scholarly journals The Influence of Physiological Noise Correction on Test–Retest Reliability of Resting-State Functional Connectivity

2014 ◽  
Vol 4 (7) ◽  
pp. 511-522 ◽  
Author(s):  
Rasmus M. Birn ◽  
Maria Daniela Cornejo ◽  
Erin K. Molloy ◽  
Rémi Patriat ◽  
Timothy B. Meier ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Functional Connectivity ◽  
Physiological Noise ◽  
Retest Reliability ◽  
Noise Correction ◽  
Test Retest Reliability

scholarly journals Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data

2020 ◽  
Author(s):  
Arun S. Mahadevan ◽  
Ursula A. Tooley ◽  
Maxwell A. Bertolero ◽  
Allyson P. Mackey ◽  
Danielle S. Bassett
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Partial Correlation ◽  
Motion Artifact ◽  
Resting State Fmri ◽  
Alternative Methods ◽  
Fmri Data ◽  
Retest Reliability ◽  
Human Connectome Project ◽  
Test Retest Reliability

AbstractFunctional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of six different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

scholarly journals Age-Related Differences in Test-Retest Reliability in Resting-State Brain Functional Connectivity

PLoS ONE ◽  
2012 ◽  
Vol 7 (12) ◽  
pp. e49847 ◽  
Author(s):  
Jie Song ◽  
Alok S. Desphande ◽  
Timothy B. Meier ◽  
Dana L. Tudorascu ◽  
Svyatoslav Vergun ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Retest Reliability ◽  
Age Related ◽  
Test Retest Reliability ◽  
Brain Functional Connectivity

scholarly journals Heritability analysis with repeat measurements and its application to resting-state functional connectivity

2017 ◽  
Vol 114 (21) ◽  
pp. 5521-5526 ◽  
Author(s):  
Tian Ge ◽  
Avram J. Holmes ◽  
Randy L. Buckner ◽  
Jordan W. Smoller ◽  
Mert R. Sabuncu
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Resting State Fmri ◽  
Transient Effects ◽  
Resting State Functional Connectivity ◽  
Linear Mixed Effects ◽  
Heritability Analysis ◽  
Test Retest Reliability ◽  
Different Levels

Heritability, defined as the proportion of phenotypic variation attributable to genetic variation, provides important information about the genetic basis of a trait. Existing heritability analysis methods do not discriminate between stable effects (e.g., due to the subject’s unique environment) and transient effects, such as measurement error. This can lead to misleading assessments, particularly when comparing the heritability of traits that exhibit different levels of reliability. Here, we present a linear mixed effects model to conduct heritability analyses that explicitly accounts for intrasubject fluctuations (e.g., due to measurement noise or biological transients) using repeat measurements. We apply the proposed strategy to the analysis of resting-state fMRI measurements—a prototypic data modality that exhibits variable levels of test–retest reliability across space. Our results reveal that the stable components of functional connectivity within and across well-established large-scale brain networks can be considerably heritable. Furthermore, we demonstrate that dissociating intra- and intersubject variation can reveal genetic influence on a phenotype that is not fully captured by conventional heritability analyses.

scholarly journals Default(y) Mode Network: Important regions of DMN do not survive alterations in flip angles

2020 ◽  
Author(s):  
Ishan Singhal ◽  
Abhishek K. Soni ◽  
Narayanan Srinivasan
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Posterior Cingulate Cortex ◽  
Intrinsic Activity ◽  
Bold Signal ◽  
Resting State Functional Connectivity ◽  
Physiological Noise ◽  
Fmri Study ◽  
The Brain

AbstractThe default mode network (DMN) is thought to capture intrinsic activity of the brain and has been instrumental in understanding the dynamics of the brain. However, the DMN has not been without critics; both conceptual and empirical. The empirical criticisms caution against physiological noise as a source for the reported connectivity in the DMN. Smaller flip angles (FAs) have been modelled and shown to reduce physiological noise in BOLD signal recordings. A previous functional MRI (fMRI) study with flickering checkerboard stimuli, manipulated FAs to show that activity in the posterior-cingulate cortex (PCC) and precuneus is prone to physiological noise. This raises questions about studies that show activations in these areas (PCC and precuneus) with a fixed FA and the role of these areas in brain networks like DMN. Given the prominent role of PCC and precuneus in DMN, we studied the effect of FAs on the resting-state functional connectivity involving these areas in DMN. We used four FAs and recorded resting-state activity in a 3-T scanner. The results show PCC and precuneus BOLD functional connectivity is inconsistent. We lend support to previous empirical criticisms of DMN, linking its activity to physiological noise. Our results add to concerns about PCC and precuneus related BOLD activity and their putative role in DMN. Alongside previous studies we advocate using smaller flip angles as an empirical tool to investigate physiological noise in fMRI studies.

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