Abstract
Alzheimer's disease (AD) causes the progressive deterioration of neural connections, disrupting structural connectivity (SC) networks within the brain. Graph-based analyses of SC networks have shown that topological properties can reveal the course of AD propagation. Different whole-brain parcellation schemes have been developed to define the nodes of these SC networks, although it remains unclear which scheme can best describe the AD-related deterioration of SC networks. In this study, four whole-brain parcellation schemes with different numbers of parcels were used to define SC network nodes. SC networks were constructed based on high angular resolution diffusion imaging (HARDI) tractography for a mixed cohort that includes 20 normal controls (NC), 20 early mild cognitive impairment (EMCI), 20 late mild cognitive impairment (LMCI), and 20 AD patients, from the Alzheimer's Disease Neuroimaging Initiative. Parcellation schemes investigated in this study include the OASIS-TRT-20 (62 regions), AAL (116 regions), HCP-MMP (180 regions), and Gordon-rsfMRI (333 regions), which have all been widely used for the construction of brain structural or functional connectivity networks. Topological characteristics of the SC networks, including the network strength, global efficiency, clustering coefficient, rich-club, characteristic path length, k-core, rich-club coefficient, and modularity, were fully investigated at the network level. Statistical analyses were performed on these metrics using Kruskal-Wallis tests to examine the group differences that were apparent at different stages of AD progression. Results suggest that the HCP-MMP scheme is the most robust and sensitive to AD progression, while the OASIS-TRT-20 scheme is sensitive to group differences in network strength, global efficiency, k-core, and rich-club coefficient at k-levels from 18 and 39. With the exception of the rich-club and modularity coefficients, AAL could not significantly identify group differences on other topological metrics. Further, the Gordon-rsfMRI atlas only significantly differentiates the groups on network strength, characteristic path length, k-core, and rich-club coefficient. Results show that the topological examination of SC networks with different parcellation schemes can provide important complementary AD-related information and thus contribute to a more accurate and earlier diagnosis of AD.
Highlights
As the leading cause of dementia in elderly adults, Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by increasing cognitive and behavioral deficits (Mueller et al, 2005)
The primary goal of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD (Jack et al, 2008; Risacher et al, 2009; Petersen et al, 2010)
P-values derived from Kruskal-Wallis tests assessing the structural connectivity (SC) differences among normal controls (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD groups for each parcellation scheme are reported in Table 3
Summary
As the leading cause of dementia in elderly adults, Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by increasing cognitive and behavioral deficits (Mueller et al, 2005). The WM degeneration and neuronal death linked to AD progression creates abnormal connectivity patterns between anatomically related brain regions (Lo et al, 2010). AD-related cognitive and behavioral deficits may be directly linked the disconnection of brain regions (Delbeuck et al, 2003; Sorg et al, 2009; Lo et al, 2010), such that altered SC topological patterns reflect the propagation stage of AD
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