Differences in the resting-state fMRI global signal amplitude between the eyes open and eyes closed states are related to changes in EEG vigilance

AbstractChanges in vigilance or alertness during a typical resting state fMRI scan are inevitable and have been found to affect measures of functional brain connectivity. Since it is not often feasible to monitor vigilance with EEG during fMRI scans, it would be of great value to have methods for estimating vigilance levels from fMRI data alone. A recent study, conducted in macaque monkeys, proposed a template-based approach for fMRI-based estimation of vigilance fluctuations. Here, we use simultaneously acquired EEG/fMRI data to investigate whether the same template-based approach can be employed to estimate vigilance fluctuations of awake humans across different resting-state conditions. We first demonstrate that the spatial pattern of correlations between EEG-defined vigilance and fMRI in our data is consistent with the previous literature. Notably, however, we observed a significant difference between the eyes-closed (EC) and eyes-open (EO) conditions finding stronger negative correlations with vigilance in regions forming the default mode network and higher positive correlations in thalamus and insula in the EC condition when compared to the EO condition. Taking these correlation maps as “templates” for vigilance estimation, we found that the template-based approach produced fMRI-based vigilance estimates that were significantly correlated with EEG-based vigilance measures, indicating its generalizability from macaques to humans. We also demonstrate that the performance of this method was related to the overall amount of variability in a subject’s vigilance state, and that the template-based approach outperformed the use of the global signal as a vigilance estimator. In addition, we show that the template-based approach can be used to estimate the variability across scans in the amplitude of the vigilance fluctuations. We discuss the benefits and tradeoffs of using the template-based approach in future fMRI studies.

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Resting state connectivity during total sleep deprivation: A functional magnetic resonance study

10.31237/osf.io/3dtxz ◽

2020 ◽

Author(s):

Vivianne Jakobsson

Keyword(s):

Magnetic Resonance ◽

Sleep Deprivation ◽

Resting State ◽

Resting State Fmri ◽

Head Motion ◽

Functional Magnetic Resonance ◽

Global Signal ◽

Significant Difference ◽

Before And After ◽

Eyes Open

Introduction: Sleep deprivation is a common problem that may have serious consequences. In this study, functional magnetic resonance imaging (fMRI), a technique frequently used to study networks in the brain, was used to investigate the resting state of the sleep deprived brain, in order to discover whether this state affects the intrinsic connectivity and the global signal variability (GSV). Aims: To investigate whether GSV increases with sleep deprivation. Material and Methods: In this cross over study 18 healthy participants, age 20 – 30, underwent in randomized order resting-state fMRI for 20min before and after 24h sleep deprivation. We extracted the global signal, calculated the standard deviation per participant, and analysed it with respect to sleep depraved yes/no, head motion, eyes open/closed and self-evaluation of sleepiness using Karolinska Sleepiness Score (KSS). Results: We found that GSV was higher during sleep deprivation (0.3362 ± 0.0241, p<0.0001) without KSS data. With KSS, sleep deprivation was not significant (0.0619 ± 0.1145, p=0.5889). High KSS rating had a significant effect on GSV (0.1497 ± 0.0409, p=0.0003), as had head motion (1.7974 ± 0.1539, p<0.0001). There was no significant difference between having eyes open or closed (0.0126 ± 0.0578, p=0.8278), and no significant increase for each time period of 20s in the scanner (0.0065 ± 0.0021, p=0.0029). Conclusions: We found that the global signal variation is increased by sleep deprivation and sleepiness. More specific conclusions cannot be made from our data so far.

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Functional Network Connectivity in Eyes Open vs. Eyes Closed Resting State FMRI

NeuroImage ◽

10.1016/s1053-8119(09)71499-5 ◽

2009 ◽

Vol 47 ◽

pp. S147

Author(s):

L Wu ◽

VD Calhoun ◽

T Eichele

Keyword(s):

Resting State ◽

Network Connectivity ◽

Resting State Fmri ◽

Functional Network ◽

Functional Network Connectivity ◽

Eyes Closed ◽

Eyes Open

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Eyes-Open/Eyes-Closed Dataset Sharing for Reproducibility Evaluation of Resting State fMRI Data Analysis Methods

Neuroinformatics ◽

10.1007/s12021-013-9187-0 ◽

2013 ◽

Vol 11 (4) ◽

pp. 469-476 ◽

Cited By ~ 48

Author(s):

Dongqiang Liu ◽

Zhangye Dong ◽

Xinian Zuo ◽

Jue Wang ◽

Yufeng Zang

Keyword(s):

Data Analysis ◽

Resting State ◽

Resting State Fmri ◽

Fmri Data ◽

Eyes Closed ◽

Analysis Methods ◽

Fmri Data Analysis ◽

Eyes Open ◽

Data Analysis Methods

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Functional connectivity between the thalamus and visual cortex under eyes closed and eyes open conditions: A resting-state fMRI study

Human Brain Mapping ◽

10.1002/hbm.20728 ◽

2009 ◽

Vol 30 (9) ◽

pp. 3066-3078 ◽

Cited By ~ 104

Author(s):

Qihong Zou ◽

Xiangyu Long ◽

Xinian Zuo ◽

Chaogan Yan ◽

Chaozhe Zhu ◽

...

