in the data. Children with perinatal stroke exhibited, on average, more positive global and voxel-wise BAGs, particularly those with AIS. Motor scores were negatively correlated with global and voxel-wise BAGs in the contralesional hemisphere. The correlations between BAGs and motor scores differed between the sexes. This is the first study to propose and explore voxel-wise brain age prediction for a pediatric cohort and the first to utilize brain age prediction to study perinatal stroke. Further development of these methods may reveal biomarkers that are valuable to study other pediatric diseases and promote personalized rehabilitation for affected individuals.