Resting State fMRI

1. Resting State fMRI Catie Chang Advanced MRI Section, LFMI, NINDS, NIH

2. Outline • Background • Properties • Analysis • Noise & variability • Summary

3. Resting-state fMRI • no task or stimuli • typical 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 regions • E.g., seed-based correlation analysis: 0.8 r r 0.3 threshold correlate seed’s time series with every other voxel’s time series 0.8 seed seed 0

6. Functional connectivity • We can analyze relationships between the time series of different brain regions time series during resting-state scan Biswal et al. 1995 • Signals from different regions have correlated resting-state activity • Regions that are correlated tend to be “functionally” related

7. Resting-state “networks” have a close correspondence with task-activation networks Task Rest Task Rest Task Rest Smith et al. 2010

8. Resting-state networks Rocca et a. 2012 • resting-state functional connectivity: phenomenon of correlated resting-state fluctuations between remote brain areas • resting-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 dataset • can be used when task performance is not possible (fetus, coma, ...) • potential biomarker of healthy & diseased brain • resting-state functional connectivity may reflect functional organization and dynamics Meunier et al. 2011

10. Challenges • Resting-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 systems • The terms ‘FC’ and ‘RSN’ are purely descriptive • Understanding of origins &mechanisms is still limited • Evidence that these are not trivially due to the above

11. Outline • Background • Properties • Analysis • Noise & variability • Summary

12. RSNs are (mostly) conserved across sessions, individuals, states, species, ... • suggests not arising solely from conscious processes • Monkeys • Infants • Sleep • Rats Horovitz et al. 2008; Vincent et al. 2007; Lu et al. 2007; Doria et al. 2010

13. Default Mode Network • higher activity during passive baseline conditions comapred to (most) tasks • functional connectivity in resting state • Raichle at el., 2001 • review: Buckner et al. , Ann. N.Y. Acad. Sci. 2008 • Greicius et al. 2003

14. Coherence in spontaneous electrophysiological signals Kenet et al, 2003 spontaneous fluctuations in membrane voltage resemble orientation columns & evoked activity

15. Simultaneous LFP-fMRI of resting-state fluctuations correlations are spatially widespread! Shmuel & Leopold, 2008 gamma power fluctuations in local field potential (LFP) found to correlate with fMRI signal Scholvinck et al., 2010

16. Human ECoG of resting-state activity How well do “networks” of electrical signals match “networks” of BOLD fMRI? Keller et al. 2013 • also 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. 2005 Buckner et al. 2011 Kim et al. 2013

18. Structrual connectivity affects functional connectivity Quigley et al., 2003 resting-state functional connectivity task activation via indirect connections? Johnston et al., 2008

19. Clinical applications • Altered functional connectivity found in a range of neurological & psychiatric disorders • Affects “expected” regions and may relate to severity of disease • Potential for classifying patients vs. healthy controls • No task necessary; can be used for patients, coma, ...... Healthy control Alzheimer’s Schizophrenia • Greicius et al. 2004, • Whitfield-Gabrieli et al. 2009, • Lewis et al. 2009 • Underpinnings of altered functional connectivity need further investigation

20. Outline • Background • Properties • Analysis • Noise & variability • Summary

21. Seed-based correlation analysis “network” • Requires a priori seed (hypothesis) • How define the seed (atlas? functional localizer?) – sensitivity of results to exact size/placement • Straightforward intepretation 0.8 r r 0.3 threshold correlate seed’s time series with every other voxel’s time series 0.8 seed 0

22. Independent component analysis • Cocktail party problem • N microphones around a room record different mixtures of N speakers’ voices • How to separate the voices of each speaker? ? Observed data time1 time2 • ICA can be applied to ‘unmix’ fMRI data into networks • Multivariate time3

23. Original Sound sources “Cocktail party” mixes Estimated sources adapted from http://research.ics.aalto.fi/ica/cocktail/cocktail_en.cgi by Jen Evans

24. Independent component analysis • Cocktail party problem • N microphones around a room record different mixtures of N speakers’ voices • How to separate the voices of each speaker? ? Observed data time1 time2 • ICA can be applied to ‘unmix’ fMRI data into networks • Multivariate time3

25. Spatial ICA • Decompose fMRI data into fixed spatial components (“networks”) with time-dependent weights (network time courses) time t: a1(t) + a2(t) + aN-1(t) + aN(t) = … … McKeown et al, 1998 Thomas et al, 2002 raw_data(t)

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 difficult Review: Cole et al. 2010: “Advances and pitfalls in the analysis and interpretation of resting-state fMRI data”

28. Network analysis e.g. SEM, DCM, Granger causality, partial correlation… • complex network analysis Review: Rubinov & Sporns, 2011 Bullmore & Sporns, 2012 Review: Smith et al. 2013, TICS: Functional connectomics from resting-state fMRI Wig et al. 2011

29. Outline • Background • Properties • Analysis • Noise & variability • Summary

30. Resting state: signal vs. noise? stimulus • No model (timing of task/stimuli) • No trial averaging • Considers relationships between the voxel time series themselves (signal + noise)

31. Noise in fMRI • Thermal noise • Slow drifts (magnet instability; gradient heating) • Head motion • Physiological processes (respiration, cardiac)

32. Breathing variations affect BOLD signal Respiration BOLD 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. 2006 • Common influence over many regions creates ‘false positive’ correlations

34. Reducing physiological noise • Model-based approaches: estimate noise based on physiological measurements (e.g. RETROICOR, RETROKCOR, RV/HRCOR..). whole-brain average fMRI signal in task-free scan predicted fMRI signal derived from respiration measuremen • Data-driven approaches: estimate noise from the data itself • e.g. CompCor, FIX, PESTICA, ... Chang et al., 2009

35. Global signal regression anti-correlated resting state networks...? Murphy et al, 2009 Fransson 2005, Fox et al, 2005 are anticorrelations state-dependent?

36. State-related variability Resting (undirected) Horovitz et al., 2009 Recalling memories Eyes open/closed eyes closed Shirer et al, 2011 eyes open/fixation Bianciardi et al., 2009

37. State-related variability • Caffeine can influence resting-state correlations Wong et al. 2010 • Fluctuations in alertness/drowsiness modulate FC Chang et al. 2013

38. “Dynamic” resting-state analysis • Can we extract more information by moving beyond static / average corrlelation? + Allen et al. 2012

39. Xiao Liu et al. 2013

40. Variability: discussion • Resting-state signals and correlations vary over time • Sources: cognitive/vigilance state, noise, spontaneous…. • Consider when interpreting group differences • What time scales to study / how long to scan? • Why study variability? • model within-scan variance • neural basis of natural state changes (drowsiness, emotion….) • learn about dynamics of brain activity • Simultaneous recordings (EEG, physiology) during resting state can help

41. Outline • Background • Properties • Analysis • Noise & variability • Summary

42. Summary • Resting-state fMRI is proving valuable for clinical applications and basic neuroscience • RSNs relate to anatomic connectivity and electrophysiology, but precise relationship still not clear • Understand analysis methods/tradeoffs • no single “correct” analysis of resting-state data • avoid bias, fishing • Noise can skew connectivity estimates • clean up the signal as best as possible! See future lecture… • There can be substantial within-scan variability • need 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 Raven Jennifer Evans Dan Handwerker Peter Bandettini Gary Glover Mika Rubinov Zhongming Liu