scholarly journals Correction: Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity

PLoS Biology ◽  
2021 ◽  
Vol 19 (5) ◽  
pp. e3001258
Author(s):  
Csaba Orban ◽  
Ru Kong ◽  
Jingwei Li ◽  
Michael W. L. Chee ◽  
B. T. Thomas Yeo
Keyword(s):  
Functional Connectivity ◽  
Time Of Day ◽  
Global Signal ◽  
Signal Fluctuation

scholarly journals Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity

2019 ◽  
Author(s):  
Csaba Orban ◽  
Ru Kong ◽  
Jingwei Li ◽  
Michael W.L. Chee ◽  
B. T. Thomas Yeo
Keyword(s):  
Functional Connectivity ◽  
Diurnal Variation ◽  
Resting State ◽  
Fluid Intelligence ◽  
Circadian Variation ◽  
Signal Amplitude ◽  
Time Of Day ◽  
Resting State Functional Connectivity ◽  
Global Signal ◽  
Signal Fluctuation

1.AbstractThe brain exhibits substantial diurnal variation in physiology and function but neuroscience studies rarely report or consider the effects of time of day. Here, we examined variation in resting-state fMRI in around 900 subjects scanned between 8am to 10pm on two different days. Multiple studies across animals and humans have demonstrated that the brain’s global signal amplitude (henceforth referred to as “fluctuation”) increases with decreased arousal. Thus, in accord with known circadian variation in arousal, we hypothesised that global signal fluctuation would be lowest in the morning, increase in the mid-afternoon and dip in the early evening. Instead, we observed a cumulative decrease (22% between 9am to 9pm) in global signal fluctuation as the day progressed. To put the magnitude of this decrease in context, we note that task-evoked fMRI responses are typically in the order of 1% to 3%. Respiratory variation also decreased with time of day, although control analyses suggested that this did not account for the reduction in GS fluctuation. Finally, time of day was associated with marked decreases in resting state functional connectivity across the whole brain. The magnitude of decrease was significantly stronger than associations between functional connectivity and behaviour (e.g., fluid intelligence). These findings reveal unexpected effects of time of day on the resting human brain, which challenge the prevailing notion that the brain’s global signal reflects mostly arousal and physiological artefacts. We conclude by discussing potential mechanisms for the observed diurnal variation in resting brain activity and the importance of accounting for time of day in future studies.

scholarly journals Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity

PLoS Biology ◽  
2020 ◽  
Vol 18 (2) ◽  
pp. e3000602 ◽  
Author(s):  
Csaba Orban ◽  
Ru Kong ◽  
Jingwei Li ◽  
Michael W. L. Chee ◽  
B. T. Thomas Yeo
Keyword(s):  
Functional Connectivity ◽  
Time Of Day ◽  
Global Signal ◽  
Signal Fluctuation

scholarly journals Beyond the low frequency fluctuations morning and evening differences in human brain

2019 ◽  
Author(s):  
Magdalena Fafrowicz ◽  
Bartosz Bohaterewicz ◽  
Anna Ceglarek ◽  
Monika Cichocka ◽  
Koryna Lewandowska ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Human Performance ◽  
Brain Activity ◽  
Activity Patterns ◽  
Low Frequency ◽  
Basic Mechanism ◽  
Time Of Day ◽  
Resting State Functional Connectivity ◽  
Circadian Typology

Human performance, alertness, and most biological functions express rhythmic fluctuations across a 24-hour-period. This phenomenon is believed to originate from differences in both circadian and homeostatic sleep-wake regulatory processes. Interactions between these processes result in time-of-day modulations of behavioral performance as well as brain activity patterns. Although the basic mechanism of the 24-hour clock is conserved across evolution, there are interindividual differences in the timing of sleep-wake cycles, subjective alertness and functioning throughout the day. The study of circadian typology differences has increased during the last few years, especially research on extreme chronotypes, which provide a unique way to investigate the effects of sleep-wake regulation on cerebral mechanisms. Using functional magnetic resonance imaging (fMRI), we assessed the influence of chronotype and time-of-day on resting-state functional connectivity. 29 extreme morning- and 34 evening-type participants underwent two fMRI sessions: about one hour after wake-up time (morning) and about ten hours after wake-up time (evening), scheduled according to their declared habitual sleep-wake pattern on a regular working day. Analysis of obtained neuroimaging data disclosed only an effect of time of day on resting-state functional connectivity; there were different patterns of functional connectivity between morning and evening sessions. The results of our study showed no differences between extreme morning-type and evening-type individuals. We demonstrate that circadian and homeostatic influences on the resting-state functional connectivity have a universal character, unaffected by circadian typology.

