Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities

Sahin Hanalioglu; Siyar Bahadir; Ilkay Isikay; Pinar Celtikci; Emrah Celtikci; Fang-Cheng Yeh; Kader Karli Oguz; Taghi Khaniyev

doi:10.3389/fnins.2021.782995

Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities

Front Neurosci. 2021 Dec 21:15:782995. doi: 10.3389/fnins.2021.782995. eCollection 2021.

Authors

Sahin Hanalioglu¹, Siyar Bahadir¹, Ilkay Isikay¹, Pinar Celtikci², Emrah Celtikci³, Fang-Cheng Yeh⁴, Kader Karli Oguz^{5

6}, Taghi Khaniyev^{7

8}

Affiliations

¹ Department of Neurosurgery, Hacettepe University Faculty of Medicine, Ankara, Turkey.
² Department of Radiology, Ankara City Hospital, Ankara, Turkey.
³ Department of Neurosurgery, Gazi University Faculty of Medicine, Ankara, Turkey.
⁴ Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, United States.
⁵ Department of Radiology, Hacettepe University Faculty of Medicine, Ankara, Turkey.
⁶ National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey.
⁷ Department of Industrial Engineering, Faculty of Engineering, Bilkent University, Ankara, Turkey.
⁸ Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, United States.

Abstract

Objective: Graph theory applications are commonly used in connectomics research to better understand connectivity architecture and characterize its role in cognition, behavior and disease conditions. One of the numerous open questions in the field is how to represent inter-individual differences with graph theoretical methods to make inferences for the population. Here, we proposed and tested a simple intuitive method that is based on finding the correlation between the rank-ordering of nodes within each connectome with respect to a given metric to quantify the differences/similarities between different connectomes. Methods: We used the diffusion imaging data of the entire HCP-1065 dataset of the Human Connectome Project (HCP) (n = 1,065 subjects). A customized cortical subparcellation of HCP-MMP atlas (360 parcels) (yielding a total of 1,598 ROIs) was used to generate connectivity matrices. Six graph measures including degree, strength, coreness, betweenness, closeness, and an overall "hubness" measure combining all five were studied. Group-level ranking-based aggregation method ("measure-then-aggregate") was used to investigate network properties on population level. Results: Measure-then-aggregate technique was shown to represent population better than commonly used aggregate-then-measure technique (overall r_s: 0.7 vs 0.5). Hubness measure was shown to highly correlate with all five graph measures (r_s: 0.88-0.99). Minimum sample size required for optimal representation of population was found to be 50 to 100 subjects. Network analysis revealed a widely distributed set of cortical hubs on both hemispheres. Although highly-connected hub clusters had similar distribution between two hemispheres, average ranking values of homologous parcels of two hemispheres were significantly different in 71% of all cortical parcels on group-level. Conclusion: In this study, we provided experimental evidence for the robustness, limits and applicability of a novel group-level ranking-based hubness analysis technique. Graph-based analysis of large HCP dataset using this new technique revealed striking hemispheric asymmetry and intraparcel heterogeneities in the structural connectivity of the human brain.

Keywords: connectome; graph theory; hemispheric asymmetry; hub; hubness; network; parcellation; population.