Structural connectome with high angular resolution diffusion imaging MRI: assessing the impact of diffusion weighting and sampling on graph-theoretic measures

Giuseppina Caiazzo,Federica Di Nardo,Francesca Trojsi,Fabrizio Esposito,Gioacchino Tedeschi,Michele Fratello

doi:10.1007/s00234-018-2003-7

Abstract

PurposeAdvances in computational network analysis have enabled the characterization of topological properties of human brain networks (connectomics) from high angular resolution diffusion imaging (HARDI) MRI structural measurements. In this study, the effect of changing the diffusion weighting (b value) and sampling (number of gradient directions) was investigated in ten healthy volunteers, with specific focus on graph theoretical network metrics used to characterize the human connectome.MethodsProbabilistic tractography based on the Q-ball reconstruction of HARDI MRI measurements was performed and structural connections between all pairs of regions from the automated anatomical labeling (AAL) atlas were estimated, to compare two HARDI schemes: low b value (b = 1000) and low direction number (n = 32) (LBLD); high b value (b = 3000) and high number (n = 54) of directions (HBHD).ResultsLBLD and HBHD data sets produced connectome images with highly overlapping hub structure. Overall, the HBHD scheme yielded significantly higher connection probabilities between cortical and subcortical sites and allowed detecting more connections. Small worldness and modularity were reduced in HBHD data. The clustering coefficient was significantly higher in HBHD data indicating a higher level of segregation in the resulting connectome for the HBHD scheme.ConclusionOur results demonstrate that the HARDI scheme as an impact on structural connectome measures which is not automatically implied by the tractography outcome. As the number of gradient directions and b values applied may introduce a bias in the assessment of network properties, the choice of a given HARDI protocol must be carefully considered when comparing results across connectomic studies.

Highlights

Brain connectivity and the connectome have unlocked new experimental and theoretical avenues in many areas of neuroscience
Our results demonstrate that the high angular resolution diffusion imaging (HARDI) scheme as an impact on structural connectome measures which is not automatically implied by the tractography outcome
As the number of gradient directions and b values applied may introduce a bias in the assessment of network properties, the choice of a given HARDI protocol must be carefully considered when comparing results across connectomic studies

Summary

Methods

Ten healthy subjects (4 male and 6 female, age 52 ± 7.15 years) were scanned on a 3T GE Medical System scanner (Signa HDxt3T twin speed GE) equipped with an eightchannel parallel head coil. We calculated eight network measures as described in [20] with the Brain Connectivity Toolbox: Betweeness Centrality, Global efficiency, Local efficiency, Node strength, Node degree, Clustering coefficient (C), Characteristic Path-length (L), Modularity (M), and Small worldness. Each of these properties and their biological significance has been defined and discussed in detail elsewhere [1, 6]. NBS analysis revealed a significantly higher connection probability in HBHD compared to LBLD data sets in 88 pairs of node (p = 0.0002) (Fig. 1, Table 1). There were no significant differences in betweenness centrality, node strength, and node degree after FDR correction

Conclusion

Introduction

Discussion

Findings

Compliance with ethical standards