Cerebral palsy (CP) is the most common physical disability in children. Almost half of all new CP diagnoses are made in children who were born preterm. Although CP results from abnormal development or injury of the brain during the fetal or neonatal period, children with CP typically do not receive a diagnosis until 2 years of age. These first 2 years are critical for neuroplasticity, when proven habilitative interventions could restore motor function. Our strong preliminary data suggest that early and accurate individualized prediction of CP is possible by using a combination of advanced brain MRI biomarkers at term corrected age (CA). We have developed reliable methods for measuring structural and functional connectivity in neonates using diffusion MRI (dMRI) and functional connectivity MRI (fcMRI), respectively. These methods can sensitively diagnose reduced neuronal connectivity, even in infants with a normal-appearing structural MRI (sMRI) that later develop CP. We have found that a combination of 3 sensorimotor network biomarkers correctly classified 98% of preterm infants with or without CP. The overall objective of this proposal is to determine the value of brain connectivity biomarkers, individually and in combination, to accurately diagnose CP within 3 months of birth. We propose a large multicenter prospective cohort study in very preterm infants (?31 weeks gestational age), using advanced MRI at term CA and developmental testing at 1 and 2 years CA. Our central hypothesis is that CP is a disorder of reduced sensorimotor network connectivity, and sensitive diagnosis of this reduced connectivity using advanced MRI at term CA will result in early and accurate prediction of CP. Diagnosis of CP soon after birth will guide the prescription and refinement of early, evidence-based sensorimotor interventions and novel neuroprotective therapies to enable improved outcomes in children with CP. The two specific aims to test the central hypothesis are: (1) To differentiate regional and global structural and functional connectivity at term CA in infants with a normal sMRI who develop CP, compared to infants who do not; (2) To define the prognostic test properties of structural connectivity biomarkers at term CA, independently and in combined multivariable models, and identify the model that most accurately enables personalized prediction of CP in very preterm infants. Under the first aim, we will perform dMRI tractography of 6 sensorimotor tracts and evaluate regional and global brain connectivity using graph theory measures. For the second aim, we will evaluate promising connectivity biomarkers to identify the most significant multivariable model for individualized prediction of CP. The approach is innovative because it will integrate advances in neuroimaging with established epidemiologic principles to elucidate pathophysiology and accurately predict CP within 3 months of birth in a large population of very preterm infants. The proposed research is significant because it will reduce the time to diagnosis of CP by 2 years so that early intervention resources and biologically based therapies can be targeted to the highest risk infants during a period of optimal neuroplasticity for reducing future impairments.
The proposed research is relevant to public health because advances in brain imaging can be used to reduce the time to diagnosis of cerebral palsy by 2 years to allow earlier and more targeted interventions, thereby reducing development of functional disabilities. The project is relevant to NINDS's mission because this approach should yield fundamental knowledge about the most common physical disability in children in order to reduce the associated burden of cerebral palsy.
|Li, Hailong; Parikh, Nehal A; He, Lili (2018) A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes. Front Neurosci 12:491|
|He, Lili; Li, Hailong; Holland, Scott K et al. (2018) Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework. Neuroimage Clin 18:290-297|
|Teli, Radhika; Hay, Margaret; Hershey, Alexa et al. (2018) Postnatal Microstructural Developmental Trajectory of Corpus Callosum Subregions and Relationship to Clinical Factors in Very Preterm Infants. Sci Rep 8:7550|
|He, Lili; Wang, Jinghua; Lu, Zhong-Lin et al. (2018) Optimization of magnetization-prepared rapid gradient echo (MP-RAGE) sequence for neonatal brain MRI. Pediatr Radiol 48:1139-1151|