Topology of whole-brain functional MRI networks: Improving the truncated scale-free model

E. Ruiz Vargas, D.G.V. Mitchell, S.G. Greening and L.M. Wahl

Physica A: Statistical Mechanics and its Applications, 2014, vol. 405, issue C, 151-158

Abstract: Networks of connections within the human brain have been the subject of intense recent research, yet their topology is still only partially understood. We analyze weighted networks calculated from functional magnetic resonance imaging (fMRI) data acquired during task performance. Expanding previous work in the area, our analysis retains all of the connections between all of the voxels in the full brain fMRI data, computing correlations between approximately 200,000 voxels per subject for 10 subjects. We evaluate the extent to which this rich dataset can be described by existing models of scale-free or exponentially truncated scale-free topology, comparing results across a large number of more complex topological models as well. Our results suggest that the novel “log quadratic” model presented in this paper offers a significantly better fit to networks of functional connections at the voxel level in the human brain.

Keywords: fMRI data; Scale-free topologies; Log quadratic model (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:405:y:2014:i:c:p:151-158

DOI: 10.1016/j.physa.2014.03.025

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