3 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
Stability Indicators in Network Reconstruction
The number of algorithms available to reconstruct a biological network from a
dataset of high-throughput measurements is nowadays overwhelming, but
evaluating their performance when the gold standard is unknown is a difficult
task. Here we propose to use a few reconstruction stability tools as a
quantitative solution to this problem. We introduce four indicators to
quantitatively assess the stability of a reconstructed network in terms of
variability with respect to data subsampling. In particular, we give a measure
of the mutual distances among the set of networks generated by a collection of
data subsets (and from the network generated on the whole dataset) and we rank
nodes and edges according to their decreasing variability within the same set
of networks. As a key ingredient, we employ a global/local network distance
combined with a bootstrap procedure. We demonstrate the use of the indicators
in a controlled situation on a toy dataset, and we show their application on a
miRNA microarray dataset with paired tumoral and non-tumoral tissues extracted
from a cohort of 241 hepatocellular carcinoma patients