Statistical analysis of minimum cost path based structural brain connectivity

Autor: Henri A. Vrooman, Marius de Groot, Wiro J. Niessen, M. Arfan Ikram, Aad van der Lugt, Meike W. Vernooij, Fedde van der Lijn, Monique M.B. Breteler, Renske de Boer, Michiel Schaap
Přispěvatelé: Radiology & Nuclear Medicine, Epidemiology
Rok vydání: 2011
Předmět:
Zdroj: NeuroImage, 55(2), 557-565. Academic Press
NeuroImage, 55 (2), 2011
ISSN: 1053-8119
DOI: 10.1016/j.neuroimage.2010.12.012
Popis: Diffusion MRI can be used to study the structural connectivity within the brain. Brain connectivity is often represented by a binary network whose topology can be studied using graph theory. We.. present a framework for the construction of weighted structural brain networks, containing information about connectivity, which can be effectively analyzed using statistical methods. Network nodes are defined by segmentation of subcortical structures and by cortical parcellation. Connectivity is established using minimum cost path (mcp) method with an anisotropic local cost function based directly on diffusion weighted images. We refer to this framework as Statistical Analysis of Minimum cost path based Structural Connectivity (SAMSCo) and the weighted structural connectivity networks as mcp-networks. In a proof of principle study we investigated the information contained in mcp-networks by predicting subject age based on the mcp-networks of a group of 974 middle-aged and elderly subjects. Using SAMSCo, age was predicted with an average error of 3.7 years. This was significantly better than predictions based on fractional anisotropy or mean diffusivity averaged over the whole white matter or over the corpus callosum, which showed average prediction errors of at least 4.8 years. Additionally, we classified subjects, based on the mcp-networks, into groups with low and high white matter lesion load, while correcting for age, sex and white matter atrophy. The SAMSCo classification outperformed the classification based on the diffusion measures with a classification accuracy of 76.0% versus 63.2%. We also performed a classification in groups with mild and severe atrophy, correcting for age, sex and white matter lesion load. In this case, mcp-networks and diffusion measures yielded similar classification accuracies of 68.3% and 67.8% respectively. The SAMSCo prediction and classification experiments indicate that the mcp-networks contain information regarding age, white matter lesion load and white matter atrophy, and that in case of age and white matter lesion load the mcp-network based models outperformed the predictions based on diffusion measures. (C) 2010 Elsevier Inc. All rights reserved.
Databáze: OpenAIRE
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