Structural networks in Alzheimer's disease

Alzheimer's disease (AD) appears to be a uniquely human condition, which is possibly attributable to our expanded longevity and peculiar capacity for episodic memory. Due to a lack of naturally-occurring animal model for investigating AD pathogenesis, our knowledge about the disease must be derived from correlational observation of humans, or from animal models produced by genetic manipulation of known risk factors in humans. Advances in neuroimaging, cellular and molecular science, and computational methods have proven useful for the improvement of such techniques, but the general limitation persists; as a result we remain without clear answers to some of the fundamental questions posed by AD. On the other hand, much progress has been made in characterizing the longitudinal progression of AD pathology, which includes the formation of plaques and tangles, a distinct topological pattern of atrophy of grey and white matter, and the concurrent decline of specific cognitive functions, beginning with mild memory impairments and ending with general debilitating dementia. In this review, we first discuss the existing literature which characterizes AD etiology, pathology, and pathogenesis, with the intention of framing the disease as primarily a disconnection syndrome. We next describe methodologies for investigating the topological properties of human brain networks, using graph theoretical techniques and connectivity information derived from anatomical and diffusion-weighted MR imaging. Finally, we discuss how these methodologies have been applied to systems-level analyses of AD, to help characterize the network changes underlying the disease process, and how these patterns relate to specific cognitive outcome measures. (C) 2012 Elsevier B.V. and ECNP. All rights reserved.