The Study of Graph Measurements for Hub Identification in Multiple Parcellated Brain Networks of Healthy Older Adult

Abstract

Hubs are a set of highly connected brain regions which play an important role in the human brain network. Various graph measurements and parcellation atlases have been utilized for hub identification. However, the relationship of those measurements and the comparison of hub identification results derived from different types of brain networks are not clear yet. The current study used four measurements to identify hubs in 75 healthy aging subjects' brain networks. Also, to figure out the potential effect using various parcellation schemes on the hub identification, five kinds of brain networks were constructed, which were accomplished by two types of brain parcellation schemes including anatomical parcellation atlases (AAL atlas and HOA altas) and random parcellation scheme (uniformed parcellation atlas with 32, 128 and 512 regions). From the results, we found that hubs can be consistently identified in the same type of brain network regardless of measurements. On the contrary, hubs were notably different in the different types of brain networks even using the same measurement. Beyond these, hub consistency between measurements derived from anatomical brain networks tend to be relatively stable than that derived from uniformed parcellated brain networks. Importantly, the consistency of identification results of uniformed parcellated brain networks were also constrained by the selection of identification threshold level. For the relationship between graph measurements, results revealed a robust relationship between vulnerability and other measurements. Our findings provide a better understanding to the effect of identification measurements, parcellation atlases, and identification thresholds on the hub identification, which may offer a future prospect of being able to create a unified standard for the hub identification.

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