Spatial and Temporal Limits of fMRI Jody Culham Department of Psychology University of Western Ontario http://www.fmri4newbies.com/ Spatial and Temporal Limits of fMRI Last Update: November 29, 2008 fMRI Course, Louvain, Belgium

Spatial Limits of fMRI

fMRI in the Big Picture

What Limits Spatial Resolution noise smaller voxels have lower SNR head motion the smaller your voxels, the more contamination head motion induces temporal resolution the smaller your voxels, the longer it takes to acquire the same volume 4 mm x 4 mm at 16 slices/sec OR 1 mm x 1 mm at 1 slice/sec vasculature depends on pulse sequences e.g., spin echo sequences reduce contributions from large vessels some preprocessing techniques may reduce contribution of large vessels (Menon, 2002, MRM)

Ocular Dominance Columns Columns on the order of ~0.5 mm have been observed with fMRI

Submillimeter Resolution vein Stria of Gennari (Layer IV) Spin Echo Functional (activation localized to Layer IV) Gradient Echo Functional (superficial activation includes vessels) Spin Echo Anatomical Gradient Echo Anatomical Goenze, Zappe & Logothetis, 2007, Magnetic Resonance Imaging anaesthetized monkey; 4.7 T; contrast agent (MION) ~0.3 x 0.3 x 2 mm

Temporal Limits of fMRI AND Event-Related Averaging

Sampling Rate Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

BOLD Time Course

Evolution of BOLD Response Hu et al., 1997, MRM

Event-Related Averaging In this example an “event” is the start of a block

Event-Related Averaging

Event-Related Averaging

Event-Related Averaging “Zero” = average signal intensity in first volume of all 8 events

Event-Related Averaging RAW Not particularly useful No baseline, just averaged values Error bars may be huge (esp. if multiple runs) FILE-BASED Zero = average across all events at specified volume(s) Safest procedure to use Most similar to GLM stats EPOCH-BASED Zero = starting point of each curve at specified volume(s) Sometimes useful if well-justified May look very different than GLM stats

Event-related Averaging File-based zero is based on average starting point of all curves works best when low frequencies have been filtered out of your data similar to what your GLM stats are testing Epoch-based each curve starts at zero can be risky with noisy data only use it if you are fairly certain your pre-stim baselines are valid (e.g., you have a long ITI or your trial orders are counterbalanced) can give very different results from GLM stats

Convolution of Single Trials Neuronal Activity BOLD Signal Haemodynamic Function Time Time Slide from Matt Brown

BOLD Summates Neuronal Activity BOLD Signal Slide from Matt Brown

BOLD Overlap and Jittering Closely-spaced haemodynamic impulses summate. Constant ITI causes tetanus. Burock et al. 1998.

Design Types Block Design Slow ER Design Rapid Counterbalanced = trial of one type (e.g., face image) = null trial (nothing happens) Design Types = trial of another type (e.g., place image) Block Design Slow ER Design Rapid Counterbalanced ER Design Rapid Jittered ER Design Jody Mixed Design

Block Designs = trial of one type (e.g., face image) = trial of another type (e.g., place image) Block Design Early Assumption: Because the hemodynamic response delays and blurs the response to activation, the temporal resolution of fMRI is limited. WRONG!!!!! Jody

What are the temporal limits? What is the briefest stimulus that fMRI can detect? Blamire et al. (1992): 2 sec Bandettini (1993): 0.5 sec Savoy et al (1995): 34 msec 2 s stimuli single events Data: Blamire et al., 1992, PNAS Figure: Huettel, Song & McCarthy, 2004 Data: Robert Savoy & Kathy O’Craven Figure: Rosen et al., 1998, PNAS Jody Although the shape of the HRF delayed and blurred, it is predictable. Event-related potentials (ERPs) are based on averaging small responses over many trials. Can we do the same thing with fMRI?

