3 Resting-state fMRI no task or stimuli +no task or stimulitypical instructions: keep eyes closed, or keep them open/fixation; don’t fall asleep; let your mind freely wander....
4 Resting-state fMRI resting-state signal fluctuations = ? spontaneous neural activity (i.e., cannot be attributed to a task or overt behavior)noise (hardware, motion, physiological...)
5 Functional connectivity analysis We can analyze relationships between the time series of different brain regionsE.g., seed-based correlation analysis:0.8rseed0.30.8rcorrelate seed’s time series with every other voxel’s time seriesthresholdseed
6 Functional connectivity We can analyze relationships between the time series of different brain regionsBiswal et al. 1995time series during resting-state scanSignals from different regions have correlated resting-state activityRegions that are correlated tend to be “functionally” related
7 Resting-state “networks” have a close correspondence with task-activation networks Smith et al. 2010
8 Resting-state networks Rocca et a. 2012resting-state functional connectivity: phenomenon of correlated resting-state fluctuations between remote brain areasresting-state networks (RSN): set of regions with mutually high functional connectivity in resting state
9 Implications task-free mapping of functional networks? query multiple networks from the same datasetcan be used when task performance is not possible (fetus, coma, ...)potential biomarker of healthy & diseased brainresting-state functional connectivity may reflect functional organization and dynamicsMeunier et al. 2011
10 ChallengesResting-state networks “look real”... but could also arise due to:noise (hardware, physiology)vascular pulsation“hidden tasks”: conscious thoughts, actions, sensation, etc. causing activation within functional systemsThe terms ‘FC’ and ‘RSN’ are purely descriptiveUnderstanding of origins &mechanisms is still limitedEvidence that these are not trivially due to the above
12 RSNs are (mostly) conserved across sessions, individuals, states, species, ... MonkeysInfantsSleepRatsHorovitz et al. 2008; Vincent et al. 2007; Lu et al. 2007; Doria et al. 2010suggests not arising solely from conscious processes
13 Default Mode Networkhigher activity during passive baseline conditions comapred to (most) tasksfunctional connectivity in resting stateRaichle at el., 2001review: Buckner et al. , Ann. N.Y. Acad. Sci. 2008Greicius et al. 2003
14 Coherence in spontaneous electrophysiological signals -- This correspondence between coherent resting-state activity and functional organization has also been observed with more direct recordings of neural activity, suggesting it’s not just something unique to fmri or driven by vasculature.-- What’s shown here is membrane potential over a patch of visual cortex measured with voltage sensitive dye; and they showed that if you focus on particular time frames or snapshots of spontaneous activity, like here, you see a spatial pattern that looks very much like maps of orientation columns derived from presenting stimuli, as well as in the single time frames of evoked activity.Kenet et al, 2003spontaneous fluctuations in membrane voltage resemble orientation columns & evoked activity
15 Simultaneous LFP-fMRI of resting-state fluctuations correlations arespatially widespread!Scholvinck et al., 2010Shmuel & Leopold, 2008gamma power fluctuations in local field potential (LFP) found to correlate with fMRI signal
16 Human ECoG of resting-state activity How well do “networks” of electrical signals match “networks” of BOLD fMRI?Keller et al. 2013also with slow cortical potential (He et al, 2010)macaque ECoG reveals broadband phenomenon (Liu et al. 2014)
17 Functional connectivity at finer spatial scales Beckmann et al. 2005Buckner et al. 2011Kim et al. 2013
18 Structrual connectivity affects functional connectivity Johnston et al., 2008Quigley et al., 2003task activationresting-statefunctional connectivityvia indirect connections?
