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Abstract
Little is known about human muscle growth in children with and without cerebral palsy (CP). The MUGgLE study aims to investigate growth-related changes in the three-dimensional (3D) architecture of lower leg muscles (muscle volume, physiological cross-sectional area (PCSA), fascicle length, and pennation angle) in 320 infants and children with and without CP aged < 3 months and 5 to 15 years. Infants have one leg scan (anatomical magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) images), while children have three scans over three years. The MUGgLE study is ongoing. This thesis presents data derived from the first scan conducted on each of 208 typically developing (TD) infants and children.
Chapter 2 provides muscle volumes of ten muscle groups in infants, and the architecture and moment arms of the medial (MG) and lateral gastrocnemius (LG) muscles. By comparing these data to data obtained from adults, it was shown that MG muscle fascicles grow primarily in cross-section rather than in length from birth to adulthood.
Chapter 3 determines if lower leg muscles grow synchronously from birth to 15 years. The data show that muscle volumes, normalised to total lower leg volume, vary with age, indicating asynchronous growth. The soleus and MG muscles grow disproportionately faster.
Chapter 4 determines muscle-, age-, and sex-conditional distributions of MG and tibialis anterior (TA) muscle architecture from birth to 15 years. Up to age 15 years, both muscles grow nonlinearly in volume, PCSA, and fascicle length, while the pennation angles remain nearly constant. The MG and TA muscle fascicles grow primarily transversely rather than longitudinally over this period.
Chapter 5 explores the development and evaluation of a portable dynamometer used to estimate the passive length-tension curves of the gastrocnemius muscles in children. The evaluation shows that the dynamometer requires further methodological refinements to be reliable enough for clinical and research use.
This thesis contributes to the fields of biomechanics, muscle physiology, and human anatomy, providing the largest high-resolution 3D dataset of muscle architecture in children to date. Biomechanists can use the data to build more effective structure-function models of children’s muscles, clinicians can use the data to investigate disordered muscle growth in children and inform early interventions and treatments, and academics can use the data to teach muscle and bone anatomy.
