Background

MS pathogenesis seems to involve both genetic susceptibility and environmental risk factors. Three sequential factors are implicated in the environmental risk. The first acts near birth, the second acts during childhood, and the third acts long thereafter. Two candidate factors (vitamin D deficiency and Epstein-Barr viral infection) seem well suited to the first two environmental events.

Methodology/principal findings

A mathematical Model for MS pathogenesis is developed, incorporating these environmental and genetic factors into a causal scheme that can explain some of the recent changes in MS-epidemiology (e.g., increasing disease prevalence, a changing sex-ratio, and regional variations in monozygotic twin concordance rates).

Conclusions/significance

This Model suggests that genetic susceptibility is overwhelmingly the most important determinant of MS pathogenesis. Indeed, over 99% of individuals seem genetically incapable of developing MS, regardless of what environmental exposures they experience. Nevertheless, the contribution of specific genes to MS-susceptibility seems only modest. Thus, despite HLA DRB1*1501 being the most consistently identified genetic marker of MS-susceptibility (being present in over 50% of northern MS patient populations), only about 1% of individuals with this allele are even genetically susceptible to getting MS. Moreover, because genetic susceptibility seems so similar throughout North America and Europe, environmental differences principally determine the regional variations in disease characteristics. Additionally, despite 75% of MS-patients being women, men are 60% more likely to be genetically-susceptible than women. Also, men develop MS at lower levels of environmental exposure than women. Nevertheless, women are more responsive to the recent changes in environmental-exposure (whatever these have been). This explains both the changing sex-ratio and the increasing disease prevalence (which has increased by a minimum of 32% in Canada over the past 35 years). As noted, environmental risk seems to result from three sequential components of environmental exposure. The potential importance of this Model for MS pathogenesis is that, if correct, a therapeutic strategy, designed to interrupt one or more of these sequential factors, has the potential to markedly reduce or eliminate disease prevalence in the future.

Neocortical neurons show UP-DOWN state (UDS) oscillations under a variety of conditions. These UDS have been extensively studied because of the insight they can yield into the functioning of cortical networks, and their proposed role in putative memory formation. A key element in these studies is determining the precise duration and timing of the UDS. These states are typically determined from the membrane potential of one or a small number of cells, which is often not sufficient to reliably estimate the state of an ensemble of neocortical neurons. The local field potential (LFP) provides an attractive method for determining the state of a patch of cortex with high spatio-temporal resolution; however current methods for inferring UDS from LFP signals lack the robustness and flexibility to be applicable when UDS properties may vary substantially within and across experiments. Here we present an explicit-duration hidden Markov model (EDHMM) framework that is sufficiently general to allow statistically principled inference of UDS from different types of signals (membrane potential, LFP, EEG), combinations of signals (e.g., multichannel LFP recordings) and signal features over long recordings where substantial non-stationarities are present. Using cortical LFPs recorded from urethane-anesthetized mice, we demonstrate that the proposed method allows robust inference of UDS. To illustrate the flexibility of the algorithm we show that it performs well on EEG recordings as well. We then validate these results using simultaneous recordings of the LFP and membrane potential (MP) of nearby cortical neurons, showing that our method offers significant improvements over standard methods. These results could be useful for determining functional connectivity of different brain regions, as well as understanding network dynamics.