Are early measured resting-state EEG parameters predictive for upper limb motor impairment six months poststroke?

M. Saes; C.G.M. Meskers; A. Daffertshofer; E.E.H. van Wegen; G. Kwakkel

doi:10.1016/j.clinph.2020.09.031

Are early measured resting-state EEG parameters predictive for upper limb motor impairment six months poststroke?

M. Saes
, C.G.M. Meskers
, A. Daffertshofer
, E.E.H. van Wegen
, G. Kwakkel

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

Objectives: Investigate whether resting-state EEG parameters recorded early poststroke can predict upper extremity motor impairment reflected by the Fugl-Meyer motor score (FM-UE) after six months, and whether they have prognostic value in addition to FM-UE at baseline.

Methods: Quantitative EEG parameters delta/alpha ratio (DAR), brain symmetry index (BSI) and directional BSI (BSIdir) were derived from 62-channel resting-state EEG recordings in 39 adults within three weeks after a first-ever ischemic hemispheric stroke. FM-UE scores were acquired within three weeks (FM-UEbaseline) and at 26 weeks poststroke (FM-UEw26). Linear regression analyses were performed using a forward selection procedure to predict FM-UEw26.

Results: BSI calculated over the theta band (BSItheta) (β = −0.40; p = 0.013) was the strongest EEG-based predictor regarding FM-UEw26. BSItheta (β = −0.27; p = 0.006) remained a significant predictor when added to a regression model including FM-UEbaseline, increasing explained variance from 61.5% to 68.1%.

Conclusion: Higher BSItheta values, reflecting more power asymmetry over the hemispheres, predict more upper limb motor impairment six months after stroke. Moreover, BSItheta shows additive prognostic value regarding FM-UEw26 next to FM-UEbaseline scores, and thereby contains unique information regarding upper extremity motor recovery. Significance: To our knowledge, we are the first to show that resting-state EEG parameters can serve as prognostic biomarkers of stroke recovery, in addition to FM-UEbaseline scores.

Original language	English
Pages (from-to)	56-62
Number of pages	7
Journal	Clinical Neurophysiology
Volume	132
Issue number	1
Early online date	3 Nov 2020
DOIs	https://doi.org/10.1016/j.clinph.2020.09.031
Publication status	Published - Jan 2021

Bibliographical note

Funding

In addition to the authors of the present study, the consortium consists of: Caroline Winters, Sarah Zandvliet, Aukje Andringa, Dirk Hoevenaars, Luuk Haring and Elza van Duijnhoven of Amsterdam UMC location VUmc; Jun Yao and Julius Dewald of Northwestern University Chicago; and Frans van der Helm, Martijn Vlaar, Teodoro Solis-Escalante, Alfred Schouten, Yuan Yang, Mark van de Ruit, Konstantina Kalogianni, Joost van Kordelaar, and Lena Filatova of Delft University of Technology. This research was funded by the European Research Council under the European Union's Seventh Framework Programme (FP/2007–2013 ERC Grant Agreement No. 291339, project 4DEEG: A new tool to investigate the spatial and temporal activity patterns in the brain), the Dutch Brain Foundation (F2011(1)-25) and the Netherlands Organization for Scientific Research (NWO). Sponsors had no other involvement than financial support. This research was funded by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007–2013 ERC Grant Agreement No. 291339, project 4DEEG: A new tool to investigate the spatial and temporal activity patterns in the brain), the Dutch Brain Foundation (F2011(1)-25) and the Netherlands Organization for Scientific Research (NWO). Sponsors had no other involvement than financial support.

Funders	Funder number
Dutch Brain Foundation	F2011(1)-25
Seventh Framework Programme
European Research Council	291339
Technische Universiteit Delft
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Seventh Framework Programme

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1016/j.clinph.2020.09.031

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Cite this

@article{52e3d2250fc24340acd4d30c8ebe254f,

title = "Are early measured resting-state EEG parameters predictive for upper limb motor impairment six months poststroke?",

abstract = "Objectives: Investigate whether resting-state EEG parameters recorded early poststroke can predict upper extremity motor impairment reflected by the Fugl-Meyer motor score (FM-UE) after six months, and whether they have prognostic value in addition to FM-UE at baseline. Methods: Quantitative EEG parameters delta/alpha ratio (DAR), brain symmetry index (BSI) and directional BSI (BSIdir) were derived from 62-channel resting-state EEG recordings in 39 adults within three weeks after a first-ever ischemic hemispheric stroke. FM-UE scores were acquired within three weeks (FM-UEbaseline) and at 26 weeks poststroke (FM-UEw26). Linear regression analyses were performed using a forward selection procedure to predict FM-UEw26. Results: BSI calculated over the theta band (BSItheta) (β = −0.40; p = 0.013) was the strongest EEG-based predictor regarding FM-UEw26. BSItheta (β = −0.27; p = 0.006) remained a significant predictor when added to a regression model including FM-UEbaseline, increasing explained variance from 61.5\% to 68.1\%. Conclusion: Higher BSItheta values, reflecting more power asymmetry over the hemispheres, predict more upper limb motor impairment six months after stroke. Moreover, BSItheta shows additive prognostic value regarding FM-UEw26 next to FM-UEbaseline scores, and thereby contains unique information regarding upper extremity motor recovery. Significance: To our knowledge, we are the first to show that resting-state EEG parameters can serve as prognostic biomarkers of stroke recovery, in addition to FM-UEbaseline scores.",

author = "M. Saes and C.G.M. Meskers and A. Daffertshofer and \{van Wegen\}, E.E.H. and G. Kwakkel",

note = "{\textcopyright} 2020 International Federation of Clinical Neurophysiology.",

year = "2021",

month = jan,

doi = "10.1016/j.clinph.2020.09.031",

language = "English",

volume = "132",

pages = "56--62",

journal = "Clinical Neurophysiology",

issn = "1388-2457",