Restricted accessResearch articleFirst published online 2017-03
Test–Retest Reproducibility of the Intrinsic Default Mode Network: Influence of Functional Magnetic Resonance Imaging Slice-Order Acquisition and Head-Motion Correction Methods
Head motion is a known challenge in resting-state functional magnetic resonance imaging studies for biasing functional connectivity (FC) among distinct anatomical regions. These persist even with small motion, limiting comparisons of groups with different head-motion characteristics. This motivates an interest in the optimization of acquisition and correction strategies to minimize motion sensitivity. In this test–retest (TRT) study of healthy young volunteers (N = 23), we investigate the effects of slice-order acquisitions (sequential or interleaved) and head-motion correction methods (volume- or slice-based) on the TRT reproducibility of intrinsic connectivity of the default mode network (DMN). We evaluated the TRT reproducibility of the entire DMN and each main node using the absolute percentage error, intraclass correlation coefficient (ICC), and the Jaccard coefficient. Regardless of slice-order acquisition, the slice-based motion correction method systematically estimated larger motion and returned significantly higher temporal signal-to-noise ratio. Although consistently extracted across all acquisition and motion correction approaches, DMN connectivity was sensitive to these choices. However, the TRT reproducibility of the whole DMN was stable and showed no sensitivity to the methods tested (absolute reproducibility ∼7%, ICC = 0.47, and Jaccard = 40%). Percentage errors and ICCs were consistent across single nodes, but the Jaccard coefficients were not. The posterior cingulate was the most reproducible node (Jaccard = 52%), whereas the anterior cingulate was the least reproducible (Jaccard = 30%). Our study suggests that the slice-order and motion correction methods evaluated offer comparable sensitivity to detect DMN connectivity changes in a longitudinal study of individuals with low head-motion characteristics, but that controlling for the consistency in acquisition and correction protocols is important in cross-sectional studies.
Get full access to this article
View all access options for this article.
References
1.
Abou ElseoudA, LittowH, RemesJ, StarckT, NikkinenJ, NissiläJ, et al.2011. Group-ICA model order highlights patterns of functional brain connectivity. Front Syst Neurosci, 5:37.
2.
Andrews-HannaJR. 2012. The brain's default network and its adaptive role in internal mentation. Neuroscientist, 18:251–270.
3.
BaiF, WatsonDR, YuH, ShiY, YuanY, ZhangZ. 2009. Abnormal resting-state functional connectivity of posterior cingulate cortex in amnestic type mild cognitive impairment. Brain Res, 1302:167–174.
4.
BalthazarML, de CamposBM, FrancoAR, DamascenoBP, CendesF. 2014. Whole cortical and default mode network mean functional connectivity as potential biomarkers for mild Alzheimer's disease. Psychiatry Res, 221:37–42.
5.
BeallEB, LoweMJ. 2014. SimPACE: generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: a new, highly effective slicewise motion correction. Neuroimage, 101:21–34.
6.
BeckmannCF, DeLucaM, DevlinJT, SmithSM. 2005. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci, 360:1001–1013.
7.
BeckmannCF, MackayCE, FilippiniN, SmithSM. 2009. Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. Neuroimage, 47 (Suppl 1):S148.
8.
BeckmannCF, SmithSM. 2004. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging, 23:137–152.
9.
BennettCM, MillerMB. 2010. How reliable are the results from functional magnetic resonance imaging?. Ann N Y Acad Sci, 1191:133–155.
10.
BinnewijzendMA, SchoonheimMM, Sanz-ArigitaE, WinkAM, van der FlierWM, TolboomN, et al.2012. Resting-state fMRI changes in Alzheimer's disease and mild cognitive impairment. Neurobiol Aging, 33:2018–2028.
11.
BiswalB, YetkinFZ, HaughtonVM, HydeJS. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med, 34:537–541.
12.
BiswalBB, MennesM, ZuoXN, GohelS, KellyC, SmithSM, et al.2010. Toward discovery science of human brain function. Proc Natl Acad Sci U S A, 107:4734–4739.
13.
BraunU, PlichtaMM, EsslingerC, SauerC, HaddadL, GrimmO, et al.2012. Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. Neuroimage, 59:1404–1412.
14.
BrightMG, MurphyK. 2013. Removing motion and physiological artifacts from intrinsic BOLD fluctuations using short echo data. Neuroimage, 64:526–537.
15.
BucknerRL. 2012. The serendipitous discovery of the brain's default network. Neuroimage, 62:1137–1145.
16.
BucknerRL, Andrews-HannaJR, SchacterDL. 2008. The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci, 1124:1–38.
17.
BucknerRL, CarrollDC. 2007. Self-projection and the brain. Trends Cogn Sci, 11:49–57.
18.
CalhounVD, LiuJ, AdalıT. 2009. A review of group-ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage, 45:S163–S172.
19.
