Presentation is loading. Please wait.

Presentation is loading. Please wait.

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

Similar presentations


Presentation on theme: "Resting State fMRI Catie Chang Advanced MRI Section, LFMI, NINDS, NIH."— Presentation transcript:

1

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

3 Outline Background Properties Analysis Noise & variability Summary

4 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....

5 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...)

6 Functional connectivity analysis r seed correlate seed’s time series with every other voxel’s time series threshold seed r We can analyze relationships between the time series of different brain regions E.g., seed-based correlation analysis:

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

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

9 Resting-state networks Rocca et a 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

10 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

11 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

12 Outline Background Properties Analysis Noise & variability Summary

13 RSNs 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 Sleep

14 Default 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 Greicius et al functional connectivity in resting state

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

16 Simultaneous 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

17 Human ECoG of resting-state activity Keller et al 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?

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

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

20 Clinical applications Healthy control Alzheimer’s Schizophrenia Greicius et al. 2004, Whitfield-Gabrieli et al. 2009, Lewis et al 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

21 Outline Background Properties Analysis Noise & variability Summary

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

23 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

24 Original Sound sources “Cocktail party” mixes Estimated sources adapted from by Jen Evans

25 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

26 Decompose fMRI data into fixed spatial components (“networks”) with time-dependent weights (network time courses) McKeown et al, 1998 Thomas et al, = raw_data(t) … … time t: a N (t) a 1 (t) a N-1 (t) a 2 (t) Spatial ICA

27 Independent component analysis Damoiseaux et al. 2006

28 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”

29 Network analysis e.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

30 Outline Background Properties Analysis Noise & variability Summary

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

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

33 BOLD 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

34 Birn et al Changes in rate / depth of breathing over time correlate with BOLD signal Common influence over many regions creates ‘false positive’ correlations

35 Chang 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,...

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

37 State-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

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

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

40 Xiao Liu et al. 2013

41 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

42 Outline Background Properties Analysis Noise & variability Summary

43 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

44 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


Download ppt "Resting State fMRI Catie Chang Advanced MRI Section, LFMI, NINDS, NIH."

Similar presentations


Ads by Google