Abstract
Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.
Highlights
Noninvasive neuroimaging techniques provide quantitative measurements of structural and functional connectivity in the human brain
We explore a set of synthetic networks that includes a random Erdos-Renyi network (ER), a ring lattice (RL), a smallworld network (SW), and a fractal hierarchical network (FH)
While multi-resolution techniques could be usefully applied to a broad range of network diagnostics, here we focus on two complementary mesoscale characteristics that can be used to probe the manner in which groups of brain regions are connected with one another: modularity and bipartivity
Summary
Noninvasive neuroimaging techniques provide quantitative measurements of structural and functional connectivity in the human brain. Measurements can be represented as a weighted network [1,2,3,4,5,6,7], in which nodes correspond to brain regions, and the weighted connection strength between two nodes can, for example, represent correlated activity (fMRI) or fiber density (DWI). The network representation of human brain connectivity facilitates quantitative and statistically stringent investigations of human cognitive function, aging and development, and injury or disease. Target applications of these measurements include disease diagnosis, monitoring of disease progression, and prediction of treatment outcomes [8,9,10,11,12]. Efforts to develop robust methods to reduce these large and complex neuroimaging data sets to statistical diagnostics that differentiate between patient populations have been stymied by the dearth of methods to quantify the statistical significance of apparent group differences in network organization [13,14,15,16]
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