RT Journal Article
SR Electronic
T1 Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 151993
DO 10.1101/151993
A1 Dmitry Petrov
A1 Alexander Ivanov
A1 Joshua Faskowitz
A1 Boris Gutman
A1 Daniel Moyer
A1 Julio Villalon
A1 Neda Jahanshad
A1 Paul Thompson
YR 2017
UL http://biorxiv.org/content/early/2017/06/19/151993.abstract
AB There is no consensus on how to construct structural brain networks from diffusion MRI. How variations in pre-processing steps affect network reliability and its ability to distinguish subjects remains opaque. In this work, we address this issue by comparing 35 structural connectome-building pipelines. We vary diffusion reconstruction models, tractography algorithms and parcellations. Next, we classify structural connectome pairs as either belonging to the same individual or not. Connectome weights and eight topological derivative measures form our feature set. For experiments, we use three test-retest datasets from the Consortium for Reliability and Reproducibility (CoRR) comprised of a total of 105 individuals. We also compare pairwise classification results to a commonly used parametric test-retest measure, Intraclass Correlation Coefficient (ICC)‡.