ChenS, RossTJ, ZhanW, MyersCS, ChuangKS, HeishmanSJ, et al.2008. Group independent component analysis reveals consistent resting-state networks across multiple sessions. Brain Res, 1239:141–151.
20.
ChengH, PuceA. 2014. Reducing respiratory effect in motion correction for EPI images with sequential slice acquisition order. J Neurosci Methods, 227:83–89.
21.
CoxRW. 1996. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res, 29:162–173.
22.
DamoiseauxJS. 2012. Resting-state fMRI as a biomarker for Alzheimer's disease?. Alzheimers Res Ther, 4:8.
23.
DamoiseauxJS, RomboutsSARB, BarkhofF, ScheltensP, StamCJ, SmithSM, BeckmannCF. 2006. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A, 103:13848–13853.
24.
DunnOJ. 1964. Multiple comparisons using rank sums. Technometrics, 6:241–252.
25.
EspositoF, ScarabinoT, HyvarinenA, HimbergJ, FormisanoE, ComaniS, et al.2005. Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage, 25:193–205.
26.
FoxMD, CorbettaM, SnyderAZ, VincentJL, RaichleME. 2006. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci U S A, 103:10046–10051.
27.
FrancoAR, PritchardA, CalhounVD, MayerAR. 2010. Interrater and intermethod reliability of default mode network selection. Human Brain Mapp, 30:2293–2303.
28.
FranssonP, MarrelecG. 2008. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: evidence from a partial correlation network analysis. NeuroImage, 42:1178–1184.
29.
GodenschwegerF, KägebeinU, StuchtD, YarachU, SciarraA, YakupovR, et al.2016. Motion correction in MRI of the brain. Phys Med Biol, 61:R32–R56.
30.
GoebelR, EspositoF, FormisanoE. 2006. Analysis of FIAC data with BrainVoyager QX: from single-subject to cortically aligned group GLM analysis and self-organizing group ICA. Human Brain Mapp, 27:392–401.
31.
GreiciusMD, KrasnowB, ReissAL, MenonV. 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A, 100:253–258.
32.
GreiciusMD, MenonV. 2004. Default-mode activity during a passive sensory task: uncoupled from deactivation but impacting activation. J Cogn Neurosci, 16:1484–1492.
33.
GreiciusMD, SrivastavaG, ReissAL, MenonV. 2004. Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A, 101:4637–4642.
34.
GreveDN, FischlB. 2009. Accurate and robust brain image alignment using boundary-based registration. Neuroimage, 48:63–72.
35.
GriffantiL, Salimi-KhorshidiG, BeckmannCF, AuerbachEJ, DouaudG, SextonCE, et al.2014. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. Neuroimage, 95:232–247.
HallquistMN, HwangK, LunaB. 2013. The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. Neuroimage, 82:208–225.
38.
HawellekDJ, HippJF, LewisCM, CorbettaM, EngelAK. 2011. Increased functional connectivity indicates the severity of cognitive impairment in multiple sclerosis. Proc Natl Acad Sci U S A, 108:19066–19071.
39.
HeddenT, Van DijkKR, BeckerJA, MehtaA, SperlingRA, JohnsonKA, BucknerRL. 2009. Disruption of functional connectivity in clinically normal older adults harboring amyloid burden. J Neurosci, 29:12686–12694.
40.
JenkinsonM, BannisterP, BradyJM, SmithSM. 2002. Improved optimisation for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17:825–841.
41.
JenkinsonM, SmithS. 2001. A global optimisation method for robust affine registration of brain images. Med Image Anal, 5:143–156.
42.
JohnstoneT, Ores WalshKS, GreischarLL, AlexanderAL, FoxAS, DavidsonRJ, OakesTR. 2006. Motion correction and the use of motion covariates in multiple-subject fMRI analysis. Human Brain Mapp, 27:779–788.
43.
JonesTB, BandettiniPA, BirnRM. 2008. Integration of motion correction and physiological noise regression in fMRI. Neuroimage, 42:582–590.
44.
JovicichJ, MinatiL, MarizzoniM, MarchitelliR, Sala-LlonchR, Bartrés-FazD, et al.2016. Longitudinal reproducibility of default-mode network connectivity in healthy elderly participants: a multicentric resting-state fMRI study. Neuroimage, 124:442–454.
45.
KimB, YeoDTB, BhagaliaR. 2008. Comprehensive mathematical simulation of functional magnetic resonance imaging time series including motion-related image distortion and spin saturation effect. Magn Reson Imaging, 26:147–159.
46.
KunduP, InatiSJ, EvansJW, LuhWM, BandettiniPA. 2012. Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. Neuroimage, 60:1759–1770.
MarchitelliR, MinatiL, MarizzoniM, BoschB, Bartrés‐FazD, MüllerBW, et al.2016. Test‐retest reliability of the default mode network in a multi‐centric fMRI study of healthy elderly: effects of data‐driven physiological noise correction techniques. Human Brain Mapp, 37:2114–2132.
49.
