Altered whole-brain white matter networks in preclinical Alzheimer's disease

Abstract

Surrogates of whole-brain white matter (WM) networks reconstructed using diffusion tensor imaging (DTI) are novel markers of structural brain connectivity. Global connectivity of networks has been found impaired in clinical Alzheimer's disease (AD) compared to cognitively healthy aging. We hypothesized that network alterations are detectable already in preclinical AD and investigated major global WM network properties. Other structural markers of neurodegeneration typically affected in prodromal AD but seeming largely unimpaired in preclinical AD were also examined.12 cognitively healthy elderly with preclinical AD as classified by florbetapir-PET (mean age 73.4 ± 4.9) and 31 age-matched controls without cerebral amyloidosis (mean age 73.1 ± 6.7) from the ADNI were included. WM networks were reconstructed from DTI using tractography and graph theory. Indices of network capacity and the established imaging markers of neurodegeneration hippocampal volume, and cerebral glucose utilization as measured by fludeoxyglucose-PET were compared between the two groups. Additionally, we measured surrogates of global WM integrity (fractional anisotropy, mean diffusivity, volume).We found an increase of shortest path length and a decrease of global efficiency in preclinical AD. These results remained largely unchanged when controlling for WM integrity. In contrast, neither markers of neurodegeneration nor WM integrity were altered in preclinical AD subjects.Our results suggest an impairment of WM networks in preclinical AD that is detectable while other structural imaging markers do not yet indicate incipient neurodegeneration. Moreover, these findings are specific to WM networks and cannot be explained by other surrogates of global WM integrity.

Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment

“…The progress of neuroimaging techniques, in particular diffusion magnetic resonance imaging (dMRI) and functional MRI, as well as post-processing (tractography) and network analysis methods, has enabled the in vivo study of the structural and functional connectivity that form the brain connectome. Connectomics has so far confirmed the impairment of network connectivity in AD observed by neuroanatomical and neuropathological studies, supporting the role of white matter degeneration in the disease, and the fact that part of the symptoms are due to disconnection of distant cortical regions (Daianu et al, 2013b(Daianu et al, , 2013aFischer et al, 2015;Mallio et al, 2015;Nir et al, 2012Nir et al, , 2015Prescott et al, 2014).…”

Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of 18F-FDG PET Data

“…Neurodegenerative processes affect modular networks rather than isolated regions (16). Long-distance interregional metabolic correlations are impaired by the anatomofunctional progression of neuronal and white matter fiber degeneration (17), causing local compensatory networks to take over and increasing the anatomic and functional segregation of brain processes.…”

“…Also, the number of voxels composing the independent components progressively increased from control subjects (17,491) to AD patients (51,740). We highlight here only the independent components with pathophysiologic significance, and the brain regions included in them (Table 2).…”

Measuring Cortical Connectivity in Alzheimer’s Disease as a Brain Neural Network Pathology: Toward Clinical Applications

“…Regions less connected were less likely to aggregate amyloid. (Fischer et al, 2015) DTI, Amyloid-PET Graph-theoretical measures of structural connectivity revealed significant group differences between 31 amyloid-negative cognitively healthy subjects vs. 12 amyloid-positive cognitively healthy subjects.…”

“…In AD dementia, structural connectivity networks exhibit altered topological network metrics, such as increased shortest path lengths, decreased local and global efficiency, and decreased number of rich-club hub nodes (Daianu et al, 2013(Daianu et al, , 2015Lo et al, 2010;Shao et al, 2012). These topological network changes were also shown to account for core memory and executive function deficits (Reijmer et al, 2013), and were already detectable in cognitively normal individuals with high amyloid burden (Fischer, Wolf, Scheurich, & Fellgiebel, 2015) and asymptomatic APOE4 carriers (Brown et al, 2011). However, current DTI-based fiber tracking algorithms are limited in their ability to resolve crossing and touching fiber bundles, which are highly prevalent fiber configurations in the human white matter (Jeurissen, Leemans, Tournier, Jones, & Sijbers, 2013).…”