Disrupted Modularity and Local Connectivity of Brain Functional Networks in Childhood-Onset Schizophrenia

By Aaron F. Alexander-Bloch, Nitin Gogtay, David Meunier, Rasmus M. Birn, Liv S. Clasen, François Lalonde, Rhoshel K. Lenroot, Jay N. Giedd, E. Bullmore. Frontiers in Systems Neuroscience, 2010

472 citations recorded by the source corpus.

Modularity is a fundamental concept in systems neuroscience, referring to the formation of local cliques or modules of densely intra-connected nodes that are sparsely inter-connected with nodes in other modules. Topological modularity of brain functional networks can quantify theoretically anticipated abnormality of brain network community structure – so-called dysmodularity – in developmental disorders such as childhood-onset schizophrenia (COS). We used graph theory to investigate topology of networks derived from resting-state fMRI data on 13 COS patients and 19 healthy volunteers. We measured functional connectivity between each pair of 100 regional nodes, focusing on wavelet correlation in the frequency interval 0.05–0.1 Hz, then applied global and local thresholding rules to construct graphs from each individual association matrix over the full range of possible connection densities. We show how local thresholding based on the minimum spanning tree facilitates group comparisons of networks by forcing the connectedness of sparse graphs. Threshold-dependent graph theoretical results are compatible with the results of a k-means unsupervised learning algorithm and a multi-resolution (spin glass) approach to modularity, both of which also find community structure but do not require thresholding of the association matrix. In general modularity of brain functional networks was significantly reduced in COS, due to a relatively reduced density of intra-modular connections between neighboring regions. Other network measures of local organization such as clustering were also decreased, while complementary measures of global efficiency and robustness were increased, in the COS group. The group differences in complex network properties were mirrored by differences in simpler statistical properties of the data, such as the variability of the global time series and the internal homogeneity of the time series within anatomical regions of interest.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3545c2a775eac32a928f164d215cfc49be2ed1b6

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Citations in the corpus

Brain graphs: graphical models of the human brain connectome. (2011, methodology): “…brain network, it must therefore be grown somehow, e.g., by addition of extra edges that pass a global threshold value, such that graph metrics can be estimated over a full cost range of connection densities, without any change in the number of connected network nodes (Alexander-Bloch et al. 2010).”
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, methodology): “Proportional thresholding is applied after this step, to595 preserve the top 20% of the edges and reduce threshold effects on the network measures Garrison et al. (2015); Alexander-Bloch et al. (2010).”
Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data (2011, background): “Nevertheless, it’s hard to conclude which approach or which parcellation is better since all of them are valid and important approaches to uncover brain connectivity architecture from different perspectives [40,43,61,62].”
Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015, background): “However, this modular structure is not static but undergoes changes in response to external and internal drivers, ranging from cognitive processes (3, 31) to disease (32).”
The trees and the forest: Characterization of complex brain networks with minimum spanning trees. (2014, unknown): “In the first study resting-state fMRI was performed in 13 patients with childhood onset schizophrenia (COS) and 19 healthy controls (Alexander-Bloch et al., 2010).”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, unknown): “Specific thresholdingmethods that ensure connected graphs have been proposed (Alexander-Bloch et al., 2010).”
The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis (2012, background): “From an analysis perspective, sparse graphs (with =∼ 25% of edges connected) can be considered to have higher “signal to noise” ratio (Alexander-Bloch et al., 2010), preserving only the strongest connections in the network.”
Modular and Hierarchically Modular Organization of Brain Networks (2010, background): “In support of this expectation, some evidence for dysmodularity, or abnormal modular organization, has already been reported in the brain functional networks of patients with childhood-onset schizophrenia (Alexander-Bloch et al., 2010).”
Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures (2012, result): “to find a compromise between low-density but fully connected networks, some authors suggest to use a maximum spanning tree (MST)-based approach (Alexander-Bloch et al., 2010; Hagmann et al., 2008).”
Schizophrenia, neuroimaging and connectomics (2012, background): “…functional connectivity can be observed in the absence of marked topological differences (Fornito et al., 2011a) suggests that topological alterations are not a necessary consequence of connectivity differences, although the two are often related (Alexander-Bloch et al., 2010; Lynall et al., 2010).”
Network hubs in the human brain. (2013, background): “Furthermore, childhood-onset schizophrenia has been associated with a disrupted modular architecture [122], together with disturbed connectivity of network connector hubs in multimodal association cortex [123].”
Simple models of human brain functional networks (2012, methodology): “To ensure that no nodeswere disconnected from the rest of the network, we used theminimum spanning tree as a backbone, then added further edges in order of decreasing correlation strength to produce binary graphs over a range of connection densities (26), from 4% to 16% of the maximum possible numbe”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “So far, only very few works have tried to address this issue based on minimum spanning tree approaches [169] or data mining techniques [170].”
Proportional thresholding in resting‐state fMRI functional connectivity networks and consequences for patient‐control connectome studies: Issues and recommendations (2017, unknown): “However, as discussed in the graph theoretical studies of Van Wijk et al. (2010) and others (e.g. (Alexander-Bloch et al., 2010; Fornito et al., 2013; van den Heuvel and Fornito, 2014) the inclusion of lower and thus potentially less reliable correlations as functional network edges can have an e ﬀ…”
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, background): “(Alexander-Bloch et al., 2010).”