65,204 research outputs found
Statistical Network Analysis for Functional MRI: Summary Networks and Group Comparisons
Comparing weighted networks in neuroscience is hard, because the topological
properties of a given network are necessarily dependent on the number of edges
of that network. This problem arises in the analysis of both weighted and
unweighted networks. The term density is often used in this context, in order
to refer to the mean edge weight of a weighted network, or to the number of
edges in an unweighted one. Comparing families of networks is therefore
statistically difficult because differences in topology are necessarily
associated with differences in density. In this review paper, we consider this
problem from two different perspectives, which include (i) the construction of
summary networks, such as how to compute and visualize the mean network from a
sample of network-valued data points; and (ii) how to test for topological
differences, when two families of networks also exhibit significant differences
in density. In the first instance, we show that the issue of summarizing a
family of networks can be conducted by adopting a mass-univariate approach,
which produces a statistical parametric network (SPN). In the second part of
this review, we then highlight the inherent problems associated with the
comparison of topological functions of families of networks that differ in
density. In particular, we show that a wide range of topological summaries,
such as global efficiency and network modularity are highly sensitive to
differences in density. Moreover, these problems are not restricted to
unweighted metrics, as we demonstrate that the same issues remain present when
considering the weighted versions of these metrics. We conclude by encouraging
caution, when reporting such statistical comparisons, and by emphasizing the
importance of constructing summary networks.Comment: 16 pages, 5 figure
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction
Graph Signal Processing (GSP) is a promising framework to analyze
multi-dimensional neuroimaging datasets, while taking into account both the
spatial and functional dependencies between brain signals. In the present work,
we apply dimensionality reduction techniques based on graph representations of
the brain to decode brain activity from real and simulated fMRI datasets. We
introduce seven graphs obtained from a) geometric structure and/or b)
functional connectivity between brain areas at rest, and compare them when
performing dimension reduction for classification. We show that mixed graphs
using both a) and b) offer the best performance. We also show that graph
sampling methods perform better than classical dimension reduction including
Principal Component Analysis (PCA) and Independent Component Analysis (ICA).Comment: 5 pages, GlobalSIP 201
Local Difference Measures between Complex Networks for Dynamical System Model Evaluation
Acknowledgments We thank Reik V. Donner for inspiring suggestions that initialized the work presented herein. Jan H. Feldhoff is credited for providing us with the STARS simulation data and for his contributions to fruitful discussions. Comments by the anonymous reviewers are gratefully acknowledged as they led to substantial improvements of the manuscript.Peer reviewedPublisher PD
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