Structural connectivity centrality changes mark the path toward Alzheimer's disease

Abstract

Introduction The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting prion-like spreading processes of neurofibrillary tangles and amyloid plaques. Methods Using diffusion magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative database, we first identified relevant features for dementia diagnosis. We then created dynamic models with the Nathan Kline Institute-Rockland Sample database to estimate the earliest detectable stage associated with dementia in the simulated disease progression. Results A classifier based on centrality measures provides informative predictions. Strength and closeness centralities are the most discriminative features, which are associated with the medial temporal lobe and subcortical regions, together with posterior and occipital brain regions. Our model simulations suggest that changes associated with dementia begin to manifest structurally at early stages. Discussion Our analyses suggest that diffusion magnetic resonance imaging–based centrality measures can offer a tool for early disease detection before clinical dementia onset.

Classification and Graphical Analysis of Alzheimer’s Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype

“…We used NiftyReg ( Young et al, 2013 ; Gupta et al, 2019a ), the pyClusterROI script ( Craddock et al, 2012 ), PANDA ( Cui et al, 2013 ), DPRASF ( Chao-Gan and Yu-Feng, 2010 ), and the CAT12 toolbox with the integration of SPM12 ( Ashburner and Friston, 2001 ) for the extraction of features from the structural and functional neuroimaging data. Furthermore, graph-based analysis ( John et al, 2017 ; Peraza et al, 2019 ) was performed to study the organization of (nodal and group) network connectivity using anatomical features (including GM volume, cortical thickness, and WM pathways between GM regions), and using the regional time series of the 200 brain regions included in the Craddock atlas. For this graph-based analysis, we used the BRAPH toolbox ( Mijalkov et al, 2017 ).…”

Communicability distance reveals hidden patterns of Alzheimer’s disease

“…The hypothesis of the self-propagation of AD in combination with network neurosciences has triggered the use of epidemiological models on networks to simulate the propagation of a disease factor as AD progresses. In particular, Peraza et al (2019) have proposed the use of the susceptible-infected (SI) model on networks (see, for instance, Canright & Engø-Monsen, Susceptible-infected model: Epidemiological model where individuals can be either susceptible to a disease or be infected by it.…”

Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings

“…As opposed to previous studies which considered highly detailed, non-linear, stochastic models to simulate the activity of each brain region in detail [35,37,40,43,[79][80][81], here we considered an abstract model of epidemic spreading, the SI model, as a proxy for seizure propagation dynamics (see figures 3 and 4). Epidemic models capture the basic mechanisms of processes that diffuse on networked systems, and have been used, for example, to study the propagation of pathological proteins on brain networks [52] and of ictal activity [41].…”

“…These models simulate the propagation of an agent from some given location to other connected areas, a basic phenomenon appearing in a multitude of systems. Such models have been used, for instance, to study the spreading of pathological proteins on brain networks [52], or the relation between brain structure and function [53].…”