AbstractWe explored structural brain connectomes in children with spastic unilateral cerebral palsy (uCP) and its relation to sensory‐motor function using graph theory. In 46 children with uCP (mean age = 10 years 7 months ± 2 years 9 months; Manual Ability Classification System I = 15, II = 16, III = 15) we assessed upper limb somatosensory and motor function. We collected multi‐shell diffusion‐weighted, T1‐weighted and T2‐FLAIR MRI and identified the corticospinal tract (CST) wiring pattern using transcranial magnetic stimulation. Structural connectomes were constructed using Virtual Brain Grafting‐modified FreeSurfer parcellations and multi‐shell multi‐tissue constrained spherical deconvolution‐based anatomically‐constrained tractography. Graph metrics (characteristic path length, global/local efficiency and clustering coefficient) of the whole brain, the ipsilesional/contralesional hemisphere, and the full/ipsilesional/contralesional sensory‐motor network were compared between lesion types (periventricular white matter (PWM) = 28, cortical and deep gray matter (CDGM) = 18) and CST‐wiring patterns (ipsilateral = 14, bilateral = 14, contralateral = 12, unknown = 6) using ANCOVA with age as covariate. Using elastic‐net regularized regression we investigated how graph metrics, lesion volume, lesion type, CST‐wiring pattern and age predicted sensory‐motor function. In both the whole brain and subnetworks, we observed a hyperconnectivity pattern in children with CDGM‐lesions compared with PWM‐lesions, with higher clustering coefficient (p = [<.001–.047], =[0.09–0.27]), characteristic path length (p = .003, =0.19) and local efficiency (p = [.001–.02], =[0.11–0.21]), and a lower global efficiency with age (p = [.01–.04], =[0.09–0.15]). No differences were found between CST‐wiring groups. Overall, good predictions of sensory‐motor function were obtained with elastic‐net regression (R² = .40–.87). CST‐wiring pattern was the strongest predictor for motor function. For somatosensory function, all independent variables contributed equally to the model. In conclusion, we demonstrated the potential of structural connectomics in understanding disease severity and brain development in children with uCP.