, 2009), dementia with Lewy bodies and posterior cortical atrophy ( Rabinovici and Jagust, 2009), MK 8776 etc. PIB-positive
binding to A-beta and plaques were observed in 25%–45% of cognitively normal older subjects in postmortem autopsy studies ( Rabinovici and Jagust, 2009). BvFTD accommodates an even more bewildering array of pathological correlates, including alpha-synuclein, tau, ubiquitin, TDP-43, and Lewy bodies ( Whitwell et al., 2005, Forman et al., 2006 and Pereira et al., 2009). Pereira et al. found that clinical variants of bvFTD, but not histologic variants, correlated with regional atrophy, and that there was no volumetric difference between tau and ubiquitin bvFTD pathology regardless EGFR inhibitor of clinical subtype.
No group-wise differences were found in the atrophy patterns of tau-positive versus TDP-43-positive FTLD cases ( Whitwell et al., 2009). These results indicate that clinical presentation of dementias are only dependent on the brain regions they affect, rather than their histopathological correlates. If true, these findings would provide strong support for our work, which infers macroscopic consequences of proteopathic progression without being encumbered by their specifics. The main contribution of the proposed network diffusion model is that it turns qualitative understanding of proteopathic transmission into a quantitative, fully testable model and provides a plausible alternative explanation for the apparent selective vulnerability of brain regions in various dementias. The network diffusion model does not support the “retrogenesis”
hypothesis that AD is a WM-specific disease and is caused by demyelination of late myelinating fiber pathways (Bartzokis, 2004). A PDK4 model that is informed by the minutiae of the neuropathology of degeneration, melding the most current and detailed histopathological findings, might prove more accurate. Nevertheless, we note that as a first-order approximation, the presented model appears to capture the essential patterns of dementia atrophy. Simple models can sometimes capture the emergent behavior of large-scale complex systems like the brain, which can be surprisingly linear within large phase domains bounded by (nonlinear) phase transitions. Indeed, the emergence of predictable and regular behavior from chaotic ensembles is considered a hallmark of complexity (Shalizi, 2001). For example, the admittance of large electrical networks of capacitative and resistive elements is known to be chaotic, yet its frequency response is essentially linear in large frequency ranges (Almond et al., 2011). This kind of predictable, regular emergent behavior is seen in complex systems as varied as the flocking of geese (Martinez et al., 2007) and complex biological signaling networks (Bhalla, 2002).