Quantitative Susceptibility Mapping and Resting State Network Analyses in Parkinsonian Phenotypes—A Systematic Review of the Literature

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Abstract

An imbalance of iron metabolism with consecutive aggregation of α-synuclein and axonal degeneration of neurons has been postulated as the main pathological feature in the development of Parkinson’s disease (PD). Quantitative susceptibility mapping (QSM) is a new imaging technique, which enables to measure structural changes caused by defective iron deposition in parkinsonian brains. Due to its novelty, its potential as a new imaging technique remains elusive for disease-specific characterization of motor and non-motor symptoms (characterizing the individual parkinsonian phenotype). Functional network changes associated with these symptoms are however frequently described for both magnetoencephalography (MEG) and resting state functional magnetic imaging (rs-fMRI). Here, we performed a systematic review of the current literature about QSM imaging, MEG and rs-fMRI in order to collect existing data about structural and functional changes caused by motor and non-motor symptoms in PD. Whereas all three techniques provide an effect in the motor domain, the understanding of network changes caused by non-motor symptoms is much more lacking for MEG and rs-fMRI, and does not yet really exist for QSM imaging. In order to better understand the influence of pathological iron distribution onto the functional outcome, whole-brain QSM analyses should be integrated in functional analyses (especially for the non-motor domain), to enable a proper pathophysiological interpretation of MEG and rs-fMRI network changes in PD. Herewith, a better understanding of the relationship between neuropathological changes, functional network changes and clinical phenotype might become possible.

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Pelzer, E. A., Florin, E., & Schnitzler, A. (2019, August 6). Quantitative Susceptibility Mapping and Resting State Network Analyses in Parkinsonian Phenotypes—A Systematic Review of the Literature. Frontiers in Neural Circuits. Frontiers Media S.A. https://doi.org/10.3389/fncir.2019.00050

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