scholarly journals Test-retest reliability of functional connectivity networks during naturalistic fMRI paradigms

2016 ◽  
Author(s):  
Jiahui Wang ◽  
Yudan Ren ◽  
Xintao Hu ◽  
Vinh Thai Nguyen ◽  
Lei Guo ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Visual Processing ◽  
Ecological Validity ◽  
Brain Networks ◽  
Viewing Condition ◽  
Retest Reliability ◽  
Fmri Session ◽  
Test Retest Reliability ◽  
Resting State Connectivity

AbstractFunctional connectivity analysis has become a powerful tool for probing the human brain function and its breakdown in neuropsychiatry disorders. So far, most studies adopted resting state paradigm to examine functional connectivity networks in the brain, thanks to its low demand and high tolerance that are essential for clinical studies. However, the test-retest reliability of resting state connectivity measures is moderate, potentially due to its low behavioral constraint. On the other hand, naturalistic neuroimaging paradigms, an emerging approach for cognitive neuroscience with high ecological validity, could potentially improve the reliability of functional connectivity measures. To test this hypothesis, we characterized the test-retest reliability of functional connectivity measures during a natural viewing condition, and benchmarked it against resting state connectivity measures acquired within the same functional magnetic resonance imaging (fMRI) session. We found that the reliability of connectivity and graph theoretical measures of brain networks is significantly improved during natural viewing conditions over resting state conditions, with an average increase of almost 50% across various connectivity measures. Not only sensory networks for audio-visual processing become more reliable, higher order brain networks, such as default mode and attention networks, also appear to show higher reliability during natural viewing. Our results support the use of natural viewing paradigms in estimating functional connectivity of brain networks, and have important implications for clinical application of fMRI.

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 Boost in Test-Retest Reliability in Resting State fMRI with Predictive Modeling

2019 ◽  
Author(s):  
Aman Taxali ◽  
Mike Angstadt ◽  
Saige Rutherford ◽  
Chandra Sripada
Keyword(s):  
Predictive Modeling ◽  
Predictive Models ◽  
Resting State ◽  
Imaging Features ◽  
Retest Reliability ◽  
Modeling Methods ◽  
Aggregate Information ◽  
Human Connectome Project ◽  
Test Retest Reliability ◽  
Resting State Connectivity

AbstractRecent studies found low test-retest reliability in fMRI, raising serious concerns among researchers, but these studies mostly focused on reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test-retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply ten predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared to mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all ten modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume-versus surface-based processing). For the most reliable methods, reliability of predicted outcomes was mostly, though not exclusively, in the “good” range (above 0.60).Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test-retest reliability by making greater use of predictive models.

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

Test–retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures

NeuroImage ◽  
2012 ◽  
Vol 59 (2) ◽  
pp. 1404-1412 ◽  
Author(s):  
Urs Braun ◽  
Michael M. Plichta ◽  
Christine Esslinger ◽  
Carina Sauer ◽  
Leila Haddad ◽  
...  
Keyword(s):  
Resting State ◽  
Retest Reliability ◽  
Network Characteristics ◽  
Test Retest Reliability ◽  
Resting State Connectivity

scholarly journals Test-Retest Reliability of Graph Metrics in Functional Brain Networks: A Resting-State fNIRS Study

PLoS ONE ◽  
2013 ◽  
Vol 8 (9) ◽  
pp. e72425 ◽  
Author(s):  
Haijing Niu ◽  
Zhen Li ◽  
Xuhong Liao ◽  
Jinhui Wang ◽  
Tengda Zhao ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Networks ◽  
Retest Reliability ◽  
Functional Brain ◽  
Graph Metrics ◽  
Functional Brain Networks ◽  
Test Retest Reliability

Test-retest reliability of graph metrics of resting state MRI functional brain networks: A review

2015 ◽  
Vol 253 ◽  
pp. 183-192 ◽  
Author(s):  
Martina Andellini ◽  
Vittorio Cannatà ◽  
Simone Gazzellini ◽  
Bruno Bernardi ◽  
Antonio Napolitano
Keyword(s):  
Resting State ◽  
Brain Networks ◽  
Retest Reliability ◽  
Functional Brain ◽  
Graph Metrics ◽  
Functional Brain Networks ◽  
Test Retest Reliability

scholarly journals Modulation of intrinsic resting-state fMRI networks in women with chronic migraine

