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

Tracking whole-brain connectivity dynamics in the resting-state fMRI with post-facial paralysis synkinesis

2021 ◽  
Author(s):  
Zhen-Zhen Ma ◽  
Jia-Jia Wu ◽  
Xu-Yun Hua ◽  
Mou-Xiong Zheng ◽  
Xiang-Xin Xing ◽  
...  
Keyword(s):  
Facial Paralysis ◽  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Whole Brain

scholarly journals Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy

NeuroImage ◽  
2021 ◽  
Vol 231 ◽  
pp. 117844
Author(s):  
Behzad Iravani ◽  
Artin Arshamian ◽  
Peter Fransson ◽  
Neda Kaboodvand
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Adhd Subtypes ◽  
Whole Brain

scholarly journals Corrigendum to “Resting-state fMRI correlations: From link-wise unreliability to whole brain stability” [NeuroImage 157 (2017 Aug 15) 250–262]

NeuroImage ◽  
2018 ◽  
Vol 174 ◽  
pp. 599-604 ◽  
Author(s):  
M. Pannunzi ◽  
R. Hindriks ◽  
R.G. Bettinardi ◽  
E. Wenger ◽  
N. Lisofsky ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Whole Brain

scholarly journals Disrupted Topological Organization in Whole-Brain Functional Networks of Heroin-Dependent Individuals: A Resting-State fMRI Study

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e82715 ◽  
Author(s):  
Guihua Jiang ◽  
Xue Wen ◽  
Yingwei Qiu ◽  
Ruibin Zhang ◽  
Junjing Wang ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Functional Networks ◽  
Whole Brain ◽  
Brain Functional Networks ◽  
Fmri Study ◽  
Topological Organization

scholarly journals Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Temporal Structure ◽  
Structural Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Network ◽  
Dynamical Analysis ◽  
Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

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 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 Connectomic markers of symptom severity in sport-related concussion: Whole-brain analysis of resting-state fMRI

2018 ◽  
Vol 18 ◽  
pp. 518-526 ◽  
Author(s):  
Nathan W. Churchill ◽  
Michael G. Hutchison ◽  
Simon J. Graham ◽  
Tom A. Schweizer
Keyword(s):  
Resting State ◽  
Symptom Severity ◽  
Resting State Fmri ◽  
Whole Brain
Sign in / Sign up

Export Citation Format

Share Document