Using Individualized Brain Network for Analyzing Structural Covariance of the Cerebral Cortex in Alzheimer's Patients

• Published in: Frontiers in neuroscience Vol. 10; p. 394
• Main Authors: Kim, Hee-Jong, Shin, Jeong-Hyeon, Han, Cheol E, Kim, Hee Jin, Na, Duk L, Seo, Sang Won, Seong, Joon-Kyung
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 01.09.2016; Frontiers Media S.A
• Subjects: Age; Aging; Alzheimer's disease; Atrophy; Brain diseases; Cerebral cortex; Cognitive ability; cortical thickness; Datasets; Gender; Individual SCN; Medical imaging; Morphology; network architecture; Network entropy; Neural networks; Neurodegenerative diseases; Neuroimaging; Neuroscience; NMR; Nuclear magnetic resonance; Schizophrenia; Structural covariance network (SCN); Studies; South Korea; network entropy; Alzheimer's disease; network architecture; cortical thickness; structural covariance network (SCN); individual SCN
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2016.00394

Cortical thinning patterns in Alzheimer's disease (AD) have been widely reported through conventional regional analysis. In addition, the coordinated variance of cortical thickness in different brain regions has been investigated both at the individual and group network levels. In this study, we aim to investigate network architectural characteristics of a structural covariance network (SCN) in AD, and further to show that the structural covariance connectivity becomes disorganized across the brain regions in AD, while the normal control (NC) subjects maintain more clustered and consistent coordination in cortical atrophy variations. We generated SCNs directly from T1-weighted MR images of individual patients using surface-based cortical thickness data, with structural connectivity defined as similarity in cortical thickness within different brain regions. Individual SCNs were constructed using morphometric data from the Samsung Medical Center (SMC) dataset. The structural covariance connectivity showed higher clustering than randomly generated networks, as well as similar minimum path lengths, indicating that the SCNs are "small world." There were significant difference between NC and AD group in characteristic path lengths (z = -2.97, p < 0.01) and small-worldness values (z = 4.05, p < 0.01). Clustering coefficients in AD was smaller than that of NC but there was no significant difference (z = 1.81, not significant). We further observed that the AD patients had significantly disrupted structural connectivity. We also show that the coordinated variance of cortical thickness is distributed more randomly from one region to other regions in AD patients when compared to NC subjects. Our proposed SCN may provide surface-based measures for understanding interaction between two brain regions with co-atrophy of the cerebral cortex due to normal aging or AD. We applied our method to the AD Neuroimaging Initiative (ADNI) data to show consistency in results with the SMC dataset.