Keyword(s):

Visual Cortex ◽

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Eyes Closed ◽

Fmri Study ◽

Eyes Open

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The effect of congenital blindness on resting-state functional connectivity revisited

Scientific Reports ◽

10.1038/s41598-021-91976-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Maria J. S. Guerreiro ◽

Madita Linke ◽

Sunitha Lingareddy ◽

Ramesh Kekunnaya ◽

Brigitte Röder

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Visual Experience ◽

Brain Regions ◽

State Condition ◽

Resting State Functional Connectivity ◽

Congenital Blindness ◽

Eyes Closed ◽

Congenitally Blind ◽

Eyes Open

AbstractLower resting-state functional connectivity (RSFC) between ‘visual’ and non-‘visual’ neural circuits has been reported as a hallmark of congenital blindness. In sighted individuals, RSFC between visual and non-visual brain regions has been shown to increase during rest with eyes closed relative to rest with eyes open. To determine the role of visual experience on the modulation of RSFC by resting state condition—as well as to evaluate the effect of resting state condition on group differences in RSFC—, we compared RSFC between visual and somatosensory/auditory regions in congenitally blind individuals (n = 9) and sighted participants (n = 9) during eyes open and eyes closed conditions. In the sighted group, we replicated the increase of RSFC between visual and non-visual areas during rest with eyes closed relative to rest with eyes open. This was not the case in the congenitally blind group, resulting in a lower RSFC between ‘visual’ and non-‘visual’ circuits relative to sighted controls only in the eyes closed condition. These results indicate that visual experience is necessary for the modulation of RSFC by resting state condition and highlight the importance of considering whether sighted controls should be tested with eyes open or closed in studies of functional brain reorganization as a consequence of blindness.

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Rest-task Modulation of fMRI-derived Global Signal Topography is Mediated by Transient Co-activation Patterns

10.1101/798819 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jianfeng Zhang ◽

Zirui Huang ◽

Shankar Tumati ◽

Georg Northoff

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Peak Period ◽

Physiological Noise ◽

Activation Patterns ◽

Global Signal ◽

Co Activation ◽

Human Connectome Project ◽

Partial Dissociation ◽

Signal Correlation

AbstractRecent resting-state fMRI studies have revealed that the global signal (GS) exhibits a non-uniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of global signal topography by analyzing static global signal correlation and dynamic co-activation patterns in a large sample of fMRI dataset (n=837) from the Human Connectome Project. The GS topography in the resting-state and in seven different tasks was first measured by correlating the global signal with the local timeseries (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, while low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient co-activation patterns at the peak period of global signal (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of global signal in the resting-state whereas both differed during the task states; due to such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of global signal topography by showing its rest-task modulation, the underlying dynamic co-activation patterns, and its partial dissociation from respiration effects during task states.

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The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI

10.1101/634063 ◽

2019 ◽

Author(s):

Hannes Almgren ◽

Frederik Van de Steen ◽

Adeel Razi ◽

Karl Friston ◽

Daniele Marinazzo

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Effective Connectivity ◽

Network Connectivity ◽

Resting State Fmri ◽

Measurement Noise ◽

Resting State Networks ◽

Cross Sectional ◽

Global Signal ◽

Leibler Divergence

AbstractThe influence of the global BOLD signal on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting-state networks – as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies; however, individual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler divergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in resting state fMRI.

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A Pilot Study Investigating a Novel Non-Linear Measure of Eyes Open versus Eyes Closed EEG Synchronization in People with Alzheimer’s Disease and Healthy Controls

Brain Sciences ◽

10.3390/brainsci8070134 ◽

2018 ◽

Vol 8 (7) ◽

pp. 134 ◽

Cited By ~ 6

Author(s):

Daniel Blackburn ◽

Yifan Zhao ◽

Matteo De Marco ◽

Simon Bell ◽

Fei He ◽

...

Keyword(s):

Pilot Study ◽

Resting State ◽

Low Cost ◽

Linear Measure ◽

Ageing Population ◽

Healthy Controls ◽

Causality Test ◽

Non Invasive ◽

Eyes Closed ◽

Eyes Open

Background: The incidence of Alzheimer disease (AD) is increasing with the ageing population. The development of low cost non-invasive diagnostic aids for AD is a research priority. This pilot study investigated whether an approach based on a novel dynamic quantitative parametric EEG method could detect abnormalities in people with AD. Methods: 20 patients with probable AD, 20 matched healthy controls (HC) and 4 patients with probable fronto temporal dementia (FTD) were included. All had detailed neuropsychology along with structural, resting state fMRI and EEG. EEG data were analyzed using the Error Reduction Ratio-causality (ERR-causality) test that can capture both linear and nonlinear interactions between different EEG recording areas. The 95% confidence intervals of EEG levels of bi-centroparietal synchronization were estimated for eyes open (EO) and eyes closed (EC) states. Results: In the EC state, AD patients and HC had very similar levels of bi-centro parietal synchronization; but in the EO resting state, patients with AD had significantly higher levels of synchronization (AD = 0.44; interquartile range (IQR) 0.41 vs. HC = 0.15; IQR 0.17, p < 0.0001). The EO/EC synchronization ratio, a measure of the dynamic changes between the two states, also showed significant differences between these two groups (AD ratio 0.78 versus HC ratio 0.37 p < 0.0001). EO synchronization was also significantly different between AD and FTD (FTD = 0.075; IQR 0.03, p < 0.0001). However, the EO/EC ratio was not informative in the FTD group due to very low levels of synchronization in both states (EO and EC). Conclusion: In this pilot work, resting state quantitative EEG shows significant differences between healthy controls and patients with AD. This approach has the potential to develop into a useful non-invasive and economical diagnostic aid in AD.

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