19 Clinical applications Altered functional connectivity found in a range of neurological & psychiatric disordersAffects “expected” regions and may relate to severity of diseasePotential for classifying patients vs. healthy controlsNo task necessary; can be used for patients, coma,Healthy controlAlzheimer’sSchizophreniaUnderpinnings of altered functional connectivity need further investigationGreicius et al. 2004,Whitfield-Gabrieli et al. 2009,Lewis et al. 2009
21 Seed-based correlation analysis “network”0.8rseed0.30.8rcorrelate seed’s time series with every other voxel’s time seriesthresholdRequires a priori seed (hypothesis)How define the seed (atlas? functional localizer?) – sensitivity of results to exact size/placementStraightforward intepretation
22 Independent component analysis Cocktail party problemN microphones around a room record different mixtures of N speakers’ voicesHow to separate the voices of each speaker??time1Observed datatime2time3ICA can be applied to ‘unmix’ fMRI data into networksMultivariate
23 Original Sound sources “Cocktail party” mixesEstimated sourcesadapted fromby Jen Evans
24 Independent component analysis Cocktail party problemN microphones around a room record different mixtures of N speakers’ voicesHow to separate the voices of each speaker??time1Observed datatime2time3ICA can be applied to ‘unmix’ fMRI data into networksMultivariate
25 Spatial ICADecompose fMRI data into fixed spatial components (“networks”) with time-dependent weights (network time courses)time t:aN(t)a1(t)aN-1(t)a2(t)+=…raw_data(t)McKeown et al, 1998Thomas et al, 2002
26 Independent component analysis Damoiseaux et al. 2006
27 ICA + very helpful for exploring structure of data! + multivariate; doesn’t require choice of seed+ useful for de-noising (but won’t completely remove it)need to specify parameters (e.g. # components)interpretation difficultReview: Cole et al. 2010: “Advances and pitfalls in the analysis and interpretation of resting-state fMRI data”
28 Network analysise.g. SEM, DCM, Granger causality, partial correlation…complex network analysisReview: Rubinov & Sporns, 2011Bullmore & Sporns, 2012Review: Smith et al. 2013, TICS: Functional connectomics from resting-state fMRIWig et al. 2011
32 (whole-brain average) Breathing variations affect BOLD signalRespirationBOLD signal(whole-brain average)Respiratory variations (RVT) changes in [CO2], HR, blood pressure hemodynamic response uncoupled from local neural activity
33 Changes in rate / depth of breathing over time correlate with BOLD signal Birn et al. 2006Common influence over many regions creates ‘false positive’ correlations
34 predicted fMRI signal derived from respiration measuremen Reducing physiological noiseModel-based approaches: estimate noise based on physiological measurements (e.g. RETROICOR, RETROKCOR, RV/HRCOR..).whole-brain average fMRI signal in task-free scanpredicted fMRI signal derived from respiration measuremenChang et al., 2009Data-driven approaches: estimate noise from the data itselfe.g. CompCor, FIX, PESTICA, ...
35 Global signal regression anti-correlated resting state networks...?Murphy et al, 2009Fransson 2005,Fox et al, 2005are anticorrelations state-dependent?
36 State-related variability Resting(undirected)Horovitz et al., 2009RecallingmemoriesEyes open/closedeyes closedShirer et al, 2011eyes open/fixationBianciardi et al., 2009
37 State-related variability Caffeine can influence resting-state correlationsWong et al. 2010Fluctuations in alertness/drowsiness modulate FCChang et al. 2013
38 “Dynamic” resting-state analysis Can we extract more information by moving beyond static / average corrlelation?+Allen et al. 2012
40 Variability: discussion Resting-state signals and correlations vary over timeSources: cognitive/vigilance state, noise, spontaneous….Consider when interpreting group differencesWhat time scales to study / how long to scan?Why study variability?model within-scan varianceneural basis of natural state changes (drowsiness, emotion….)learn about dynamics of brain activitySimultaneous recordings (EEG, physiology) during resting state can help
42 SummaryResting-state fMRI is proving valuable for clinical applications and basic neuroscienceRSNs relate to anatomic connectivity and electrophysiology, but precise relationship still not clearUnderstand analysis methods/tradeoffsno single “correct” analysis of resting-state dataavoid bias, fishingNoise can skew connectivity estimatesclean up the signal as best as possible! See future lecture…There can be substantial within-scan variabilityneed to understand these effects, determine what information is valuable
43 Thanks!AMRI group: Jeff Duyn Xiao Liu Dante Picchioni Jacco de Zwart Peter Van Gelderen Natalia Gudino Roger Jiang Xiaozhen Li Hendrik Mandelkow Erika RavenJennifer EvansDan HandwerkerPeter BandettiniGary GloverMika RubinovZhongming Liu
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