Scale-Dependent Variability and Quantitative Regimes in Graph-Theoretic Representations of Human Cortical Networks

Abstract

Studying brain connectivity is important due to potential differences in brain circuitry between health and disease. One drawback of graph-theoretic approaches to this is that their results are dependent on the spatial scale at which brain circuitry is examined and explicitly on how vertices and edges are defined in network models. To investigate this, magnetic resonance and diffusion tensor images were acquired from 136 healthy adults, and each subject's cortex was parceled into as many as 50,000 regions. Regions were represented as nodes in a reconstructed network representation, and interregional connectivity was inferred via deterministic tractography. Network model behavior was explored as a function of nodal number and connectivity weighing. Three distinct regimes of quantitative behavior assumed by network models as a function of spatial scale are identified, and their existence may be modulated by the spatial folding scale of the cortex. The maximum number of network nodes used to model human brain circuitry in this study (∼50,000) is larger than in previous macroscale neuroimaging studies. Results suggest that network model properties vary appreciably as a function of vertex assignment convention and edge weighing scheme and that graph-theoretic analysis results should not be compared across spatial scales without appropriate understanding of how spatial scale and model topology modulate network model properties. These findings have implications for comparing macro- to mesoscale studies of brain network models and understanding how choosing network-theoretic parameters affects the interpretation of brain connectivity studies.

Within brain area tractography suggests local modularity using high resolution connectomics

“…This gives confidence in the reproducibility of the connectivity generated here using the higher quality HCP data. Furthermore, the previous use of high resolution structural1115, and functional44 connectomes set precedent for this work. The speculated overlap of structural modules with functional activations (Fig.…”

“…Bonilha et al 14. used individual grey matter voxels as streamline termination points to estimate the density of connections, whilst Irimia et al 15. used networks of up to 50,000 nodes in their work.…”

Sensitivity analysis of human brain structural network construction

“…In many network studies, the total number of nodes is significantly higher than 463 (Irima & Van Horn, 2016 ; Klimm, Bassett, Carlson, & Mucha, 2014 ; van den Heuvel, Stam, Boersma& Pol 2008 . The streamline sampling rates are especially important for studies where high-resolution atlases are utilized.…”

Acute cognitive deficits after traumatic brain injury predict Alzheimer’s disease-like degradation of the human default mode network

“…To determine whether modules’ node memberships were dependent upon the DMN parcellation scheme used in the study, the DMN was reparcelled randomly 100 times to generate alternative parcellations which had the same number of nodes as the original DMN but different cortical patches corresponding to each node. An approach similar to those of Gordon et al [27] and Irimia&Van Horn [28] was used to obtain randomized parcellations of the DMN. Briefly, random points within the cortical coverage of the DMN were selected.…”