McGrawKO, WongSP. 1996. Forming inferences about some intraclass correlation coefficients. Psychol Methods, 1:30.
50.
MolloyEK, MeyerandME, BirnRM. 2014. The influence of spatial resolution and smoothing on the detectability of resting-state and task fMRI. NeuroImage, 86:221–230.
51.
MuldersPC, van EijndhovenPF, ScheneAH, BeckmannCF, TendolkarI. 2015. Resting-state functional connectivity in major depressive disorder: a review. Neurosci Biobehav Rev, 56:330–344.
PowerJD, BarnesKA, SnyderAZ, SchlaggarBL, PetersenSE. 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59:2142–2154.
54.
PowerJD, MitraA, LaumannTO, SnyderAZ, SchlaggarBL, PetersenSE. 2014. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage, 84:320–341.
55.
PruimRHR, MennesM, van RooijD, Llera ArenasA, BuitelaarJK, BeckmannCF. 2014. ICA-AROMA: a robust ICA-based strategy for removing motion artifact from fMRI data. Neuroimage, 112:267–277.
56.
RaichleME, MacLeodAM, SnyderAZ, PowersWJ, GusnardDA, ShulmanGL. 2001. A default mode of brain function. Proc Natl Acad Sci U S A, 98:676–682.
57.
ReuterM, TisdallMD, QureshiA, BucknerRL, Van der KouweAJ, FischlB. 2015. Head-motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage, 107:107–115.
58.
RosazzaC, MinatiL, GhielmettiF, MandelliML, BruzzoneMG. 2012. Functional connectivity during resting-state functional MR imaging: study of the correspondence between independent component analysis and region-of-interest-based methods. AJNR Am J Neuroradiol, 33:180–187.
59.
Salimi-KhorshidiG, DouaudG, BeckmannCF, GlasserMF, GriffantiL, SmithSM. 2014. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage, 90:449–468.
60.
SatterthwaiteTD, ElliottMA, GerratyRT, RuparelK, LougheadJ, CalkinsME, et al.2013. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage, 64:240–256.
61.
SatterthwaiteTD, WolfDH, LougheadJ, RuparelK, ElliottMA, HakonarsonH, et al.2012. Impact of in-scanner head-motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. Neuroimage, 60:623–632.
ShulmanGL, FiezJA, CorbettaM, BucknerRL, MiezinFM, et al.1997. Common blood flow changes across visual tasks: II: decreases in cerebral cortex. J Cogn Neurosci, 9:648–663.
68.
SiegelJS, PowerJD, DubisJW, VogelAC, ChurchJA, SchlaggarBL, PetersenSE. 2014. Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points. Human Brain Mapp, 35:1981–1996.
69.
SladkyR, FristonKJ, TröstlJ, CunningtonR, MoserE, WindischbergerC. 2011. Slice-timing effects and their correction in functional MRI. Neuroimage, 58:588–594.
70.
SpornsO. 2011. Networks of the Brain. Cambridge: MIT Press.
71.
TurnerR, HowsemanA, ReesGE, JosephsO, FristonK. 1998. Functional magnetic resonance imaging of the human brain: data acquisition and analysis. Exp Brain Res, 123:5–12.
72.
van den HeuvelMP, SpornsO. 2013. Network hubs in the human brain. Trends Cogn Sci, 17:683–696.
73.
Van DijkKRA, HeddenT, VenkataramanA, EvansKC, LazarSW, BucknerRL. 2010. Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J Neurophysiol, 103:297–321.
74.
Van DijkKRA, SabuncuMR, BucknerRL. 2012. The influence of head-motion on intrinsic functional connectivity MRI. Neuroimage, 59:431–438.
75.
VincentJL, KahnI, SnyderAZ, RaichleME, BucknerRL. 2008. Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. J Neurophysiol, 100:3328–3342.
76.
WashingtonSD, GordonEM, BrarJ, WarburtonS, SawyerAT, WolfeA, et al.2014. Dysmaturation of the default mode network in autism. Human Brain Mapp, 35:1284–1296.
77.
WestbrookC, Kaut RothC, TalbotJ. 2005. MRI in Practice, 3rd ed. Malden, MA: Blackwell Publishing.
78.
ZengLL, WangD, FoxMD, SabuncuM, HuD, GeM, BucknerRL, LiuH. 2014. Neurobiological basis of head-motion in brain imaging. Proc Natl Acad Sci U S A, 111:6058–6062.
79.
ZhouY, DoughertyJH, HubnerKF, BaiB, CannonRL, HutsonRK. 2008. Abnormal connectivity in the posterior cingulate and hippocampus in early Alzheimer's disease and mild cognitive impairment. Alzheimers Dement, 4:265–270.
80.
ZuoXN, AndersonJS, BellecP, BirnRM, BiswalBB, BlautzikJ, et al.2014. An open science resource for establishing reliability and reproducibility in functional connectomics. Sci Data, 1:140049.
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
