1. Catie Chang Advanced MRI Section, LFMI, NINDS, NIH
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+
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
seed
0.3
0.8 r
correlate seed’s time series with every other voxel’s time series
threshold
seed

6. Functional connectivity
We can analyze relationships between the time series of different brain regions
Biswal et al. 1995
time series during resting-state scan
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
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, ...
Monkeys
Infants
Sleep
Rats
Horovitz et al. 2008; Vincent et al. 2007; Lu et al. 2007; Doria et al. 2010
suggests not arising solely from conscious processes

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
-- 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, 2003
spontaneous fluctuations in membrane voltage resemble orientation columns & evoked activity

15. Simultaneous LFP-fMRI of resting-state fluctuations
correlations are
spatially widespread!
Scholvinck et al., 2010
Shmuel & Leopold, 2008
gamma 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. 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
Johnston et al., 2008
Quigley et al., 2003
task activation
resting-state
functional connectivity
via indirect connections?

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
Underpinnings of altered functional connectivity need further investigation
Greicius et al. 2004,
Whitfield-Gabrieli et al. 2009,
Lewis et al. 2009

20. Outline
Background
Properties
Analysis
Noise & variability
Summary

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

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? ?
time1
Observed data
time2
time3
ICA can be applied to ‘unmix’ fMRI data into networks
Multivariate

23. Original Sound sources
“Cocktail party” mixes
Estimated sources
adapted from
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? ?
time1
Observed data
time2
time3
ICA can be applied to ‘unmix’ fMRI data into networks
Multivariate

25. Spatial ICA
Decompose 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, 1998
Thomas 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 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. (whole-brain average)
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. predicted fMRI signal derived from respiration measuremen
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
Chang et al., 2009
Data-driven approaches: estimate noise from the data itself
e.g. CompCor, FIX, PESTICA, ...

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