The Impact of Normalization and Segmentation on Resting-State Brain Networks

Abstract

Graph theory has recently received a lot of attention from the neuroscience community as a method to represent and characterize brain networks. Still, there is a lack of a gold standard for the methods that should be employed for the preprocessing of the data and the construction of the networks, as well as a lack of knowledge on how different methodologies can affect the metrics reported. The authors used graph theory analysis applied to resting-state functional magnetic resonance imaging to investigate the influence of different node-defining strategies and the effect of normalizing the functional acquisition on several commonly reported metrics used to characterize brain networks. The nodes of the network were defined using either the individual FreeSurfer segmentation of each subject or the FreeSurfer segmented Montreal National Institute (MNI) 152 template, using the Destrieux and subcortical atlas. The functional acquisition was either kept on the functional native space or normalized into MNI standard space. The comparisons were done at three levels: on the connections, on the edge properties, and on the network properties levels. The results reveal that different registration and brain parcellation strategies have a strong impact on all the levels of analysis, possibly favoring the use of individual segmentation strategies and conservative registration approaches. In conclusion, several technical aspects must be considered so that graph theoretical analysis of connectivity MRI data can provide a framework to understand brain pathologies.

Altered functional connectivity of the default mode network in Williams syndrome: a multimodal approach

“…For the graph analysis the pre-processing strategy was as described elsewhere (Magalhaes, Marques, Soares, Alves & Sousa, 2015). Briefly, the steps of slice timing correction, head motion correction, and band-pass filtering were used as described in the present work.…”

High-resolution T2-FLAIR and non-contrast CT brain atlas of the elderly

“…Both, the modality and age distribution, can pose considerable problems for neuroimaging studies and clinical applications that require accurate spatial correspondences between the patient scans and the atlas (typically determined using non-linear image registration methods). Previous studies have shown that the registration accuracy has a significant effect on the results of neuroimaging studies using traditional image analysis methods as well as state-of-the-art deep learning approaches [3][4][5][6] . Within this context, substantial shape differences between the images (e.g., atrophy caused by brain age or neurological diseases 7 ) and image appearance differences (i.e., contrast differences across imaging modalities) are known to have a considerable impact on the accuracy of brain image registration.…”

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

“…A variety of automated preprocessing pipelines have been described and implemented [e.g., DPABI, LONI (Rex et al, 2003), Nipype (Gorgolewski et al, 2011), BrainCAT (Marques et al, 2013) and C-PAC], but there is a lack of consensus about which workflow is the most effective. Several studies and reviews have explored the effects of preprocessing techniques on both task-based (Strother, 2006; Churchill et al, 2012a,b) and rs-fMRI results (Aurich et al, 2015; Magalhães et al, 2015). Herein we attempt to provide a practical guide to the most commonly used methodologies.…”