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

Hee Jin Kim,Hee-Jong Kim,Sang Won Seo,Cheol E Han,Joon-Kyung Seong,Jeong-Hyeon Shin,Duk L Na

doi:10.3389/fnins.2016.00394

Hee Jin Kim, Hee-Jong Kim + Show 5 more

Open Access

https://doi.org/10.3389/fnins.2016.00394

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Abstract

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.

Highlights

We presented a new method to construct individual structural covariance network (SCN) based on cortical thickness covariance, and applied the proposed approaches to a large group of Alzheimer’s disease (AD) patients
We first demonstrated that the AD patients had significantly disrupted network architecture when compared to normal control (NC) subjects, which implies that the anatomical covariance connectivity exhibited more spreading out and inefficient integration in AD patients
We further showed the coordinated variance of cortical thickness in different brain regions is distributed more randomly in AD patients by investigating nodal entropy in the SCNs

Summary

Introduction

The morphology of cortical gray matter has been widely used for analyzing normal development and aging (Salat et al, 2004; Sowell et al, 2004), degenerative brain diseases (Lerch et al, 2008; Tae et al, 2008; Bernhardt et al, 2009b; Querbes et al, 2009; Koolschijn et al, 2010; Järnum et al, 2011), genetic influence (Panizzon et al, 2009; Winkler et al, 2010), and developmental brain diseases (Shaw et al, 2006, 2007; Hyde et al, 2010; Jiao et al, 2010). The previous SCN-based studies represent significant breakthroughs, they are largely reliant on group-level anatomical correlations of cortical morphology (He et al, 2007, 2008; Bassett et al, 2008; Bernhardt et al, 2011; Zalesky et al, 2012; Zhang et al, 2012) Such group-level SCNs have provided a statistical framework to study synchronized morphology changes in brain regions across populations, it remains unclear how an individual-level SCN directly from a prospective subject’s T1-weighted MR images might be constructed. Raamana et al proposed a new methods to construct the individual SCNs using difference of mean cortical thickness between two regions, which is hard to reflect variance of cortical thickness within a region (Raamana et al, 2014, 2015)

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