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

2019 ◽  
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.

scholarly journals Posterior Cingulate Cortex-Related Co-Activation Patterns: A Resting State fMRI Study in Propofol-Induced Loss of Consciousness

PLoS ONE ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. e100012 ◽  
Author(s):  
Enrico Amico ◽  
Francisco Gomez ◽  
Carol Di Perri ◽  
Audrey Vanhaudenhuyse ◽  
Damien Lesenfants ◽  
...  
Keyword(s):  
Resting State ◽  
Cingulate Cortex ◽  
Resting State Fmri ◽  
Posterior Cingulate Cortex ◽  
Loss Of Consciousness ◽  
Activation Patterns ◽  
Posterior Cingulate ◽  
Fmri Study ◽  
Co Activation

scholarly journals The Ubiquitous Vigilance Signal in fMRI Time Series Data

2021 ◽  
Author(s):  
Javier Gonzalez-Castillo ◽  
Isabel Fernandez ◽  
Daniel A Handwerker ◽  
Peter A Bandettini
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Time Series Data ◽  
Resting State Fmri ◽  
Series Data ◽  
Similar Frequency ◽  
Global Signal ◽  
Human Connectome Project ◽  
Fmri Time Series ◽  
Eye Closure

Vigilance and wakefulness modulate estimates of functional connectivity, and, if unaccounted for, they can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project. Our results confirm the presence of those fluctuations in the HCP sample despite this data having relatively small inflow weighting. Moreover, we show that fluctuations of a similar frequency appear in large portions of grey matter with different temporal delays, and that they can substantially influence estimates of functional connectivity. Finally, we demonstrate that the temporal evolution of this signal cannot only help us reproduce previously reported overall sleep patterns in resting-state data, but also predict individual periods of eye closure with 70% accuracy, matching predictions attainable using the amplitude of the global signal (a common fMRI marker of arousal). In summary, our results demonstrate the ubiquitous presence of this signal in a large, publicly available, fMRI sample, its value as a marker of arousal in absence of a better metric, its relationship to the global signal, and its potential nuisance effects on functional connectivity estimates when ignored.

scholarly journals Co-activation patterns in resting-state fMRI signals

NeuroImage ◽  
2018 ◽  
Vol 180 ◽  
pp. 485-494 ◽  
Author(s):  
Xiao Liu ◽  
Nanyin Zhang ◽  
Catie Chang ◽  
Jeff H. Duyn
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Activation Patterns ◽  
Co Activation

scholarly journals Using Temporal ICA to Selectively Remove Global Noise While Preserving Global Signal in Functional MRI Data

2017 ◽  
Author(s):  
Matthew F. Glasser ◽  
Timothy S. Coalson ◽  
Janine D. Bijsterbosch ◽  
Samuel J. Harrison ◽  
Michael P. Harms ◽  
...  
Keyword(s):  
Brain Activity ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Physiological Noise ◽  
Temporal Noise ◽  
Global Signal ◽  
Human Connectome Project ◽  
Before And After ◽  
Components Analysis ◽  
Subject Motion

AbstractTemporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal “noise” from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread “global” structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary—to do or not to do global signal regression—given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network-specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a “best of both worlds” solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data.

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.

Inter-Hemispherical Investigations on the Functional Connectivity in Controls and Autism Spectrum Using Resting State fMRI

2020 ◽  
pp. 169-186
Author(s):  
S. Vidhusha ◽  
A. Kavitha
Keyword(s):  
Functional Connectivity ◽  
Granger Causality ◽  
Resting State ◽  
Cognitive Task ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Control Group ◽  
Neural Connectivity ◽  
Activation Patterns ◽  
The Brain

Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task. While a number of studies have performed functional connectivity analysis to differentiate controls and autism individuals, this work focuses on analyzing the brain activation patterns not only between controls and autistic subjects, but also analyses the brain behaviour present within autism spectrum. This can bring out more intuitive ways to understand that autism individuals differ individually. This has been performed between autism group relative to the control group using inter-hemispherical analysis. Indications of under connectivity were exhibited by the Granger Causality (GC) and Conditional Granger Causality (CGC) in autistic group. Results show that as connectivity decreases, the GC and CGC values also get decreased. Further, to demark the differences present within the spectrum of autistic individuals, GC and CGC values have been calculated.

Physiological noise model comparison for resting-state fMRI at 7 T

2016 ◽  
Author(s):  
Sandro Nunes ◽  
Marta Bianciardi ◽  
Afonso Dias ◽  
Luis M. Silveira ◽  
Lawrence L. Wald ◽  
...  
Keyword(s):  
Resting State ◽  
Model Comparison ◽  
Resting State Fmri ◽  
Noise Model ◽  
Physiological Noise

scholarly journals The Whole-Brain “Global” Signal from Resting State fMRI as a Potential Biomarker of Quantitative State Changes in Glucose Metabolism

2016 ◽  
Vol 6 (6) ◽  
pp. 435-447 ◽  
Author(s):  
Garth J. Thompson ◽  
Valentin Riedl ◽  
Timo Grimmer ◽  
Alexander Drzezga ◽  
Peter Herman ◽  
...  
Keyword(s):  
Glucose Metabolism ◽  
Resting State ◽  
Resting State Fmri ◽  
Whole Brain ◽  
Global Signal ◽  
Potential Biomarker ◽  
State Changes
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