Detection vs. Estimation detection: determination of whether activity of a given voxel (or region) changes in response to the experimental manipulation 1 estimation: measurement of the time course within an active voxel in response to the experimental manipulation % Signal Change Jody 4 8 12 Time (sec) Definitions modified from: Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

Block Designs: Poor Estimation Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

Pros & Cons of Block Designs high detection power has been the most widely used approach for fMRI studies accurate estimation of hemodynamic response function is not as critical as with event-related designs Cons poor estimation power subjects get into a mental set for a block very predictable for subject can’t look at effects of single events (e.g., correct vs. incorrect trials, remembered vs. forgotten items) becomes unmanagable with too many conditions (4 conditions + baseline is about the max I will use in one run) Jody

Slow Event-Related Designs Slow ER Design Jody

Spaced Mixed Trial: Constant ITI Bandettini et al. (2000) What is the optimal trial spacing (duration + intertrial interval, ITI) for a Spaced Mixed Trial design with constant stimulus duration? 2 s stim vary ISI Block Sync with trial onset and average Jody Source: Bandettini et al., 2000

Optimal Constant ITI Brief (< 2 sec) stimuli: Source: Bandettini et al., 2000 Brief (< 2 sec) stimuli: optimal trial spacing = 12 sec For longer stimuli: optimal trial spacing = 8 + 2*stimulus duration Effective loss in power of event related design: = -35% i.e., for 6 minutes of block design, run ~9 min ER design Jody

Trial to Trial Variability Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

How Many Trials Do You Need? Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging standard error of the mean varies with square root of number of trials Number of trials needed will vary with effect size Function begins to asymptote around 15 trials

Effect of Adding Trials Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

Pros & Cons of Slow ER Designs good estimation power allows accurate estimate of baseline activation and deviations from it useful for studies with delay periods very useful for designs with motion artifacts (grasping, swallowing, speech) because you can tease out artifacts analysis is straightforward  Hand motion artifact % signal change Time Activation  Cons poor detection power because you get very few trials per condition by spending most of your sampling power on estimating the baseline subjects can get VERY bored and sleepy with long inter-trial intervals Jody

Can we go faster?! Yes, but we have to test assumptions regarding linearity of BOLD signal first Rapid Counterbalanced ER Design Rapid Jittered ER Design Tzvi Mixed Design

Linearity of BOLD response “Do things add up?” red = 2 - 1 green = 3 - 2 Sync each trial response to start of trial Tzvi Not quite linear but good enough! Source: Dale & Buckner, 1997

Optimal Rapid ITI Rapid Mixed Trial Designs Source: Dale & Buckner, 1997 Tzvi Rapid Mixed Trial Designs Short ITIs (~2 sec) are best for detection power Do you know why?

Design Types Rapid Counterbalanced ER Design Jody = trial of one type (e.g., face image) Design Types = trial of another type (e.g., place image) Rapid Counterbalanced ER Design Jody

Detection with Rapid ER Designs Figure: Huettel, Song & McCarthy, 2004 To detect activation differences between conditions in a rapid ER design, you can create HRF-convolved reference time courses You can perform contrasts between beta weights as usual Jody

Variability of HRF Between Subjects Aguirre, Zarahn & D’Esposito, 1998 HRF shows considerable variability between subjects different subjects Within subjects, responses are more consistent, although there is still some variability between sessions same subject, same session same subject, different session

Variability of HRF Between Areas Possible caveat: HRF may also vary between areas, not just subjects Buckner et al., 1996: noted a delay of .5-1 sec between visual and prefrontal regions vasculature difference? processing latency? Bug or feature? Menon & Kim – mental chronometry Buckner et al., 1996

Variability Between Subjects/Areas greater variability between subjects than between regions deviations from canonical HRF cause false negatives (Type II errors) Consider including a run to establish subject-specific HRFs from robust area like M1 Handwerker et al., 2004, Neuroimage

The Problem of Trial History Activation Activation WARNING: This slide is confusing, needs to be redone. Supposed to show that yellow>red>white, not just because of trial summation Time Time Event-related average is wonky because trial types differ in the history of preceding trials Estimation does not work well if trial history differs between trial types Two options Control trial history by making it the same for all trial types Model the trial history by deconvolving the signal (requires jittered timing)

One Approach to Estimation: Counterbalanced Trial Orders Each condition must have the same history for preceding trials so that trial history subtracts out in comparisons For example if you have a sequence of Face, Place and Object trials (e.g., FPFOPPOF…), with 30 trials for each condition, you could make sure that the breakdown of trials (yellow) with respect to the preceding trial (blue) was as follows: …Face  Face x 10 …Place  Face x 10 …Object  Face x 10 …Face  Place x 10 …Place  Place x 10 …Object  Place x 10 …Face  Object x 10 …Place  Object x 10 …Object  Object x 10 Most counterbalancing algorithms do not control for trial history beyond the preceding one or two items

