Publication
Title
Resting-state co-activation patterns as promising candidates for prediction of Alzheimer's disease in aged mice
Author
Abstract
Alzheimer's disease (AD), a neurodegenerative disorder marked by accumulation of extracellular amyloid-β (Aβ) plaques leads to progressive loss of memory and cognitive function. Resting-state fMRI (RS-fMRI) studies have provided links between these two observations in terms of disruption of default mode and task-positive resting-state networks (RSNs). Important insights underlying these disruptions were recently obtained by investigating dynamic fluctuations in RS-fMRI signals in old TG2576 mice (a mouse model of amyloidosis) using a set of quasi-periodic patterns (QPP). QPPs represent repeating spatiotemporal patterns of neural activity of predefined temporal length. In this article, we used an alternative methodology of co-activation patterns (CAPs) that represent instantaneous and transient brain configurations that are likely contributors to the emergence of commonly observed RSNs and QPPs. We followed a recently published approach for obtaining CAPs that divided all time frames, instead of those corresponding to supra-threshold activations of a seed region as done traditionally, to extract CAPs from RS-fMRI recordings in 10 TG2576 female mice and eight wild type littermates at 18 months of age. Subsequently, we matched the CAPs from the two groups using the Hungarian method and compared the temporal (duration, occurrence rate) and the spatial (lateralization of significantly co-activated and co-deactivated voxels) properties of matched CAPs. We found robust differences in the spatial components of matched CAPs. Finally, we used supervised learning to train a classifier using either the temporal or the spatial component of CAPs to distinguish the transgenic mice from the WT. We found that while duration and occurrence rates of all CAPs performed the classification with significantly higher accuracy than the chance-level, blood oxygen level-dependent (BOLD) signals of significantly activated voxels from individual CAPs turned out to be a significantly better predictive feature demonstrating a near-perfect classification accuracy. Our results demonstrate resting-state co-activation patterns are a promising candidate in the development of a diagnostic, and potentially, prognostic RS-fMRI biomarker of AD.
Language
English
Source (journal)
Frontiers in Neural Circuits
Publication
2021
ISSN
1662-5110
DOI
10.3389/FNCIR.2020.612529
Volume/pages
14 (2021) , 15 p.
Article Reference
612529
ISI
000614749700001
Pubmed ID
33551755
Medium
E-only publicatie
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Project info
Improved classification of Alzheimer's disease: differentiation of slow propagating waves of BOLD intensity of dynamic rsfMRI in AD mice models in pre-plaque and post-plaque stages.
Multimodal Imaging of cholinergic neuromodulation during specific memory phases in the rodent brain.
Neuro Image-guided decoding of mechanisms involved in healthy, accelerated and pathological aging.
Amyloid β and sleep problems, a neurotoxic pas de deux during aging?
Improved classification of Alzheimer's disease assessed from the slowly propagating waves of BOLD intensity, the Quasi-Periodic patterns, observed in dynamic resting-state fMRI in a AD rat model at rest and upon sensory stimulation.
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 03.01.2021
Last edited 02.10.2024
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