PAGANI Toolkit: Parallel graph‐theoretical analysis package for brain network big data

Abstract

The recent collection of unprecedented quantities of neuroimaging data with high spatial resolution has led to brain network big data. However, a toolkit for fast and scalable computational solutions is still lacking. Here, we developed the PArallel Graph-theoretical ANalysIs (PAGANI) Toolkit based on a hybrid central processing unit-graphics processing unit (CPU-GPU) framework with a graphical user interface to facilitate the mapping and characterization of high-resolution brain networks. Specifically, the toolkit provides flexible parameters for users to customize computations of graph metrics in brain network analyses. As an empirical example, the PAGANI Toolkit was applied to individual voxel-based brain networks with ∼200,000 nodes that were derived from a resting-state fMRI dataset of 624 healthy young adults from the Human Connectome Project. Using a personal computer, this toolbox completed all computations in ∼27 h for one subject, which is markedly less than the 118 h required with a single-thread implementation. The voxel-based functional brain networks exhibited prominent small-world characteristics and densely connected hubs, which were mainly located in the medial and lateral fronto-parietal cortices. Moreover, the female group had significantly higher modularity and nodal betweenness centrality mainly in the medial/lateral fronto-parietal and occipital cortices than the male group. Significant correlations between the intelligence quotient and nodal metrics were also observed in several frontal regions. Collectively, the PAGANI Toolkit shows high computational performance and good scalability for analyzing connectome big data and provides a friendly interface without the complicated configuration of computing environments, thereby facilitating high-resolution connectomics research in health and disease.

Topological analyses of functional connectomics: A crucial role of global signal removal, brain parcellation, and null models

“…First, in this study, we performed the functional network analysis only at the regional level based on different parcellation schemes. As there is no widely accepted gold standard for how to select the parcellation scheme (Arslan et al, ), voxel‐wise functional network analysis is an appropriate candidate for future studies due to recent improvements in computing platforms (Du et al, ; Wang et al, ). The voxel‐based functional network analysis may reveal more detailed connectivity information at a higher spatial resolution and reduce the potential bias of regional parcellations.…”

Shared and Distinct Functional Architectures of Brain Networks Across Psychiatric Disorders

“…The network construction and calculations of global network measurements were performed using the PAGANI toolkit (https://www.nitrc. org/projects/pagani_toolkit/), 26 which was developed based on our previously established CPU-GPU hybrid framework 27 to facilitate the rapid calculation of high-resolution voxel-based brain networks. This toolbox has been used to investigate the test-retest (TRT) reliability of graph metrics of voxel-based functional brain networks.…”

Resting-State Brain Network Analysis Methods and Applications

“…However, since the spatial resolution of fMRI is relatively high (2mm-4mm), the number of voxels is rather large (around the magnitude of 100,000) and the constructed network requires huge computation power for further analysis. Researchers have proposed specialized methods, such as the Parallel Graph-theoretical Analysis (PAGANI) toolkit to accelerate the processing of voxel-based whole-brain networks [15].…”