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

Outline Background Properties Analysis Noise & variability Summary

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....

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...)

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

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

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

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

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

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

Outline Background Properties Analysis Noise & variability Summary

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

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

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

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

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)

Functional connectivity at finer spatial scales Beckmann et al. 2005 Buckner et al. 2011 Kim et al. 2013

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

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

Outline Background Properties Analysis Noise & variability Summary

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

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

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

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

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

Independent component analysis Damoiseaux et al. 2006

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”

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

Outline Background Properties Analysis Noise & variability Summary

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)

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

(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

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

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, ...

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

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

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

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

Xiao Liu et al. 2013

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

Outline Background Properties Analysis Noise & variability Summary

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

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