Abnormal Cortical Networks in Mild Cognitive Impairment and Alzheimer's Disease

By Zhijun Yao, Yuanchao Zhang, Lei Lin, Yuan Zhou, Cunlu Xu, T. Jiang. PLoS Comput. Biol., 2010

444 citations recorded by the source corpus.

Recently, many researchers have used graph theory to study the aberrant brain structures in Alzheimer's disease (AD) and have made great progress. However, the characteristics of the cortical network in Mild Cognitive Impairment (MCI) are still largely unexplored. In this study, the gray matter volumes obtained from magnetic resonance imaging (MRI) for all brain regions except the cerebellum were parcellated into 90 areas using the automated anatomical labeling (AAL) template to construct cortical networks for 98 normal controls (NCs), 113 MCIs and 91 ADs. The measurements of the network properties were calculated for each of the three groups respectively. We found that all three cortical networks exhibited small-world properties and those strong interhemispheric correlations existed between bilaterally homologous regions. Among the three cortical networks, we found the greatest clustering coefficient and the longest absolute path length in AD, which might indicate that the organization of the cortical network was the least optimal in AD. The small-world measures of the MCI network exhibited intermediate values. This finding is logical given that MCI is considered to be the transitional stage between normal aging and AD. Out of all the between-group differences in the clustering coefficient and absolute path length, only the differences between the AD and normal control groups were statistically significant. Compared with the normal controls, the MCI and AD groups retained their hub regions in the frontal lobe but showed a loss of hub regions in the temporal lobe. In addition, altered interregional correlations were detected in the parahippocampus gyrus, medial temporal lobe, cingulum, fusiform, medial frontal lobe, and orbital frontal gyrus in groups with MCI and AD. Similar to previous studies of functional connectivity, we also revealed increased interregional correlations within the local brain lobes and disrupted long distance interregional correlations in groups with MCI and AD.

Subfields: Law, Political Science and International Relations, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/e205c577fd8ab55f8c341f5c496903e70adb26c0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Networks of anatomical covariance (2013, background): “Global indices of brain morphology, such as intra-cranial volume, total grey/white matter volume or mean cortical thickness, are highly heritable (Panizzon et al., 2009; Pennington et al., 2000; Pfefferbaum et al., 2000), so studies of regional heritability must first remove the global component (Ey”
GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks (2012, methodology): “The ROIs are identical to those used in a previous graph analysis study of typical brain development by Fan and colleagues [19,22,50].”
Groupwise whole-brain parcellation from resting-state fMRI data for network node identification (2013, unknown): “…important information in understanding a wide range of clinical disorders and diseases (Anticevic et al., 2013; Auer, 2008; Brown et al., 2011; Buckner et al., 2009; Cao et al., 2011; Sanz-Arigita et al., 2010; Supekar et al., 2008; Venkataram et al., 2012; Wang et al., 2012; Yao et al., 2010).”
Structural connectomics in brain diseases (2013, unknown): “Nevertheless, significant differences of global network measures between MCI and healthy controls, or between MCI/RGD and AD patients are not necessarily present (Yao et al, 2010; Bai et al., 2012), suggesting more subtle and localized damages.”
State-space model with deep learning for functional dynamics estimation in resting-state fMRI (2016, unknown): “, 2013), increased correlations within the prefrontal lobe, and parietal lobe (Wang et al., 2007; Supekar et al., 2008; Yao et al., 2010).”