Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive imaging technique widely used in neuroscience to understand the functional connectivity of the human brain. While rs-fMRI multi-site data can help to understand the inner working of the brain, the data acquisition and processing of this data has many challenges. One of the challenges is the variability of the data associated with different acquisitions sites, and different MRI machines vendors.

TurboBC: A Memory Efficient and Scalable GPU Based Betweenness Centrality Algorithm in the Language of Linear Algebra.

Betweenness centrality (BC) is a shortest path centrality metric used to measure the influence of individual vertices or edges on huge graphs that are used for modeling and analysis of human brain, omics data, or social networks. The application of the BC algorithm to modern graphs must deal with the size of the graphs, as well with highly irregular data-access patterns. These challenges are particularly important when the BC algorithm is implemented on Graphics Processing Units (GPU), due to the limited global memory of these processors, as well as the decrease in performance due to the load unbalance resulting from processing irregular data structures.

TurboBFS: GPU Based Breadth-First Search (BFS) Algorithms in the Language of Linear Algebra.

Graphs that are used for modeling of human brain, omics data, or social networks are huge, and manual inspection of these graph is impossible. A popular, and fundamental, method used for making sense of these large graphs is the well-known Breadth-First Search (BFS) algorithm. However, BFS suffers from large computational cost especially for big graphs of interest.

A Multi-Factorial Assessment of Functional Human Autistic Spectrum Brain Network Analysis.

The variability of the results obtained by the statistical analysis of functional human brain networks depend on multiple factors such as: the source of the fMRI data, the brain parcellations, the graph theory measures, and the threshold values applied to the functional connectivity matrices to obtain adjacency matrices of sparse graphs. Therefore, the brain network used for down-stream analysis is heavily dependent on the methods that are applied to the fMRI data to obtain and analyze such networks. In this paper we present the preliminary results of a multi-factorial assessment of the statistical analysis of functional human brain networks.