A parsimonious statistical method to detect groupwise differentially expressed functional connectivity networks

Zhang

et al. 2020

Entropy

Self Cite

We propose a new metric to characterize the complexity of weighted complex networks. Weighted complex networks represent a highly organized interactive process, for example, co-varying returns between stocks (financial networks) and coordination between brain regions (brain connectivity networks). Although network entropy methods have been developed for binary networks, the measurement of non-randomness and complexity for large weighted networks remains challenging. We develop a new analytical framework to measure the complexity of a weighted network via graph embedding and point pattern analysis techniques in order to address this unmet need. We first perform graph embedding to project all nodes of the weighted adjacency matrix to a low dimensional vector space. Next, we analyze the point distribution pattern in the projected space, and measure its deviation from the complete spatial randomness. We evaluate our method via extensive simulation studies and find that our method can sensitively detect the difference of complexity and is robust to noise. Last, we apply the approach to a functional magnetic resonance imaging study and compare the complexity metrics of functional brain connectivity networks from 124 patients with schizophrenia and 103 healthy controls. The results show that the brain circuitry is more organized in healthy controls than schizophrenic patients for male subjects while the difference is minimal in female subjects. These findings are well aligned with the established sex difference in schizophrenia.

Section: Discussionmentioning

confidence: 99%

Characterizing the Complexity of Weighted Networks via Graph Embedding and Point Pattern Analysis

Zhang

et al. 2020

Entropy

Self Cite

“…Simpson et al 17 extended the NBS method using a permutation test based on Jaccard index at node level. While, Chen et al 18 enhanced NBS regulating the topological structures comprised. Other research groups [19][20][21] leveraged support vector machines (SVM) weights to identify discriminating regions.…”

Section: Local Differences Between Connectomesmentioning

confidence: 98%

Multi-Link Analysis: Brain Network Comparison via Sparse Connectivity Analysis

Crimi

Giancardo

Sambataro

et al. 2018

Preprint

The analysis of the brain from a connectivity perspective is unveiling novel insights into brain structure and function. Discovery is, however, hindered by the prior knowledge used to make hypotheses. On the other hand, exploratory data analysis is made complex by the high dimensionality of data. Indeed, in order to assess the effect of pathological states on brain networks, neuroscientists are often required to evaluate experimental effects in case-control studies, with hundreds of thousand connections.In this paper, we propose an approach to identify the multivariate relationships in brain connections that characterise two distinct groups, hence permitting the investigators to immediately discover sub-networks that contain information about the differences between experimental groups. In particular, we are interested in data discovery related to connectomics, where the connections that characterize differences between two groups of subjects are found, rather than maximizing accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups. In practice, our method exploits recent machine learning techniques employing sparsity to deal with weighted networks describing the whole-brain macro connectivity. We evaluated our technique on functional and structural connectomes from human and mice brain data. In our experiments, we automatically identified disease-relevant connections in datasets with unsupervised and anatomy driven parcellation approaches using high-dimensional datasets.

“…In the follows, we show that from the perspective of graph combinatorics, testing a combinatorial set of multivariate edges constrained in a subnetwork (subnetwork level inference) can lead to low false positive and false negative error rates. We consider the permutation test for the network level inference because the set of subgraphs G c is the power set of G that imposes difficulty for asymptotic inference and multiple testing (Zalesky et al, 2010;Chen et al, 2015;Chen et al, 2019…”

Section: Graph Combinatorics For Network-level Testmentioning

confidence: 99%

Graph combinatorics based group-level network inference

Hong

2019

Preprint

Self Cite

We consider group-level statistical inference for networks, where outcomes are multivariate edge variables constrained in an adjacency matrix. The graph notation is used to represent the network outcome variables, where nodes are identical biological units (e.g. brain regions) shared across subjects and edge-variables indicate the strengths of the interactive relationships between nodes. The edge-variables vary