![]() ![]() Because different neurons/sensors are highly correlated and not all are informative for the behavior of interest, dimensionality reduction methods (such as principal component analysis, PCA) are typically applied to identify a low-dimensional subspace capturing the majority of the variance in the data and/or most relevant to the behavior in question (Briggman et al., 2005 Churchland et al., 2012 Stokes et al., 2013 Baria et al., 2017). We speculate that the premotor cortex maintains an updated internal representation of the multisensory/postural state of the own hand, implementing context and priors for the multisensory estimation process in the PPC in a dynamic process that involves top-down and bottom-up interactions between these two regions. The neural implementation of multisensory BCI may have a hierarchical organization Noppeney, 2015, 2016 Cao et al., 2019 Rohe et al., 2019) where parietal regions implement multisensory estimates of body ownership, regardless of the context (Ganguli et al., 2008 Suzuki and Gottlieb, 2013), and frontal regions integrate contextual cues, and prior expectations, taking into account sensory uncertainty (Gau and Noppeney, 2016 Kayser and Kayser, 2018). ![]() These considerations notwithstanding, there are changes in effective connectivity between the PPC and the PMv during the RHI Limanowski and Blankenburg, 2015 Casula et al., 2022), which are anatomically connected, that may vary with the level of sensory uncertainty and the prior (e.g., experimental context) magnitude/type of multisensory conflict. ![]()
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