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

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.

Global and regional cortical connectivity maturation index (CCMI) of developmental human brain with quantification of short-range association tracts.

From early childhood to adulthood, synaptogenesis and synaptic pruning continuously reshape the structural architecture and neural connection in developmental human brains. Disturbance of the precisely balanced strengthening of certain axons and pruning of others may cause mental disorders such as autism and schizophrenia. To characterize this balance, we proposed a novel measurement based on cortical parcellation and diffusion MRI (dMRI) tractography, a cortical connectivity maturation index (CCMI).

Test-retest reliability of graph metrics in high-resolution functional connectomics: a resting-state functional MRI study.

Background: The combination of resting-state functional MRI (R-fMRI) technique and graph theoretical approaches has emerged as a promising tool for characterizing the topological organization of brain networks, that is, functional connectomics. In particular, the construction and analysis of high-resolution brain connectomics at a voxel scale are important because they do not require prior regional parcellations and provide finer spatial information about brain connectivity. However, the test-retest reliability of voxel-based functional connectomics remains largely unclear.

A hybrid CPU-GPU accelerated framework for fast mapping of high-resolution human brain connectome.

Recently, a combination of non-invasive neuroimaging techniques and graph theoretical approaches has provided a unique opportunity for understanding the patterns of the structural and functional connectivity of the human brain (referred to as the human brain connectome). Currently, there is a very large amount of brain imaging data that have been collected, and there are very high requirements for the computational capabilities that are used in high-resolution connectome research. In this paper, we propose a hybrid CPU-GPU framework to accelerate the computation of the human brain connectome.