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Reliability modelling of resting-state functional connectivity

Jalmar Teeuw, Hilleke E. Hulshoff Pol, Dorret I. Boomsma, Rachel M. Brouwer
2021 NeuroImage  
Resting-state functional magnetic resonance imaging (rs-fMRI) has an inherently low signal-to-noise ratio largely due to thermal and physiological noise that attenuates the functional connectivity (FC)  ...  We applied a measurement model to split-half FC estimates from the resting-state fMRI data of 1003 participants from the Human Connectome Project (HCP) to examine the benefit of reliability modelling of  ...  However, little is known about the effectiveness of reliability modelling of resting-state MRI functional connectivity and its ability to uncover the true associations between functional connectivity and  ... 
doi:10.1016/j.neuroimage.2021.117842 pmid:33581291 fatcat:xi42cdrghnfybkldpifxyluwyq

Effects of resting state condition on reliability, trait specificity, and network connectivity of brain function measured with arterial spin labeled perfusion MRI

Zhengjun Li, Marta Vidorreta, Natalie Katchmar, David C. Alsop, Daniel H. Wolf, John A. Detre
2018 NeuroImage  
Our findings confirm ASL CBF as a reliable, stable, and consistent measure of resting-state regional brain function and support the use of EC or EO over FIX and PVT as the resting-state condition.  ...  For resting-state ASL data, EC provided the highest CBF reliability, reproducibility, trait specificity, and network connectivity reliability, followed by EO, while FIX was lowest on all of these measures  ...  DCA is an inventor of pseudocontinuous ASL and receives a share of postmarket royalties from his institution's licenses to GE Healthcare, Philips Healthcare, Siemens Healthineers, Hitachi Medical, and  ... 
doi:10.1016/j.neuroimage.2018.02.028 pmid:29454933 pmcid:PMC5957091 fatcat:3t2qtqjfurddrgdcotyvz2jvzy

Test-Retest Reliability of Magnetoencephalography Resting-State Functional Connectivity in Schizophrenia

Felicha T Candelaria-Cook, Julia M Stephen
2020 Frontiers in Psychiatry  
The reliability of magnetoencephalography (MEG) resting-state functional connectivity in schizophrenia (SZ) is unknown as previous research has focused on healthy controls (HC).  ...  The functional connectivity metrics of coh, ppc, and plv performed best across 1 hour and 1 week in HC and SZ.  ...  This work was supported in part by grants from the National Institutes of Health (P20GM103472 and P30GM122734) and National Science Foundation (NSF) 1539067.  ... 
doi:10.3389/fpsyt.2020.551952 pmid:33391043 pmcid:PMC7772354 fatcat:ndkc4j6oebhudb5ehbzr7wctpe

Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors

Kwangsun Yoo, Monica D. Rosenberg, Stephanie Noble, Dustin Scheinost, RTodd Constable, Marvin M. Chun
2019 NeuroImage  
Brain functional connectivity features can predict cognition and behavior at the level of the individual.  ...  , stronger prediction of individual differences, and greater sensitivity to brain states within individuals.  ...  Acknowledgements This project was supported by the National Institutes of Health grant MH108591 and by the National Science Foundation grant BCS1558497.  ... 
doi:10.1016/j.neuroimage.2019.04.060 pmid:31039408 pmcid:PMC6591084 fatcat:ctgpkiwh5ja5tjwdkslv43cilm

Test-retest reliability of regression dynamic causal modeling [article]

Stefan Frässle, Klaas Enno Stephan
2021 bioRxiv   pre-print
Generally, for all methods and metrics, task-based connectivity estimates showed greater reliability than those from the resting state.  ...  Comparing the test-retest reliability of rDCM connectivity estimates to measures of functional connectivity, rDCM performed favorably – particularly when focusing on strong connections.  ...  Test-retest reliability of model parameter estimates for regression DCM and functional 24 connectivity.  ... 
doi:10.1101/2021.06.01.446526 fatcat:x7vbckd7yzbazed2awaw5fzjta

General Functional Connectivity: shared features of resting-state and task fMRI drive reliable individual differences in functional brain networks [article]

