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

• Published in: Human brain mapping Vol. 39; no. 5; pp. 1869 - 1885
• Main Authors: Du, Haixiao, Xia, Mingrui, Zhao, Kang, Liao, Xuhong, Yang, Huazhong, Wang, Yu, He, Yong
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.05.2018; John Wiley and Sons Inc
• Subjects: Adults; Algorithms; Big Data; Brain; Brain - diagnostic imaging; Brain mapping; Brain Mapping - instrumentation; Brain Mapping - methods; Central processing units; Computation; Computational neuroscience; Computer Graphics; connectome; CPUs; CUDA; Data management; Data processing; Empirical analysis; Female; fMRI; Functional magnetic resonance imaging; graph theory; Graphical user interface; Graphics processing units; hub; Humans; Image Processing, Computer-Assisted; Intelligence; Magnetic Resonance Imaging; Male; Medical imaging; Models, Neurological; Modularity; Networks; Neural Pathways - diagnostic imaging; Neuroimaging; Neurology; Oxygen - blood; Rest; Software; Spatial data; Spatial discrimination; Spatial resolution; Theoretical analysis; Young Adult; Young adults; CUDA; Big Data; fMRI; connectome; hub; graph theory
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.23996

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.