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“Neurodegenerative

brain diseases are collectively characterized by two core features: abnormal protein deposition and distinctive profiles of damage across the brain and over time (Frisoni et al., 2010 and Rohrer et al., 2011). If we understood in detail how proteinopathies translate to clinical phenotypes, we might anticipate and perhaps prevent the devastating impact of these diseases. While we have recognized for some time that spatiotemporal brain atrophy profiles track neuropathological patterns of disease evolution (Frisoni et al., 2010), we have lacked a principled framework for understanding and predicting the profiles observed. The Dolutegravir brain is composed of neural networks and graph theory provides a methodology for representing and analyzing those networks (Bullmore

and Sporns, 2009). Work in animal models has demonstrated a correspondence between mathematically derived network characteristics and the hierarchical and distributed www.selleckchem.com/products/z-vad-fmk.html architectures of neuroanatomy (Modha and Singh, 2010). Network-level analysis is an ideal approach to understanding neurodegenerative diseases, due both to the fundamentally coherent and distributed nature of the underlying pathological processes and the failure of conventional approaches to adequately explain the distinctive phenomenology of these diseases. However, the potential clinical value of network-based approaches remains largely unrealized. Two papers in this issue of Neuron ( Raj et al., 2012 and Zhou et al., 2012) take us further toward this goal, by applying the methods of graph theory to quantify and predict network disintegration in

a range of neurodegenerative diseases. These papers capitalize on two key recent insights: the expression of neurodegeneration within specific, distributed, intrinsic brain networks ( Zhou et al., 2010) and the propensity of culprit proteins to “template” further protein aggregation and spread of disease along neural pathways ( Hardy, 2005 and de Calignon et al., 2012). Raj et al. (2012) model network diffusion based on tractography data in the healthy brain and not derive robust spatial eigenmodes that correspond closely to atrophy profiles observed in Alzheimer’s disease and frontotemporal dementia; their model makes no prior assumptions about selective neuronal vulnerabilities or protein-specific factors. Zhou et al. (2012) show that common neurodegeneration syndromes seed distinctive connectivity structures derived using task-free fMRI in the healthy brain: their data suggest that the neurodegenerative process spreads primarily between neurons according to the functional proximity of specific brain regions acting as critical hub-like “epicenters,” rather than various alternative candidate mechanisms. Both papers agree that transsynaptic diffusion plays a core role in the spread of neurodegenerative pathologies, and together they provide a succinct framework for characterizing network disintegration in these diseases.