Structural Insights into Aberrant Topological Patterns of Large-Scale Cortical Networks in Alzheimer's Disease
By Yong He, Zhang John Chen, Alan C. Evans. Journal of Neuroscience, 2008
942 citations recorded by the source corpus.
Recent research on Alzheimer's disease (AD) has shown that cognitive and memory decline in this disease is accompanied by disrupted changes in the coordination of large-scale brain functional networks. However, alterations in coordinated patterns of structural brain networks in AD are still poorly understood. Here, we used cortical thickness measurement from magnetic resonance imaging to investigate large-scale structural brain networks in 92 AD patients and 97 normal controls. Brain networks were constructed by thresholding cortical thickness correlation matrices of 54 regions and analyzed using graph theoretical approaches. Compared with controls, AD patients showed decreased cortical thickness intercorrelations between the bilateral parietal regions and increased intercorrelations in several selective regions involving the lateral temporal and parietal cortex as well as the cingulate and medial frontal cortex regions. Specially, AD patients showed abnormal small-world architecture in the structural cortical networks (increased clustering and shortest paths linking individual regions), implying a less optimal topological organization in AD. Moreover, AD patients were associated with reduced nodal centrality predominantly in the temporal and parietal heteromodal association cortex regions and increased nodal centrality in the occipital cortex regions. Finally, the brain networks of AD were about equally as robust to random failures as those of controls, but more vulnerable against targeted attacks, presumably because of the effects of pathological topological organization. Our findings suggest that the coordinated patterns of cortical morphology are widely altered in AD patients, thus providing structural evidence for disrupted integrity in large-scale brain networks that underlie cognition. This work has implications for our understanding of how functional deficits in patients are associated with their underlying structural (morphological) basis.
Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/fde3a1aaacc0cba6eddd445c1124b4878463354e
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Citations in the corpus
- A Network Diffusion Model of Disease Progression in Dementia (2012, positive): “This view is supported by recent studies showing alterations in brain networks due to neurodegeneration (He et al., 2008; Lo et al., 2010).”
- Whole-brain anatomical networks: Does the choice of nodes matter? (2010, methodology): “In Achard et al. (2006); Van den Heuvel et al. (2008); Liu et al. (2008), the algorithm in Maslov and Sneppen (2002) was reinvoked M times to compute M random graphs R 1 ,…, R M equivalent to G, thereby affording the Monte-Carlo approximation c R = hc R 1 ; N ; c RM i and l R = hl R1 ; N ; l RM i.…”
- Brain graphs: graphical models of the human brain connectome. (2011, background): “…schizophrenia (Bassett et al. 2008, 2009; Liu et al. 2008; Lynall et al. 2010; Micheloyannis et al. 2006; Rubinov 2009), Alzheimer’s disease (He et al. 2008, 2009a; Stam 2010; Stam et al. 2007a; Supekar et al. 2008), epilepsy (Horstmann et al. 2010, Raj et al. 2010, van Dellen et al.…”
- Parcellation‐dependent small‐world brain functional networks: A resting‐state fMRI study (2009, methodology): “…alterations in development (Micheloyannis et al., in press), normal aging (Achard and Bullmore, 2007) and brain disorders (Bartolomei et al., 2006; He et al., 2008; Liu et al., 2008; Micheloyannis et al., 2006b; Ponten et al., 2007; Rubinov et al., in press; Stam et al., 2007; Wang et al., in…”
- Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits (2010, background): “While this definition is currently widely used [11,15,63,70], it provides an indirect estimate and likely an under-estimate of the true length of white matter tracts and axons in these neural systems, which may take convoluted paths to connect a given pair of nodes.”
- Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory (2010, background): “To omit all k -dependencies one may adjust the threshold for each individual network so that k is fixed over all recordings and conditions (e.g., [6,25,29,34,35]).”
- Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory (2009, result): “Loss of small-world structure in AD was also demonstrated in recent MRI studies applying graph theory [19,20].”
- Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data (2011, background): “Currently, there is no a definitive way to accurately determine threshold and previous studies construct brain networks either under a single threshold (e.g., [43,44]) or over a continuous threshold range (e.g., [45,46]) in terms of specific constraint conditions.”
- Loss of ‘Small-World’ Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity (2010, background): “[19] on the analysis of cortical thickness intercorrelations exemplifies this difficulty, describing a structural network characterized by a long path length that is in direct contrast with the presently described shorter functional path length.”
- Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans (2009, methodology): “evaluated the effect of ‘lesion’ of edges on the overall topology of brain functional networks [29,58,59].”
- Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks (2010, background): “showed different structural alteration of brain small-world organization [83].”