Maxwell L Elliott, Annchen R Knodt, M. Justin Kim, Tracy R Melzer, Ross Keenan, David Ireland, Sandhya Ramrakha, Richie Poulton, Avshalom Caspi, Terrie E Moffitt, Ahmad R Hariri
2018 bioRxiv   pre-print
Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate that GFC is substantially more reliable than intrinsic  ...  Intrinsic connectivity, commonly measured using resting-state fMRI, has emerged as a fundamental feature of the brain.  ...  In this context, 60 most studies have focused on the acquisition of resting-state functional MRI to map the intrinsic 61 connectivity of neural networks.  ... 
doi:10.1101/330530 fatcat:drygm7p5xzhllopi6g6nq3dgla

Impact of Concatenating fMRI Data on Reliability for Functional Connectomics

Jae Wook Cho, Annachiara Korchmaros, Joshua T Vogelstein, Michael Milham, Ting Xu
2020 NeuroImage  
Our findings provide an overview of multiple dependencies of data concatenation that should be considered to optimize reliability in analysis of functional connectivity data.  ...  Specifically, we compared functional connectivity estimates derived after concatenating from: 1) multiple scans under the same state, 2) multiple scans under different states (i.e. hybrid or general functional  ...  Reliability of edgewise functional connectivity for resting and hybrid data as a function of the numbers of states combined in the concatenation process (2, 4 and 6 different states).  ... 
doi:10.1016/j.neuroimage.2020.117549 pmid:33248255 pmcid:PMC7983579 fatcat:x5n42hk6ofca3kzboc33btfd2e

Reliability of Static and Dynamic Network Metrics in the Resting-State: A MEG-Beamformed Connectivity Analysis

Stavros I. Dimitriadis, Bethany Routley, David E. Linden, Krish D. Singh
2018 Frontiers in Neuroscience  
The resting activity of the brain can be described by so-called intrinsic connectivity networks (ICNs), which consist of spatially and temporally distributed, but functionally connected, nodes.  ...  Although many algorithms for the analysis of brain connectivity have been proposed, the reliability of network metrics derived from both static and dynamic functional connectivity is still unknown.  ...  The dynamic functional connectivity patterns can be modeled as prototypical functional connectivity microstates (FCµstates).  ... 
doi:10.3389/fnins.2018.00506 pmid:30127710 pmcid:PMC6088195 fatcat:mpxnhkrhnfe75h3bd67zfukec4

Reliability of Static and Dynamic Network Metrics in the Resting-State: A MEG-beamformed Connectivity Analysis [article]

Stavros I Dimitriadis, Bethany Routley, David E Linden, Krish D Singh
2018 biorxiv/medrxiv   pre-print
The resting activity of the brain can be described by so-called intrinsic connectivity networks (ICNs), which consist of spatially and temporally distributed, but functionally connected, nodes.  ...  Although many algorithms for the analysis of brain connectivity have been proposed, the reliability of network metrics derived from both static and dynamic functional connectivity is still unknown.  ...  The dynamic functional connectivity patterns can be modeled as prototypical functional connectivity microstates (FCµstates).  ... 
doi:10.1101/358192 fatcat:xcvdd5kmkbcajptxxtnaylsnpe

The effect of scan length on the reliability of resting-state fMRI connectivity estimates

Rasmus M. Birn, Erin K. Molloy, Rémi Patriat, Taurean Parker, Timothy B. Meier, Gregory R. Kirk, Veena A. Nair, M. Elizabeth Meyerand, Vivek Prabhakaran
2013 NeuroImage  
In our present study, we examine the test-retest reliability and similarity of resting-state functional connectivity for scans ranging in length from 3 to 27 min as well as for time series acquired during  ...  To date, most research assessing the reliability with which resting-state functional connectivity characterizes the brain's functional networks has been on scans between 3 and 11 min in length.  ...  Introduction Resting state functional connectivity MRI (rs-fcMRI) measures functional connections in the brain via the temporal correlation of low-frequency (<0.1 Hz) fluctuations in the MRI signal.  ... 
doi:10.1016/j.neuroimage.2013.05.099 pmid:23747458 pmcid:PMC4104183 fatcat:2yybszmqg5h5tdcdoezjxlpcjm

