Connectivity network measures predict volumetric atrophy in mild cognitive impairment - Nir, Talia M. (Neurobiol Aging, 2014)
Alzheimer’s disease (AD) is characterized by cortical atrophy and disrupted anatomical connectivity, and leads to abnormal interactions between neural systems. Diffusion weighted imaging (DWI) and graph theory can be used to evaluate major brain networks, and detect signs of a breakdown in network connectivity. In a longitudinal study using both DWI and standard MRI, we assessed baseline white matter connectivity patterns in 30 subjects with mild cognitive impairment (MCI; mean age: 71.8+/−7.5 yrs; 18M/12F) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using both standard MRI-based cortical parcellations and whole-brain tractography, we computed baseline connectivity maps from which we calculated global “small-world” architecture measures, including mean clustering coefficient (MCC) and characteristic path length (CPL). We evaluated whether these baseline network measures predicted future volumetric brain atrophy in MCI subjects, who are at risk for developing AD, as determined by 3D Jacobian “expansion factor maps” between baseline and 6-month follow-up anatomical scans. This study suggests that DWI-based network measures may be a novel predictor of AD progression.