Analysis of Single Trials with Counterbalanced Orders Approach used by Kourtzi & Kanwisher (2001, Science) for pre-defined ROI’s: for each trial type, compute averaged time courses synced to trial onset; then subtract differences Event-related average with control period factored out A signal change = (A – F)/F B signal change = (B – F)/F … Raw data Event-related average sync to trial onset A B F

Pros & Cons of Counterbalanced Rapid ER Designs high detection power with advantages of ER designs (e.g., can have many trial types in an unpredictable order) Cons and Caveats reduced detection compared to block designs estimation power is better than block designs but not great accurate detection requires accurate HRF modelling counterbalancing only considers one or two trials preceding each stimulus; have to assume that higher-order history is random enough not to matter what do you do with the trials at the beginning of the run… just throw them out? you can’t exclude error trials and keep counterbalanced trial history you can’t use this approach when you can’t control trial status (e.g., items that are later remembered vs. forgotten)

Design Types Rapid Jittered ER Design Jody = trial of one type (e.g., face image) Design Types = trial of another type (e.g., place image) Rapid Jittered ER Design Jody

BOLD Overlap With Regular Trial Spacing Neuronal activity from TWO event types with constant ITI Partial tetanus BOLD activity from two event types Slide from Matt Brown

BOLD Overlap with Jittering Neuronal activity from closely-spaced, jittered events BOLD activity from closely-spaced, jittered events Slide from Matt Brown

BOLD Overlap with Jittering Neuronal activity from closely-spaced, jittered events BOLD activity from closely-spaced, jittered events Slide from Matt Brown

Fast fMRI Detection A) BOLD Signal B) Individual Haemodynamic Components C) 2 Predictor Curves for use with GLM (summation of B) Slide from Matt Brown

Post Hoc Trial Sorting Example Wagner et al., 1998, Science

Fast fMRI Detection Pros: Incorporates prior knowledge of BOLD signal form affords some protection against noise Easy to implement Can do post hoc sorting of trial type Cons: Vulnerable to inaccurate hemodyamic model No time course produced independent of assumed haemodynamic shape

Fast fMRI Estimation We can detect fMRI activation in rapid event related designs in the same way that we do for other designs (block design, slow event related design For any kind of event-related designs, it is very important to have a resonably accurate model of the HRF In addition, with rapid event related designs, we can also estimate time courses using a technique called deconvolution

Convolution of Single Trials Neuronal Activity BOLD Signal Haemodynamic Function Time Time Slide from Matt Brown

DEconvolution of Single Trials Neuronal Activity BOLD Signal Haemodynamic Function Time Time Slide from Matt Brown

Deconvolution Example time course from 4 trials of two types (pink, blue) in a “jittered” design

Summed Activation

Single Stick Predictor single predictor for first volume of pink trial type

Predictors for Pink Trial Type set of 12 predictors for subsequent volumes of pink trial type need enough predictors to cover unfolding of HRF (depends on TR)

Predictor Matrix Diagonal filled with 1’s .

Predictors for the Blue Trial Type set of 12 predictors for subsequent volumes of blue trial type

Linear Deconvolution Miezen et al. 2000 Jittering ITI also preserves linear independence among the hemodynamic components comprising the BOLD signal.

Predictor x Beta Weights for Pink Trial Type sequence of beta weights for one trial type yields an estimate of the average activation (including HRF)

Predictor x Beta Weights for Blue Trial Type height of beta weights indicates amplitude of response (higher betas = larger response)

Fast fMRI: Estimation Pros: Produces time course Does not assume specific shape for hemodynamic function Can use narrow jitter window Robust against trial history biases (though not immune to it) Compound trial types possible Cons: Complicated Unrealistic assumptions about maintenance activity BOLD is non-linear with inter-event intervals < 6 sec. Nonlinearity becomes severe under 2 sec. Sensitive to noise

Design Types Mixed Design Jody = trial of one type (e.g., face image) = trial of another type (e.g., place image) Jody Mixed Design

Example of Mixed Design Otten, Henson, & Rugg, 2002, Nature Neuroscience used short task blocks in which subjects encoded words into memory In some areas, mean level of activity for a block predicted retrieval success

Pros and Cons of Mixed Designs allow researchers to distinguish between state-related and item-related activation Cons sensitive to errors in HRF modelling

A Variant of Mixed Designs: Semirandom Designs a type of event-related design in which the probability of an event will occur within a given time interval changes systematically over the course of an experiment First period: P of event: 25% Middle period: P of event: 75% Last period: P of event: 25% probability as a function of time can be sinusoidal rather than square wave