Machine learning prediction of cognition from functional connectivity: Are feature weights reliable? [article]

Ye Tian, Andrew Zalesky
2021 bioRxiv   pre-print
We comprehensively investigate feature weight test-retest reliability for various predictive models of cognitive performance built from resting-state functional connectivity networks in healthy young adults  ...  AbstractCognitive performance can be predicted from an individual's functional brain connectivity with modest accuracy using machine learning approaches.  ...  The goal of this study is to evaluate the test-retest reliability of resting-state functional connectivity feature weights estimated by predictive models of intelligence and cognitive function.  ... 
doi:10.1101/2021.05.27.446059 fatcat:hmjprh4xwzdtxefcupz5gqezxq

Predicting human resting-state functional connectivity from structural connectivity

C. J. Honey, O. Sporns, L. Cammoun, X. Gigandet, J. P. Thiran, R. Meuli, P. Hagmann
2009 Proceedings of the National Academy of Sciences of the United States of America  
; and (iii) that resting-state functional connectivity exhibits variability within and across both scanning sessions and model runs.  ...  These empirical and modeling results demonstrate that although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength  ...  ; and (iii) that resting-state functional connectivity exhibits variability within and across both scanning sessions and model runs.  ... 
doi:10.1073/pnas.0811168106 pmid:19188601 pmcid:PMC2634800 fatcat:rk37m332cjbylfz72iozw2r65i

Impact of Concatenating fMRI Data on Reliability for Functional Connectomics [article]

Jae Wook Cho, Annachiara Korchmaros, Joshu T Vogelstein, Michael Milham, Ting Xu
2020 bioRxiv   pre-print
Our findings provide an overview of multiple dependencies of data concatenation that should be considered to optimize reliability in analysis of functional connectivity data.  ...  Specifically, we compared functional connectivity estimates derived after concatenating from: 1) multiple scans under the same state, 2) multiple scans under different states (i.e. hybrid or general functional  ...  Reliability of edgewise functional connectivity for resting and hybrid data as a 2 function of the numbers of states combined in the concatenation process (2, 4 and 6 3 different states).  ... 
doi:10.1101/2020.05.06.081679 fatcat:t6eh4m5rp5b6rjf5x5xrhtrj5a

Brain Connectivity Analysis: A Short Survey

E. W. Lang, A. M. Tomé, I. R. Keck, J. M. Górriz-Sáez, C. G. Puntonet
2012 Computational Intelligence and Neuroscience  
Resting state conditions have become a dominant experimental paradigm, and a number of resting state networks, among them the prominent default mode network, have been identified.  ...  The last decade has seen an ever increasing number of studies devoted to deduce functional or effective connectivity, mostly from functional neuroimaging experiments.  ...  [25] could demonstrate that resting state functional connectivity indeed reflects the underlying structural connectivity.  ... 
doi:10.1155/2012/412512 pmid:23097663 pmcid:PMC3477528 fatcat:zlwqwx6n7feefdrfx5pnhhbmhe

The LEGO theory of the developing functional connectome [article]

Erica Lindsey Busch, Kristina M Rapuano, Kevin Michael Anderson, Monica D. Rosenberg, Richard Watts, BJ Casey, James V Haxby, Ma Feilong
2022 bioRxiv   pre-print
We studied resting-state fMRI in 1,115 children (including 389 twin pairs) and used functional alignment to afford access to individual differences in the fine-scale connectome10,19,20.  ...  Decades of human magnetic resonance imaging (MRI) research demonstrate that variance in neuroimaging phenotypes, including functional connectivity, relate to genetics1–5 and predict cognitive traits6–9  ...  Heritability of functional connectivity profiles between regions (defined with the Glasser parcellation 40 ) was estimated using a multidimensional heritability model 32 using resting-state fMRI data  ... 
doi:10.1101/2022.05.24.493295 fatcat:q5bslksbpbg2hct5fl7kjsrw74
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