13,731 research outputs found
Sparse Predictive Structure of Deconvolved Functional Brain Networks
The functional and structural representation of the brain as a complex
network is marked by the fact that the comparison of noisy and intrinsically
correlated high-dimensional structures between experimental conditions or
groups shuns typical mass univariate methods. Furthermore most network
estimation methods cannot distinguish between real and spurious correlation
arising from the convolution due to nodes' interaction, which thus introduces
additional noise in the data. We propose a machine learning pipeline aimed at
identifying multivariate differences between brain networks associated to
different experimental conditions. The pipeline (1) leverages the deconvolved
individual contribution of each edge and (2) maps the task into a sparse
classification problem in order to construct the associated "sparse deconvolved
predictive network", i.e., a graph with the same nodes of those compared but
whose edge weights are defined by their relevance for out of sample predictions
in classification. We present an application of the proposed method by decoding
the covert attention direction (left or right) based on the single-trial
functional connectivity matrix extracted from high-frequency
magnetoencephalography (MEG) data. Our results demonstrate how network
deconvolution matched with sparse classification methods outperforms typical
approaches for MEG decoding
Metrics for Graph Comparison: A Practitioner's Guide
Comparison of graph structure is a ubiquitous task in data analysis and
machine learning, with diverse applications in fields such as neuroscience,
cyber security, social network analysis, and bioinformatics, among others.
Discovery and comparison of structures such as modular communities, rich clubs,
hubs, and trees in data in these fields yields insight into the generative
mechanisms and functional properties of the graph.
Often, two graphs are compared via a pairwise distance measure, with a small
distance indicating structural similarity and vice versa. Common choices
include spectral distances (also known as distances) and distances
based on node affinities. However, there has of yet been no comparative study
of the efficacy of these distance measures in discerning between common graph
topologies and different structural scales.
In this work, we compare commonly used graph metrics and distance measures,
and demonstrate their ability to discern between common topological features
found in both random graph models and empirical datasets. We put forward a
multi-scale picture of graph structure, in which the effect of global and local
structure upon the distance measures is considered. We make recommendations on
the applicability of different distance measures to empirical graph data
problem based on this multi-scale view. Finally, we introduce the Python
library NetComp which implements the graph distances used in this work
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