Abstract #3855

Brain connectivity assessed by mechanical covariance

Haitao Ge^1,2, Armando Manduca³, David T. Jones⁴, Clifford R. Jack JR¹, John Huston III¹, Richard L. Ehman¹, and Matthew C. Murphy¹

¹Radiology, Mayo Clinic, Rochester, MN, United States, ²School of Medical Imaging, Xuzhou Medical University, Xuzhou, China, ³Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States, ⁴Neurology, Mayo Clinic, Rochester, MN, United States

The human connectome is a comprehensive representation of the brain’s network architecture. Novel imaging tools may provide new insights into this organization in both health and disease. Given the established sensitivity of the brain’s mechanical properties to its function, we test whether MR elastography-based stiffness estimates can be used to measure brain connectivity. We show that the mechanical connectivity network (MCN) is significantly correlated with established structural and functional connectivity methods, and also exhibits the expected small world organization. Nonetheless, MCN topological measures significantly differ from the existing methods, suggesting MRE may provide a new perspective on the brain’s organization.The human connectome is a comprehensive representation of the brain’s network architecture. Novel imaging tools may provide new insights into this organization in both health and disease. Given the established sensitivity of the brain’s mechanical properties to its function, we test whether MR elastography-based stiffness estimates can be used to measure brain connectivity. We show that the mechanical connectivity network (MCN) is significantly correlated with established structural and functional connectivity methods, and also exhibits the expected small world organization. Nonetheless, MCN topological measures significantly differ from the existing methods, suggesting MRE may provide a new perspective on the brain’s organization.

MRI-based measures play an important role in the field of connectomics, which aims to map the brain’s network organization in health and disease. Novel imaging tools may provide new insights into this organization. In this work, we tested whether MR elastography-based stiffness estimates can be used to measure brain connectivity. The mechanical connectivity network (MCN) was significantly correlated with established structural and functional connectivity methods, and also exhibited the expected small world organization. Nonetheless, MCN topological measures significantly differed from the existing methods, suggesting MRE may provide a new perspective on the brain’s organization.

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