Structural network performance for early diagnosis of spastic cerebral palsy in periventricular white matter injury

Periventricular white matter injury (PWMI) is a common cause of spastic cerebral palsy (SCP). Diffusion tensor imaging (DTI) shows high sensitivity but moderate specificity for predicting SCP. The limited specificity may be due to the diverse and extensive brain injuries seen in infants with PWMI. We enrolled 72 infants with corrected age from 6 to 18 months in 3 groups: PWMI with SCP (n = 20), non-CP PWMI (n = 19), and control (n = 33) groups. We compared DTI-based brain network properties among the three groups and evaluated the diagnostic performance of brain network properties for SCP in PWMI infants. Our results show abnormal global parameters (reduced global and local efficiency, and increased shortest path length), and local parameters (reduced node efficiency) in the PWMI with SCP group. On logistic regression, the combined node efficiency of the bilateral precentral gyrus and right middle frontal gyrus had a high sensitivity (90%) and specificity (95%) for differentiating PWMI with SCP from non-CP PWMI, and significantly correlated with the Gross Motor Function Classification System scores. This study confirms that DTI-based brain network has great diagnostic performance for SCP in PWMI infants, and the combined node efficiency improves the diagnostic accuracy.

Automated summary

Periventricular white matter injury (PWMI) is a common cause of spastic cerebral palsy (SCP). Diffusion tensor imaging (DTI) can effectively predict SCP in PWMI infants, with the combined node efficiency of specific brain regions showing high sensitivity and specificity in differentiation.