Advanced MRI Section LFMI NINDS NIH Outline Background Properties Analysis Noise amp variability Summary Restingstate fMRI no task or stimuli typical instructions keep eyes closed or keep them openfixation dont fall asleep let your mind freely wander ID: 935882
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Slide1
Resting State fMRI
Catie Chang
Advanced MRI Section, LFMI, NINDS, NIH
Slide2OutlineBackground
Properties
Analysis
Noise & variability
Summary
Slide3Resting-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....
Slide4Resting-state fMRIresting-state signal fluctuations = ?
spontaneous neural activity (i.e., cannot be attributed to a task or overt behavior)
noise (hardware, motion, physiological...)
Slide5Functional connectivity analysis
0
0.8
r
seed
correlate seed’s time series with every other voxel’s time series
threshold
seed
0.3
0.8
r
We can analyze relationships between the time series of different brain regions
E.g., seed-based correlation analysis:
Slide6Functional connectivityWe 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
Slide7Resting-state “networks” have a close correspondence with task-activation networks
Task
Rest
Task
Rest
Task
Rest
Smith et al. 2010
Slide8Resting-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
Slide9Implications
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
Slide10Challenges
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
Slide11OutlineBackground
Properties
Analysis
Noise & variability
Summary
Slide12RSNs are (mostly) conserved across sessions, individuals, states, species, ...
suggests not arising solely from conscious processes
Infants
Monkeys
Rats
Horovitz et al. 2008; Vincent et al. 2007; Lu et al. 2007; Doria et al. 2010
Sleep
Slide13Default Mode Network
higher activity during passive baseline conditions comapred to (most) tasks
Raichle at el., 2001
review: Buckner et al. , Ann. N.Y. Acad. Sci. 2008
Greicius et al. 2003
functional connectivity in resting state
Slide14Coherence in spontaneous electrophysiological signals
Kenet
et al, 2003
spontaneous fluctuations in membrane voltage resemble orientation columns & evoked activity
Slide15Simultaneous LFP-fMRI of
resting-state fluctuations
Shmuel
& Leopold, 2008
gamma power fluctuations in local field potential (LFP) found to correlate with fMRI signal
correlations are
spatially widespread!
Scholvinck et al., 2010
Slide16Human ECoG of resting-state activity
Keller et al. 2013
also with slow cortical potential (He et al, 2010)
macaque ECoG reveals broadband phenomenon (Liu et al. 2014)
How well do “networks” of electrical signals match “networks” of BOLD fMRI?
Slide17Functional connectivity at finer spatial scales
Buckner et al. 2011
Kim et al. 2013
Beckmann et al. 2005
Slide18Quigley et al., 2003
task activation
resting-state
functional connectivity
Johnston et al., 2008
Structrual connectivity affects functional connectivity
via indirect connections?
Slide19Clinical applications
Healthy control
Alzheimer’s
Schizophrenia
Greicius et al. 2004,
Whitfield-Gabrieli et al. 2009,
Lewis et al. 2009
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, ......
Underpinnings of altered functional connectivity need further investigation
Slide20OutlineBackground
Properties
Analysis
Noise & variability
Summary
Slide21Seed-based correlation analysis
0
0.8
r
seed
correlate seed’s time series with every other voxel’s time series
threshold
0.3
0.8
r
“network”
Requires a priori seed (hypothesis)
How define the seed (atlas? functional localizer?) – sensitivity of results to exact size/placement
Straightforward intepretation
Slide22Independent component analysisCocktail 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
Slide23Original Sound sources
“Cocktail party”
mixes
Estimated sources
adapted from
http://
research.ics.aalto.fi
/
ica
/cocktail/
cocktail_en.cgi
by Jen Evans
Slide24Independent component analysisCocktail 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
Slide25Decompose fMRI data into fixed spatial components (“networks”) with time-dependent weights (network time courses)
McKeown et al, 1998
Thomas et al, 2002
+
+
+
=
raw_data(t)
…
…
time t:
a
N
(t)
a
1
(t)
a
N-1
(t)
a
2
(t)
Spatial ICA
Slide26Independent component analysis
Damoiseaux et al. 2006
Slide27ICA
+ 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”
Slide28Network analysise.g. SEM, DCM, Granger causality, partial correlation…
complex network analysis
Review: Smith et al. 2013, TICS: Functional connectomics from resting-state fMRI
Review: Rubinov & Sporns, 2011
Bullmore & Sporns, 2012
Wig et al. 2011
Slide29OutlineBackground
Properties
Analysis
Noise & variability
Summary
Slide30Resting state: signal vs. noise?
No model (timing of task/stimuli)
No trial averaging
Considers relationships between
the voxel time series themselves (signal + noise)
stimulus
Slide31Thermal noise
Slow drifts (magnet instability; gradient heating)
Head motion
Physiological processes (respiration, cardiac)
Noise in fMRI
Slide32BOLD signal(whole-brain average)
Respiration
Breathing variations affect BOLD signal
Respiratory variations (RVT)
changes in [CO2], HR, blood pressure
hemodynamic response uncoupled from local neural activity
Slide33Birn
et al. 2006
Changes in rate / depth of breathing over time correlate with BOLD signal
Common influence over many regions creates ‘false positive’ correlations
Slide34Chang et al., 2009
Reducing physiological noise
whole-brain average fMRI signal in task-free scan
predicted fMRI signal derived from respiration measuremen
Model-based approaches: estimate noise based on physiological measurements (e.g. RETROICOR, RETROKCOR, RV/HRCOR..).
Data-driven approaches: estimate noise from the data itself
e.g. CompCor, FIX, PESTICA, ...
Slide35anti-correlated resting state networks...?
Fransson
2005,
Fox et al, 2005
Global signal regression
Murphy et al, 2009
are anticorrelations state-dependent?
Slide36State-related variability
Resting
(undirected)
Recalling
memories
Shirer et al, 2011
Horovitz
et al., 2009
eyes closed
eyes open/fixation
Eyes open/closed
Bianciardi
et al., 2009
Slide37State-related variabilityCaffeine can influence resting-state correlations
Wong et al. 2010
Fluctuations in alertness/drowsiness modulate FC
Chang et al. 2013
Slide38“Dynamic” resting-state analysisCan we extract more information by moving beyond static / average corrlelation?
Allen et al. 2012
+
Slide39Xiao Liu
et al. 2013
Slide40Variability: discussionResting-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
Slide41OutlineBackground
Properties
Analysis
Noise & variability
Summary
Slide42Summary
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 variabilityneed to understand these effects, determine what information is valuable
Slide43Thanks!AMRI group:
Jeff Duyn
Xiao Liu
Dante Picchioni
Jacco de Zwart
Peter Van Gelderen
Natalia GudinoRoger Jiang
Xiaozhen LiHendrik MandelkowErika Raven
Jennifer Evans
Dan Handwerker
Peter Bandettini
Gary Glover
Mika Rubinov
Zhongming Liu