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Resting state dynamic functional connectivity in neurodegenerative conditions: A review of magnetic resonance imaging findings

Authors :
Massimo Filippi
Edoardo G. Spinelli
Camilla Cividini
Federica Agosta
Filippi, M.
Spinelli, E. G.
Cividini, C.
Agosta, F.
Source :
Frontiers in Neuroscience, Frontiers in Neuroscience, Vol 13 (2019)
Publication Year :
2019
Publisher :
Frontiers Media S.A., 2019.

Abstract

In the last few decades, brain functional connectivity (FC) has been extensively assessed using resting-state functional magnetic resonance imaging (RS-fMRI), which is able to identify temporally correlated brain regions known as RS functional networks. Fundamental insights into the pathophysiology of several neurodegenerative conditions have been provided by studies in this field. However, most of these studies are based on the assumption of temporal stationarity of RS functional networks, despite recent evidence suggests that the spatial patterns of RS networks may change periodically over the time of an fMRI scan acquisition. For this reason, dynamic functional connectivity (dFC) analysis has been recently implemented and proposed in order to consider the temporal fluctuations of FC. These approaches hold promise to provide fundamental information for the identification of pathophysiological and diagnostic markers in the vast field of neurodegenerative diseases. This review summarizes the main currently available approaches for dFC analysis and reports their recent applications for the assessment of the most common neurodegenerative conditions, including Alzheimer's disease, Parkinson's disease, dementia with Lewy bodies, and frontotemporal dementia. Critical state-of-the-art findings, limitations, and future perspectives regarding the analysis of dFC in these diseases are provided from both a clinical and a technical point of view.

Details

Language :
English
Database :
OpenAIRE
Journal :
Frontiers in Neuroscience, Frontiers in Neuroscience, Vol 13 (2019)
Accession number :
edsair.doi.dedup.....28dc02a9b7264e09932e6c13defdc1d1