English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  BOLD and EEG signal variability at rest differently relate to aging in the human brain

Kumral, D., Sansal, F., Cesnaite, E., Mahjoory, K., Al, E., Gaebler, M., et al. (2020). BOLD and EEG signal variability at rest differently relate to aging in the human brain. NeuroImage, 207: 116373. doi:10.1016/j.neuroimage.2019.116373.

Item is

Files

show Files
hide Files
:
Kumral_2020.pdf (Publisher version), 2MB
Name:
Kumral_2020.pdf
Description:
-
OA-Status:
Gold
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

show
hide
 Creators:
Kumral, Deniz1, 2, Author           
Sansal, Firat1, 3, Author           
Cesnaite, Elena1, Author           
Mahjoory, Keyvan1, 4, Author           
Al, Esra1, 2, Author           
Gaebler, Michael1, 2, Author           
Nikulin, Vadim V.1, 5, 6, Author           
Villringer, Arno1, 2, 7, 8, Author           
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2MindBrainBody Institute, Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              
3International Graduate Program Medical Neurosciences, Charité University Medicine Berlin, Germany, ou_persistent22              
4Institute for Biomagnetism and Biosignal Analysis, Münster University, Germany, ou_persistent22              
5Neurophysics Group, Department of Neurology, Charité University Medicine Berlin, Germany, ou_persistent22              
6Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia, ou_persistent22              
7Center for Stroke Research, Charité University Medicine Berlin, Germany, ou_persistent22              
8Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              

Content

show
hide
Free keywords: Brain signal variability; Resting state; BOLD; fMRI; EEG; Aging; Sex; Default mode network
 Abstract: Variability of neural activity is regarded as a crucial feature of healthy brain function, and several neuroimaging approaches have been employed to assess it noninvasively. Studies on the variability of both evoked brain response and spontaneous brain signals have shown remarkable changes with aging but it is unclear if the different measures of brain signal variability – identified with either hemodynamic or electrophysiological methods – reflect the same underlying physiology. In this study, we aimed to explore age differences of spontaneous brain signal variability with two different imaging modalities (EEG, fMRI) in healthy younger (25 ± 3 years, N = 135) and older (67 ± 4 years, N = 54) adults. Consistent with the previous studies, we found lower blood oxygenation level dependent (BOLD) variability in the older subjects as well as less signal variability in the amplitude of low-frequency oscillations (1–12 Hz), measured in source space. These age-related reductions were mostly observed in the areas that overlap with the default mode network. Moreover, age-related increases of variability in the amplitude of beta-band frequency EEG oscillations (15–25 Hz) were seen predominantly in temporal brain regions. There were significant sex differences in EEG signal variability in various brain regions while no significant sex differences were observed in BOLD signal variability. Bivariate and multivariate correlation analyses revealed no significant associations between EEG- and fMRI-based variability measures. In summary, we show that both BOLD and EEG signal variability reflect aging-related processes but are likely to be dominated by different physiological origins, which relate differentially to age and sex.

Details

show
hide
Language(s): eng - English
 Dates: 2019-10-172019-08-232019-11-172019-11-202020-02-15
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.neuroimage.2019.116373
Other: epub 2019
PMID: 31759114
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: NeuroImage
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 207 Sequence Number: 116373 Start / End Page: - Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166