Neurology ◽  
2017 ◽  
Vol 89 (2) ◽  
pp. 163-169 ◽  
Author(s):  
X. Michelle Androulakis ◽  
Kaitlin Krebs ◽  
B. Lee Peterlin ◽  
Tianming Zhang ◽  
Nasim Maleki ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Chronic Migraine ◽  
Resting State ◽  
Medication Overuse Headache ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Severe Headache ◽  
Resting State Functional Connectivity ◽  
Cutaneous Allodynia ◽  
Resting State Connectivity

Objective:To evaluate the intrinsic resting functional connectivity of the default mode network (DMN), salience network (SN), and central executive network (CEN) network in women with chronic migraine (CM), and whether clinical features are associated with such abnormalities.Methods:We analyzed resting-state connectivity in 29 women with CM as compared to age- and sex-matched controls. Relationships between clinical characteristics and changes in targeted networks connectivity were evaluated using a multivariate linear regression model.Results:All 3 major intrinsic brain networks were less coherent in CM (DMN: p = 0.030, SN: p = 0.007, CEN: p = 0.002) as compared to controls. When stratified based on medication overuse headache (MOH) status, CM without MOH (DMN: p = 0.029, SN: p = 0.023, CEN: p = 0.003) and CM with MOH (DMN: p = 0.016, SN: p = 0.016, CEN: p = 0.015) were also less coherent as compared to controls. There was no difference in CM with MOH as compared to CM without MOH (DMN: p = 0.382, SN: p = 0.408, CEN: p = 0.419). The frequency of moderate and severe headache days was associated with decreased connectivity in SN (p = 0.003) and CEN (p = 0.015), while cutaneous allodynia was associated with increased connectivity in SN (p = 0.011).Conclusions:Our results demonstrated decreased overall resting-state functional connectivity of the 3 major intrinsic brain networks in women with CM, and these patterns were associated with frequency of moderate to severe headache and cutaneous allodynia.

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 Title: Comparison of Test-Retest Reliability of Pseudo-continuous Arterial Spin Labelling (pCASL) and BOLD fMRI in a Multi-Site Study

2021 ◽  
Author(s):  
James W. Ibinson ◽  
Andrea G. Gillman ◽  
Vince Schmidthorst ◽  
Conrad Li ◽  
Vitaly Napadow ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Repeated Measures ◽  
Finger Tapping ◽  
Retest Reliability ◽  
Repeated Measures Anova ◽  
State Data ◽  
Bold Fmri ◽  
Significant Variability ◽  
Test Retest Reliability

Abstract Background: The establishment of test-retest reliability and reproducibility (TRR) is an important part of validating any research tool, including functional magnetic resonance imaging (fMRI). The primary objective of this study is to investigate the reliability of pseudo-Continuous Arterial Spin Labeling (pCASL) and Blood Oxygen Level Dependent (BOLD) fMRI data acquired across two different scanners in a sample of healthy adults. While single site/single scanner studies have shown acceptable repeatability, TRR of both in a practical multisite study occurring in two facilities spread out across the country with weeks to months between scans is critically needed.Methods: Ten subjects were imaged with similar 3T MRI scanners at the University of Pittsburgh and Massachusetts General Hospital. Finger-tapping and Resting-state data were acquired for both techniques. Analysis of the resting state data for functional connectivity was performed with the Functional Connectivity Toolbox, while analysis of the finger tapping data was accomplished with FSL. pCASL Blood flow data was generated using AST Toolbox. Activated areas and networks were identified via pre-defined atlases and dual-regression techniques. Analysis for TRR was conducted by comparing pCASL and BOLD images in terms of Intraclass correlation coefficients, Dice Similarity Coefficients, and repeated measures ANOVA.Results: Both BOLD and pCASL scans showed strong activation and correlation between the two locations for the finger tapping tasks. Functional connectivity analyses identified elements of the default mode network in all resting scans at both locations. Multivariate repeated measures ANOVA showed significant variability between subjects, but no significant variability for location. Global CBF was very similar between the two scanning locations, and repeated measures ANOVA showed no significant differences between the two scanning locations.Conclusions: The results of this study show that when similar scanner hardware and software is coupled with identical data analysis protocols, consistent and reproducible functional brain images can be acquired across sites. The variability seen in the activation maps is greater for pCASL versus BOLD images, as expected, however groups maps are remarkably similar despite the low number of subjects. This demonstrates that multi-site fMRI studies of task-based and resting state brain activity is feasible.

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