Cerebral palsy (CP), caused by brain damage during pregnancy, at birth or the first 2 yrs of life, is the most common childhood physical disability. Regardless of the severity of disability, these children face lifelong sensorimotor deficits that require continuous support, resulting in lifetime costs of around €800,000 per child. Active inclusion in the labor market would reduce these costs and would vastly benefit from optimal upper limb recovery. There is growing consensus that this can be best achieved by early (before age 4 yrs) and targeted upper limb treatment. In many European countries, Magnetic Resonance Imaging (MRI) is routinely used for CP diagnostics, often before age 3 yrs. Using this information to predict sensorimotor outcome at such a young age would bring forth a crucial advantage to optimize therapy planning. Existing classification schemes aiming to link lesion location/extent to sensorimotor deficits of the child have only limited predictive power. Here I will test whether information on structural and functional connectivity of sensorimotor networks can be used to derive clinically relevant neural biomarkers. This project will provide proof-of-principle that MRI connectivity indices can be extracted and linked to clinical outcome measurements in individuals with unilateral CP. The project focuses on a new structural mapping method based on T1-/T2-weighted MRI (structural connectivity) and on functional MRI of the resting-state (functional connectivity). These modalities are advantageous because they are task-free, can be easily acquired in young children with a disorder, and data can be shared across different MRI systems and sites, thereby forming the basis for future clinical trials. The project uses existing and new data to develop novel connectivity indices in CP and link them to upper limb sensorimotor performance measurements. All methods will be validated against current gold standards as used in highly specialized research centers.
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