Pros and Cons of Semirandom Designs good tradeoff between detection and estimation simulations by Liu et al. (2001) suggest that semirandom designs have slightly less detection power than block designs but much better estimation power Cons relies on assumptions of linearity complex analysis “However, if the process of interest differs across ISIs, then the basic assumption of the semirandom design is violated. Known causes of ISI-related differences include hemodynamic refractory effects, especially at very short intervals, and changes in cognitive processes based on rate of presentation (i.e., a task may be simpler at slow rates than at fast rates).” -- Huettel, Song & McCarthy, 2004

EXTRA SLIDES

Voxel Size non-isotropic non-isotropic EXCEPT when the activated region does not fill the voxel (partial voluming) isotropic 3 x 3 x 6 = 54 mm3 e.g., SNR = 100 3 x 3 x 3 = 27 mm3 e.g., SNR = 71 2.1 x 2.1 x 6 = 27 mm3 e.g., SNR = 71 In general, larger voxels buy you more SNR.

Partial Voluming The fMRI signal occurs in gray matter (where the synapses and dendrites are) If your voxel includes white matter (where the axons are), fluid, or space outside the brain, you effectively water down your signal fMRI for Dummies

Partial Voluming Partial volume effects: The combination, within a single voxel, of signal contributions from two or more distinct tissue types or functional regions (Huettel, Song & McCarthy, 2004) This voxel contains mostly gray matter This voxel contains mostly white matter This voxel contains both gray and white matter. Even if neurons within the voxel are strongly activated, the signal may be washed out by the absence of activation in white matter. Partial voluming becomes more of a problem with larger voxel sizes Worst case scenario: A 22 cm x 22 cm x 22 cm voxel would contain the whole brain

The Initial Dip The initial dip seems to have better spatial specificity However, it’s often called the “elusive initial dip” for a reason

Initial Dip (Hypo-oxic Phase) Transient increase in oxygen consumption, before change in blood flow Menon et al., 1995; Hu, et al., 1997 Smaller amplitude than main BOLD signal 10% of peak amplitude (e.g., 0.1% signal change) Potentially more spatially specific Oxygen utilization may be more closely associated with neuronal activity than positive response Slide modified from Duke course

Rise (Hyperoxic Phase) Results from vasodilation of arterioles, resulting in a large increase in cerebral blood flow Inflection point can be used to index onset of processing Slide modified from Duke course

Slide modified from Duke course Peak – Overshoot Over-compensatory response More pronounced in BOLD signal measures than flow measures Overshoot found in blocked designs with extended intervals Signal saturates after ~10s of stimulation Slide modified from Duke course

Slide modified from Duke course Sustained Response Blocked design analyses rest upon presence of sustained response Comparison of sustained activity vs. baseline Statistically simple, powerful Problems Difficulty in identifying magnitude of activation Little ability to describe form of hemodynamic response May require detrending of raw time course Slide modified from Duke course

Slide modified from Duke course Undershoot Cerebral blood flow more locked to stimuli than cerebral blood volume Increased blood volume with baseline flow leads to decrease in MR signal More frequently observed for longer-duration stimuli (>10s) Short duration stimuli may not evidence May remain for 10s of seconds Slide modified from Duke course

Implications Aguirre, Zarahn & D’Esposito, 1998 Generic HRF models (gamma functions) account for 70% of variance Subject-specific models account for 92% of the variance (22% more!) Poor modelling reduces statistical power Less of a problem for block designs than event-related Biggest problem with delay tasks where an inappropriate estimate of the initial and final components contaminates the delay component

Blocked vs. Event-related Source: Buckner 1998

Advantages of Event-Related Flexibility and randomization eliminate predictability of block designs avoid practice effects Post hoc sorting (e.g., correct vs. incorrect, aware vs. unaware, remembered vs. forgotten items, fast vs. slow RTs) Can look at novelty and priming Rare or unpredictable events can be measured e.g., P300 Can look at temporal dynamics of response Dissociation of motion artifacts from activation Dissociate components of delay tasks Mental chronometry Source: Buckner & Braver, 1999

Exponential Distribution of ITIs Flat Distribution Exponential Distribution Frequency Frequency 2 3 4 5 6 7 2 3 4 5 6 7 Intertrial Interval Intertrial Interval An exponential distribution of ITIs is recommended WARNING: I’ve been getting conflicting advice on whether it’s better to have an exponential distribtuion… need to find out more

NEW SLIDES