scholarly journals Infant functional networks are modulated by state of consciousness and circadian rhythm

2021 ◽  
pp. 1-36
Author(s):  
Rachel J. Smith ◽  
Ehsan Alipourjeddi ◽  
Cristal Garner ◽  
Amy L. Maser ◽  
Daniel W. Shrey ◽  
...  
Keyword(s):  
Circadian Rhythm ◽  
Functional Connectivity ◽  
Principal Component ◽  
Time Of Day ◽  
Clustering Coefficient ◽  
Functional Networks ◽  
Temporal Properties ◽  
Healthy Infants ◽  
Infant Brain ◽  
Eeg Recordings

Abstract Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.8 hours) from 19 healthy infants. Networks were subjectspecific, as inter-subject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. Principal component analysis revealed the presence of two dominant networks; visual sleep scoring confirmed that these corresponded to sleep and wakefulness. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in either wakefulness or sleep at the group level. Together, these results suggest that modulation of healthy functional networks occurs over ~24 hours and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.

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

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.

scholarly journals Optimizing and Interpreting Insular Functional Connectivity Maps Obtained During Acute Experimental Pain: The Effects of Global Signal and Task Paradigm Regression

2015 ◽  
Vol 5 (10) ◽  
pp. 649-657 ◽  
Author(s):  
James W. Ibinson ◽  
Keith M. Vogt ◽  
Kevin B. Taylor ◽  
Shiv B. Dua ◽  
Christopher J. Becker ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Experimental Pain ◽  
Global Signal ◽  
And Task

scholarly journals Spatiotemporal empirical mode decomposition of resting-state fMRI signals: application to global signal regression

2019 ◽  
Author(s):  
Narges Moradi ◽  
Mehdy Dousty ◽  
Roberto C. Sotero
Keyword(s):  
Functional Connectivity ◽  
Empirical Mode Decomposition ◽  
Resting State ◽  
Large Scale ◽  
Pearson Correlation ◽  
Brain Regions ◽  
Connectivity Matrix ◽  
Correlation Pattern ◽  
Mode Decomposition ◽  
Global Signal

AbstractResting-state functional connectivity MRI (rs-fcMRI) is a common method for mapping functional brain networks. However, estimation of these networks is affected by the presence of a common global systemic noise, or global signal (GS). Previous studies have shown that the common preprocessing steps of removing the GS may create spurious correlations between brain regions. In this paper, we decompose fMRI signals into 5 spatial and 3 temporal intrinsic mode functions (SIMF and TIMF, respectively) by means of the empirical mode decomposition (EMD), which is an adaptive data-driven method widely used to analyze nonlinear and nonstationary phenomena. For each SIMF, brain connectivity matrices were computed by means of the Pearson correlation between TIMFs of different brain areas. Thus, instead of a single connectivity matrix, we obtained 5 × 3 = 15 functional connectivity matrices. Given the high value obtained for large-scale topological measures such as transitivity, in the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, the spatiotemporal EMD of fMRI signals automatically regressed out the GS, although, interestingly, the removed noisy component was voxel-specific. We compared the performance of our method with the conventional GS regression and to the results when the GS was not removed. While the correlation pattern identified by the other methods suffers from a low level of precision, our approach demonstrated a high level of accuracy in extracting the correct correlation between different brain regions.

scholarly journals Benchmarking common preprocessing strategies in early childhood functional connectivity MRI

