Therefore, at present, no firm conclusions seem to be possible on the degree to which envelope ICMs or phase ICMs may differentiate between different disorders. At a very general level, it may be hypothesized that disorders with a high degree of structural alteration may be associated with strong changes in both envelope and phase ICMs (e.g., AD), whereas disorders with less prominent connectomic changes (e.g., PD) may primarily show altered phase ICMs (Table 1). The current data point to a preferential pathophysiological involvement of certain ICMs, which may be altered in specific subnetworks in the respective disorders. However, more neurophysiological investigations of envelope
Selleckchem Navitoclax ICMs and phase ICMs are required, which ideally should be combined with source space analyses (Hipp et al., 2012, Brookes et al., 2012 and Marzetti et al., 2013). This might allow the identification of ICMs that reflect GSK1120212 in vitro network pathologies with high specificity and sufficient sensitivity to monitor longitudinal change during disease progression or recovery. Computational modeling has taken up the challenge of investigating the mechanisms
underlying ICMs. One central motivation of such simulations has been to explore the dynamic implications of structural brain connectivity (Bullmore and Sporns, 2012). In addition to incorporating information about anatomical connections (Hagmann et al., 2008), these models also include a generalized description of the dynamics of regional neural populations (Figure 6A). Typically, the models assume largely uniform features for the dynamics of the nodes or the interconnections (Deco and Corbetta, 2011 and Deco et al., 2011). The results of several such modeling approaches (Zhou et al., 2006, Honey et al., 2007, Deco et al., 2009 and Haimovici et al., 2013) converge on a number of central MTMR9 findings. In particular, the models reproduce empirically observed correspondences between structural connectivity and envelope ICMs (Honey et al., 2009). As a result, envelope ICMs found in the models
typically reflect topological features of the underlying connectome, such as modules and hubs (Honey et al., 2007). Models further suggest that structural modularity can endow ICMs with dynamics on different temporal scales (Figure 6B). Intramodular links may provide a substrate for fast interactions, while intermodular connections allow the integration of nodes across modalities at longer timescales (Pan and Sinha, 2009). It is currently unclear to what extent this difference between topological scales may contribute to the physiological distinction between envelope and phase ICMs. Interestingly, similar results were found in models differing strongly in their local node dynamics (Figure 6A), which may be represented by chaotic oscillators (Honey et al., 2007), phase oscillators (Cabral et al., 2011), neural mass models (Deco et al., 2009), or simple discrete excitable nodes (Haimovici et al., 2013).
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