Abstract
BackgroundAlthough genetic risk factors and network-level neuroimaging abnormalities have shown effects on cognitive performance and brain atrophy in Alzheimer’s disease (AD), little is understood about how apolipoprotein E (APOE) ε4 allele, the best-known genetic risk for AD, affect brain connectivity before the onset of symptomatic AD. This study aims to investigate APOE ε4 effects on brain connectivity from the perspective of multimodal connectome.ResultsHere, we propose a novel multimodal brain network modeling framework and a network quantification method based on persistent homology for identifying APOE ε4-related network differences. Specifically, we employ sparse representation to integrate multimodal brain network information derived from both the resting state functional magnetic resonance imaging (rs-fMRI) data and the diffusion-weighted magnetic resonance imaging (dw-MRI) data. Moreover, persistent homology is proposed to avoid the ad hoc selection of a specific regularization parameter and to capture valuable brain connectivity patterns from the topological perspective. The experimental results demonstrate that our method outperforms the competing methods, and reasonably yields connectomic patterns specific to APOE ε4 carriers and non-carriers.ConclusionsWe have proposed a multimodal framework that integrates structural and functional connectivity information for constructing a fused brain network with greater discriminative power. Using persistent homology to extract topological features from the fused brain network, our method can effectively identify APOE ε4-related brain connectomic biomarkers.
Highlights
Genetic risk factors and network-level neuroimaging abnormalities have shown effects on cognitive performance and brain atrophy in Alzheimer’s disease (AD), little is understood about how apolipoprotein E (APOE) ε4 allele, the best-known genetic risk for AD, affect brain connectivity before the onset of symptomatic AD
All magnetic resonance imaging (MRI) data were acquired with a Siemens 3 T scanner with the following parameters: (1) resting state functional magnetic resonance imaging (rs-fMRI) data involved that Echo time (TE) = 30 ms, Repetition time (TR) = 3000 s, Filp angle (FA) = 90 degree, slice thickness = 3.4 mm, the number of slices = 197; (2) diffusion-weighted magnetic resonance imaging (dw-MRI) data were acquired with gradient directions = 54, TE = 56 ms, TR = 7200 ms, voxel size = 2 × 2 × 2mm3, FA = 90 degree; (3) T1 image data were acquired with FA = 9 degree, acquisition plane = SAGITTAL, slice thickness = 1.2 mm, TE = 2.95 ms, T1 = 900 ms, TR = 2300 ms
We found that the persistent homology (PH) metrics reflect the difference of networks better than graph metrics like local efficiency (LE), BET, global efficiency (GE), and clustering coefficient (CCO)
Summary
Genetic risk factors and network-level neuroimaging abnormalities have shown effects on cognitive performance and brain atrophy in Alzheimer’s disease (AD), little is understood about how apolipoprotein E (APOE) ε4 allele, the best-known genetic risk for AD, affect brain connectivity before the onset of symptomatic AD. An ample number of studies [9, 10] have investigated the brain connectivity features of APOE ε4 carriers These existing studies found specific and consistent alterations in brain network, especially involving decreased functional connectivity within default mode network (DMN). Most of these approaches for characterizing APOE-related network differences are based on pairwise correlation such as Pearson’s correlation. Some studies [11, 12] have demonstrated that the neurological processes involve the interactions of many co-activated brain regions (i.e., more than two brain regions) rather than just the pairwise variant-trait associations
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