2020 ◽  
Author(s):  
Kirk Graff ◽  
Ryann Tansey ◽  
Amanda Ip ◽  
Christiane Rohr ◽  
Dennis Dimond ◽  
...  
Keyword(s):  
Early Childhood ◽  
Functional Connectivity ◽  
Young Children ◽  
Noise Removal ◽  
Head Motion ◽  
List Type ◽  
Unique Challenge ◽  
Global Signal ◽  
Passive Viewing ◽  
The Impact

AbstractFunctional connectivity magnetic resonance imaging (FC-MRI) has been widely used to investigate neurodevelopment. However, FC-MRI is vulnerable to head motion, which is associated with age and distorts FC estimates. Numerous preprocessing strategies have been developed to mitigate confounds, each with advantages and drawbacks. Preprocessing strategies for FC-MRI have typically been validated and compared using resting state data from adults. However, FC-MRI in young children presents a unique challenge due to relatively high head motion and a growing use of passive viewing paradigms to mitigate motion. This highlights a need to compare processing choices in pediatric samples. To this end, we leveraged longitudinal, passive viewing fMRI data collected from 4 to 8-year-old children. We systematically investigated combinations of widely used and debated preprocessing strategies, namely global signal regression, volume censoring, ICA-AROMA, and bandpass filtering. We implemented commonly used metrics of noise removal (i.e. quality control-functional connectivity), metrics sensitive to individual differences (i.e. connectome fingerprinting), and, because data was collected during a passive viewing task, we also assessed the impact on stimulus-evoked responses (i.e. intersubject correlations; ISC). We found that the most efficacious pipeline included censoring, global signal regression, bandpass filtering, and head motion parameter regression. Despite the drawbacks of noise-mitigation steps, our findings show benefits for both noise removal and information retention in a high-motion early childhood sample.Highlights- We evaluated 27 preprocessing pipelines in passive viewing data from young children- Pipelines were evaluated on noise-removed and information retained- Pipelines that included censoring and GSR outperformed alternatives across benchmarks- For high-motion scans, preprocessing choices substantially alter connectomes

scholarly journals Evaluation of confound regression strategies for the mitigation of micromovement artifact in studies of dynamic resting-state functional connectivity and multilayer network modularity

2019 ◽  
Vol 3 (2) ◽  
pp. 427-454 ◽  
Author(s):  
David M. Lydon-Staley ◽  
Rastko Ciric ◽  
Theodore D. Satterthwaite ◽  
Danielle S. Bassett
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Systematic Evaluation ◽  
Resting State Functional Connectivity ◽  
Multilayer Network ◽  
Dynamic Functional Connectivity ◽  
Spontaneous Brain Activity ◽  
Global Signal ◽  
Health And Disease ◽  
Modularity Maximization

Dynamic functional connectivity reflects the spatiotemporal organization of spontaneous brain activity in health and disease. Dynamic functional connectivity may be susceptible to artifacts induced by participant motion. This report provides a systematic evaluation of 12 commonly used participant-level confound regression strategies designed to mitigate the effects of micromovements in a sample of 393 youths (ages 8–22 years). Each strategy was evaluated according to a number of benchmarks, including (a) the residual association between participant motion and edge dispersion, (b) distance-dependent effects of motion on edge dispersion, (c) the degree to which functional subnetworks could be identified by multilayer modularity maximization, and (d) measures of module reconfiguration, including node flexibility and node promiscuity. Results indicate variability in the effectiveness of the evaluated pipelines across benchmarks. Methods that included global signal regression were the most consistently effective de-noising strategies.

scholarly journals Segregation of face sensitive areas within the fusiform gyrus using global signal regression? A study on amygdala resting-state functional connectivity

2015 ◽  
Vol 36 (10) ◽  
pp. 4089-4103 ◽  
Author(s):  
Johann D. Kruschwitz ◽  
Andreas Meyer-Lindenberg ◽  
Ilya M. Veer ◽  
Carolin Wackerhagen ◽  
Susanne Erk ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Fusiform Gyrus ◽  
Resting State Functional Connectivity ◽  
Sensitive Areas ◽  
Global Signal
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