Literature DB >> 23032492

Quantifying temporal correlations: a test-retest evaluation of functional connectivity in resting-state fMRI.

Mark Fiecas1, Hernando Ombao, Dan van Lunen, Richard Baumgartner, Alexandre Coimbra, Dai Feng.   

Abstract

There have been many interpretations of functional connectivity and proposed measures of temporal correlations between BOLD signals across different brain areas. These interpretations yield from many studies on functional connectivity using resting-state fMRI data that have emerged in recent years. However, not all of these studies used the same metrics for quantifying the temporal correlations between brain regions. In this paper, we use a public-domain test-retest resting-state fMRI data set to perform a systematic investigation of the stability of the metrics that are often used in resting-state functional connectivity (FC) studies. The fMRI data set was collected across three different sessions. The second session took place approximately eleven months after the first session, and the third session was an hour after the second session. The FC metrics composed of cross-correlation, partial cross-correlation, cross-coherence, and parameters based on an autoregressive model. We discussed the strengths and weaknesses of each metric. We performed ROI-level and full-brain seed-based voxelwise test-retest analyses using each FC metric to assess its stability. For both ROI-level and voxel-level analyses, we found that cross-correlation yielded more stable measurements than the other metrics. We discussed the consequences of this result on the utility of the FC metrics. We observed that for negatively correlated ROIs, their partial cross-correlation is shrunk towards zero, thus affecting the stability of their FC. For the present data set, we found greater stability in FC between the second and third sessions (one hour between sessions) compared to the first and second sessions (approximately 11months between sessions). Finally, we report that some of the metrics showed a positive association between strength and stability. In summary, the results presented in this paper suggest important implications when choosing metrics for quantifying and assessing various types of functional connectivity for resting-state fMRI studies.
Copyright © 2012 Elsevier Inc. All rights reserved.

Mesh:

Year:  2012        PMID: 23032492     DOI: 10.1016/j.neuroimage.2012.09.052

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  23 in total

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Authors:  Xiaomu Song; Lawrence P Panych; Nan-Kuei Chen
Journal:  Brain Connect       Date:  2015-11-18

2.  Dynamic Multiscale Modes of Resting State Brain Activity Detected by Entropy Field Decomposition.

Authors:  Lawrence R Frank; Vitaly L Galinsky
Journal:  Neural Comput       Date:  2016-07-08       Impact factor: 2.026

3.  Cross-paradigm connectivity: reliability, stability, and utility.

Authors:  Hengyi Cao; Oliver Y Chen; Sarah C McEwen; Jennifer K Forsyth; Dylan G Gee; Carrie E Bearden; Jean Addington; Bradley Goodyear; Kristin S Cadenhead; Heline Mirzakhanian; Barbara A Cornblatt; Ricardo E Carrión; Daniel H Mathalon; Thomas H McGlashan; Diana O Perkins; Aysenil Belger; Heidi Thermenos; Ming T Tsuang; Theo G M van Erp; Elaine F Walker; Stephan Hamann; Alan Anticevic; Scott W Woods; Tyrone D Cannon
Journal:  Brain Imaging Behav       Date:  2021-04       Impact factor: 3.978

4.  Reliable local dynamics in the brain across sessions are revealed by whole-brain modeling of resting state activity.

Authors:  Patricio Donnelly-Kehoe; Victor M Saenger; Nina Lisofsky; Simone Kühn; Morten L Kringelbach; Jens Schwarzbach; Ulman Lindenberger; Gustavo Deco
Journal:  Hum Brain Mapp       Date:  2019-03-18       Impact factor: 5.038

5.  Sensorimotor network alterations in children and youth with prenatal alcohol exposure.

Authors:  Xiangyu Long; Graham Little; Christian Beaulieu; Catherine Lebel
Journal:  Hum Brain Mapp       Date:  2018-02-12       Impact factor: 5.038

6.  Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets.

Authors:  Pradyumna Lanka; D Rangaprakash; Michael N Dretsch; Jeffrey S Katz; Thomas S Denney; Gopikrishna Deshpande
Journal:  Brain Imaging Behav       Date:  2020-12       Impact factor: 3.978

7.  Strength and stability of EEG functional connectivity predict treatment response in infants with epileptic spasms.

Authors:  Daniel W Shrey; Olivia Kim McManus; Rajsekar Rajaraman; Hernando Ombao; Shaun A Hussain; Beth A Lopour
Journal:  Clin Neurophysiol       Date:  2018-08-04       Impact factor: 3.708

8.  Intraclass correlation: Improved modeling approaches and applications for neuroimaging.

Authors:  Gang Chen; Paul A Taylor; Simone P Haller; Katharina Kircanski; Joel Stoddard; Daniel S Pine; Ellen Leibenluft; Melissa A Brotman; Robert W Cox
Journal:  Hum Brain Mapp       Date:  2017-12-07       Impact factor: 5.038

9.  Adding dynamics to the Human Connectome Project with MEG.

Authors:  L J Larson-Prior; R Oostenveld; S Della Penna; G Michalareas; F Prior; A Babajani-Feremi; J-M Schoffelen; L Marzetti; F de Pasquale; F Di Pompeo; J Stout; M Woolrich; Q Luo; R Bucholz; P Fries; V Pizzella; G L Romani; M Corbetta; A Z Snyder
Journal:  Neuroimage       Date:  2013-05-20       Impact factor: 6.556

Review 10.  The role of fMRI in drug development.

Authors:  Owen Carmichael; Adam J Schwarz; Christopher H Chatham; David Scott; Jessica A Turner; Jaymin Upadhyay; Alexandre Coimbra; James A Goodman; Richard Baumgartner; Brett A English; John W Apolzan; Preetham Shankapal; Keely R Hawkins
Journal:  Drug Discov Today       Date:  2017-11-15       Impact factor: 7.851

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