To determine causal influences among brain regions and more directly assess theoretical anatomical models requires testing of "effective connectivity," with models that embody causal connections such as structural equation modeling (29) or generative models such as dynamic causal modeling (DCM) (30,31).
In this paper, we apply a special finite-volume scheme, limited to smooth temperature distributions and Cartesian grids, to test the importance of connectivity of the finite volumes.
The aim of this study was to test the use of connectivity metrics of water bodies, in the context of patches, in a tropical waterscape wetland (Guapore River, Amazonia, Brazil) as a predictor of potential net methylmercury (MeHg) production by periphyton communities.
Thus, SeqZip provided a new means to test the possibility of connectivity between Fn1 EDA and V splicing decisions.
Subsequently, two-sample t-tests of the connectivity values were performed between the IGE patients and the healthy controls.
Further testing of effective connectivity did not support our hypotheses of qualitative differences in frontoparietal network configuration between groups.
For each subject and session, a general linear model was formed representing the 30 s blocks of word generation convolved with the canonical haemodynamic response function representing the task, the raw thalamic time series (allowing testing of functional connectivity between the thalamus and the rest of the brain) and the interaction of the two.
Previous analyses of this issue have been hampered by the incompleteness of data testing for connectivity in both directions using consistent parcellation criteria (see the Introduction section).
The main goal of the paper consists in testing the relevance of connectivity between the LUs.
Given a set F of vertices of a connected graph G, we study the problem of testing the connectivity of G−F in polynomial time with respect to |F| and the maximum degree Δ of G.
