Abstract
Diffusion imaging is accelerating our understanding of the human brain. As brain connectivity analyses become more popular, it is vital to develop reliable metrics of the brain’s connections, and their network properties, to allow statistical study of factors that influence brain ‘wiring’. Here we chart differences in brain structural networks between normal aging and Alzheimer’s disease (AD) using 3-T whole-brain diffusion-weighted images (DWI) from 66 subjects (22 AD/44 normal elderly). We performed whole-brain tractography based on the orientation distribution functions. Connectivity matrices were compiled, representing the proportion of detected fibers interconnecting 68 cortical regions. We found clear disease effects on anatomical network topology in the structural backbone – the so-called ‘k-core’ – of the anatomical network, defined by varying the nodal degree threshold, k. However, the thresholding of the structural networks – based on their nodal degree – affected the pattern and interpretation of network differences discovered between patients and controls.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Clerx, P., et al.: New MRI markers for Alzheimer’s disease: a meta-analysis of diffusion tensor imaging and a comparison with medial temporal lobe measurements. J Alzheimer’s Dis. 29(2), 405–429 (2012)
Lee, H., et al.: Persistent brain network homology from the perspective of dendrogram. IEEE Trans. Med. Imaging. 31(12), 2267–2677 (2012)
Daianu, M., et al.: Breakdown of brain connectivity between normal aging and Alzheimer’s disease: a structural k-core network analysis. Brain Connect. 3(4), 407–422 (2013)
Hagmann, P., et al.: Mapping the structural core of the human cerebral cortex. PLoS Biol. 6(7), 1479–1493 (2008)
Aganj, I., et al.: A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography. Med. Image Anal. 15(4), 414–425 (2011)
Desikan, R.S., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968–980 (2006)
Fischl, B., et al.: Automatically parcellating the human cerebral cortex. Cereb. Cortex 14, 11–22 (2004)
Sporns, O.: Networks of the Brain, pp. 5–31. MIT Press, Cambridge (2011)
Alvarez-Hamelin, J.I., et al.: Large scale networks fingerprinting and visualization using the k-core decomposition. In: Weiss, Y., Scholkopf, B., Platt, J. (eds.) Proceedings: Advances in Neural Information Processing Systems, vol. 18, pp. 41–50. MIT Press, Cambridge, MA (2006)
Yao, Z., Zhang, Y., Lin, L., Zhou, Y., Xu, C., Jiang, T.: Alzheimer’s disease neuroimaging initiative. Abnormal cortical networks in mild cognitive impairment and Alzheimer’s disease. PLoS Comput. Biol. 6(11), e1001006 (2010)
Wijk, B.C.M., et al.: Comparing brain networks of different size and connectivity density using graph theory. PLoS One 5(10), e13701 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Daianu, M. et al. (2014). Disrupted Brain Connectivity in Alzheimer’s Disease: Effects of Network Thresholding. In: Schultz, T., Nedjati-Gilani, G., Venkataraman, A., O'Donnell, L., Panagiotaki, E. (eds) Computational Diffusion MRI and Brain Connectivity. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-02475-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-02475-2_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02474-5
Online ISBN: 978-3-319-02475-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)