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Cerebral palsy is a neurodevelopmental movement disorder that affects coordination and balance. Therapeutic treatments for balance deficiencies in this population primarily focus on the musculoskeletal system, whereas the neural basis of balance impairment is often overlooked. Magnetic resonance elastography (MRE) is an emerging technique that has the ability to sensitively assess microstructural brain health through in vivo measurements of neural tissue stiffness. Using magnetic resonance elastography, we have previously measured significantly softer grey matter in children with cerebral palsy as compared with typically developing children. To further allow magnetic resonance elastography to be a clinically useful tool in rehabilitation, we aim to understand how brain stiffness in children with cerebral palsy is related to dynamic balance reaction performance as measured through anterior and posterior single-stepping thresholds, defined as the standing perturbation magnitudes that elicit anterior or posterior recovery steps. We found that global brain stiffness is significantly correlated with posterior stepping thresholds ( P = .024) such that higher brain stiffness was related to better balance recovery. We further identified specific regions of the brain where stiffness was correlated with stepping thresholds, including the precentral and postcentral gyri, the precuneus and cuneus, and the superior temporal gyrus. Identifying brain regions affected in cerebral palsy and related to balance impairment can help inform rehabilitation strategies targeting neuroplasticity to improve motor function.

Functional fMRI data are typically analyzed under the assumption that participants experience one long, continuous connectivity state throughout rest scan sessions. The chronnectome is a model that takes into account the temporal variance in connectivity throughout a scan session. In this work, we evaluate the repeatability of properties of functional network connectivity (FNC) dynamics assessed using sliding-windowed correlations in 28 independent age-matched large samples of 250 subjects. This approach revealed that multiple discrete, reoccurring connectivity states arise during rest, and that subjects tend to remain in one connectivity state for long periods of time before transitioning to another. Occurrence time spent in certain states tends to increase as participants spend more time resting, while less time is spent in other states as time goes on. Overall, results show distinct connectivity states that are similar across groups during rest.