RT Journal Article
SR Electronic
T1 Impact of spatial scale and edge weight on predictive power of cortical thickness networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 170381
DO 10.1101/170381
A1 Pradeep Reddy Raamana
A1 Stephen C. Strother
A1 for The Alzheimer’s Disease Neuroimaging Initiative
YR 2019
UL http://biorxiv.org/content/early/2019/07/09/170381.abstract
AB Network-level analysis based on anatomical, pairwise similarities (e.g., cortical thickness) has been gaining increasing attention recently. However, there has not been a systematic study of the impact of spatial scale and edge definitions on predictive performance. In order to obtain a clear understanding of relative performance, there is a need for systematic comparison. In this study, we present a histogram-based approach to construct subject-wise weighted networks that enable a principled comparison across different methods of network analysis. We design several weighted networks based on two large publicly available datasets and perform a robust evaluation of their predictive power under three levels of separability. One of the interesting insights is that changes in nodal size (spatial scale) have no significant impact on predictive power among the three classification experiments and two disease cohorts studied, i.e., mild cognitive impairment and Alzheimer’s disease from ADNI, and Autism from the ABIDE dataset. We also release an open source python package to enable others to implement the novel network feature extraction algorithm, which is applicable to other modalities in diverse applications of connectivity research.