Effects of Brain Parcellation on the Characterization of Topological Deterioration in Alzheimer's Disease
Year: 2019
Type: article
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.
Source: Frontiers in Aging Neuroscience
Authors: Zhanxiong Wu, Dong Xu, Thomas Potter, Yingchun Zhang, The Alzheimer's Disease Neuroimaging Initiative
Institutions: University of Houston, Hangzhou Dianzi University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,801
Funders: U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, Meso Scale Diagnostics, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, National Natural Science Foundation of China, Eisai, China Scholarship Council, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, Canadian Institutes of Health Research, National Institute on Aging, Natural Science Foundation of Zhejiang Province
Awards: G104586243, G1298807586, G1427375825, G1554142629, G1593117279, G1650819308, G1700196019, G1880935189, G2100670906, G2152643862, G2189010064, G2229837479, G2340207221, G2423387114, G2471646051, G2522063444, G2764785051, G2835601961, G29951154, G3113887960, G3117452623, G3131374986, G3216574877, G3553156159, G3650152919, G3710456697, G3752451908, G3870774570, G3908963977, G4007417716, G4119534169, G43180419, G4475667458, G4501223628, G470063782, G4704239422, G4800177743, G4882004024, G5207404921, G5209990037, G528601746, G5305279072, G5324093878, G5365730499, G5481078219, G5654639750, Alzheimer's Disease Neuroimaging Initiative, G5740349702, G5850682332, G6063043944, G6114289822, G613619302, G6173816828, G6210868510, G6371410416, G6412110892, G6447063303, G6670900644, G6719828067, G6792506723, G6899163935, G6964283838, G7043785753, G72415811, G7298697282, G7568662538, G764699806, G7799614282, G781725486, G8151151163, G8228018387, G8268523665, G8281378126, G8362422284, G839208290, G8502247715, G8852128998, G8895907669, G8897063270, G8957659042, G941155069
OpenAlex ID: W2945915851