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research article

Resting-state EEG topographies: Reliable and sensitive signatures of unilateral spatial neglect

Pirondini, Elvira  
•
Goldshuv-Ezra, Nurit
•
Zinger, Nofya
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January 1, 2020
Neuroimage-Clinical

Theoretical advances in the neurosciences are leading to the development of an increasing number of proposed interventions for the enhancement of functional recovery after brain damage. Integration of these novel approaches in clinical practice depends on the availability of reliable, simple, and sensitive biomarkers of impairment level and extent of recovery, to enable an informed clinical-decision process. However, the neuropsychological tests currently in use do not tap into the complex neural re-organization process that occurs after brain insult and its modulation by treatment. Here we show that topographical analysis of resting-state electroencephalography (rsEEG) patterns using singular value decomposition (SVD) could be used to capture these processes. In two groups of subacute stroke patients, we show reliable detection of deviant neurophysiological patterns over repeated measurement sessions on separate days. These patterns generalized across patients groups. Additionally, they maintained a significant association with ipsilesional attention bias, discriminating patients with spatial neglect of different severity levels. The sensitivity and reliability of these rsEEG topographical analyses support their use as a tool for monitoring natural and treatment-induced recovery in the rehabilitation process.

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Type
research article
DOI
10.1016/j.nicl.2020.102237
Web of Science ID

WOS:000533149400055

Author(s)
Pirondini, Elvira  
Goldshuv-Ezra, Nurit
Zinger, Nofya
Britz, Juliane
Soroker, Nachum
Deouell, Leon Y.
Van De Ville, Dimitri  
Date Issued

2020-01-01

Published in
Neuroimage-Clinical
Volume

26

Article Number

102237

Subjects

Neuroimaging

•

Neurosciences & Neurology

•

unilateral spatial neglect

•

stroke

•

rehabilitation

•

outcome measurement

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computer-enhanced measurement, eeg analysis

•

eeg topography features

•

resting-state eeg biomarkers

•

machine learning

•

quantitative eeg

•

attention

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stimulation

•

neurofeedback

•

connectivity

•

variability

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networks

•

dynamics

•

parietal

Note

This is an open access article under the CC BY license.

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

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MIPLAB  
TNE  
Available on Infoscience
May 31, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/169024
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