@article {Raamana170381,
	author = {Raamana, <NAME> Strother, <NAME>.},
	title = {Impact of spatial scale and edge weight on predictive power of cortical thickness networks},
	year = {2017},
	doi = {10.1101/170381},
	publisher = {Cold Spring Harbor Labs Journals},
	abstract = {Network-level analysis based on anatomical covariance (cortical thickness) has been gaining increasing popularity 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 include the robust predictive power resulting from lack of significant impact of changes in nodal size (spatial scale) among the three classification experiments. We also release an open source python package to enable others to implement presented network feature extraction algorithm in their research.},
	URL = {http://www.biorxiv.org/content/early/2017/07/31/170381},
	eprint = {http://www.biorxiv.org/content/early/2017/07/31/170381.full.pdf},
	journal = {bioRxiv}
}
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}
