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Advanced designs and future directions
• parametric designs• factorial designs• adaptation designs (fMRA)• multivoxel pattern analysis (MVPA)• network and connectivity analyses
Why are parametric designs useful in fMRI?
• As we’ve seen, the assumption of pure insertion in subtraction logic is often false• (A + B) - (B) = A
• In parametric designs, the task stays the same while the amount of processing varies; thus, changes to the nature of the task are less of a problem • (A + A) - (A) = A• (A + A + A) - (A + A) = A
Parametric Designs in Cognitive Psychology• introduced to psychology by Saul Sternberg (1969)• asked subjects to memorize lists of different lengths;
then asked subjects to tell him whether subsequent numbers belonged to the list– Memorize these numbers: 7, 3– Memorize these numbers: 7, 3, 1, 6– Was this number on the list?: 3
• longer list lengths led to longer reaction times
• Sternberg concluded that subjects were searching serially through the list in memory to determine if target matched any of the memorized numbers
Saul Sternberg
Analysis of Parametric Designs
parametric variant: • passive viewing and tracking of 1, 2, 3, 4 or 5 balls
Potential problems
• Ceiling effects?– If you see saturation of the activation, how do you
know whether it’s due to saturation of neuronal activity or saturation of the BOLD response?
Perhaps the BOLD response cannot go any higher than this?
– Possible solution: show that under other circumstances with lower overall activation, the BOLD signal still saturates
Parametric variable
BOLDActivity
Factorial Designs• Example: Sugiura et al. (2005, JOCN) showed subjects pictures of
objects and places. The objects and places were either familiar (e.g., the subject’s office or the subject’s bag) or unfamiliar (e.g., a stranger’s office or a stranger’s bag)
• This is a “2 x 2 factorial design” (2 stimuli x 2 familiarity levels)
Factorial Designs• Main effects
– Difference between columns– Difference between rows
• Interactions– Difference between columns depending on status of row (or vice
versa)
Main Effect of Stimuli
• In LO, there is a greater activation to Objects than Places
• In the PPA, there is greater activation to Places than Objects
Main Effect of Familiarity
• In the precuneus, familiar objects generated more activation than unfamiliar objects
Interaction of Stimuli and Familiarity
• In the posterior cingulate, familiarity made a difference for places but not objects
Why do People like Factorial Designs?
• If you see a main effect in a factorial design, it is reassuring that the variable has an effect across multiple conditions
• Interactions can be enlightening and form the basis for many theories
Understanding Interactions
• Interactions are easiest to understand in line graphs -- When the lines are not parallel, that indicates an interaction is present
Unfamiliar Familiar
BrainActivation
Objects
Places
Combinations are Possible
• Hypothetical examples
Unfamiliar Familiar
BrainActivation
Objects
Places
Main effect of Stimuli+
Main Effect of Familiarity
No interaction (parallel lines)
Unfamiliar Familiar
Objects
Places
Main effect of Stimuli+
Main effect of Familiarity+
Interaction
Problems• Interactions can occur for many reasons that may or may not have
anything to do with your hypothesis• A voxelwise contrast can reveal a significant for many reasons• Consider the full pattern in choosing your contrasts and
understanding the implications
Unfamiliar Familiar
BrainActivation
Objects
Places
Unfamiliar Familiar Unfamiliar Familiar
All these patterns indicate an interaction. Do they all support the theory that this brain area encodes familiar places?
Problems
• Interactions become hard to interpret – one recent psychology study suggests the human
brain cannot understand interactions that involve more than three variables
• The more conditions you have, the fewer trials per condition you have
Keep it simple!
Using fMR Adaptation to Study Coding• Example: We know that neurons in the monkey brain
can be tuned individual faces• Question: Are neurons in human cortex also tuned to
specific individuals?
“Jennifer Aniston” neuronsQuiroga et al., 2005, Nature
Using fMR Adaptation to Study TuningA
ctiv
atio
n
Act
iva
tion
Act
iva
tion
Act
iva
tion
Neuron 1likes
Jennifer Aniston
Neuron 2likes
Julia Roberts
Neuron 3likes
Brad Pitt Even though there are neurons tuned to each object, the population as a whole shows no preference
• fMRI resolution is typically around 3 x 3 x 6 mm so each sample comes from millions of neurons
fMR Adaptation
• If you show a stimulus twice in a row, you get a reduced response the second time
Repeated
FaceTrial
Unrepeated
FaceTrial
Time
Hypothetical Activity inFace-Selective Area (e.g., FFA)
Act
ivat
ion
500-1000 msec
fMRI Adaptation
Slide modified from Russell Epstein
“different” trial:
“same” trial:
And more…
• We could use this technique to determine the selectivity of face-selective areas to many other dimensions
Repeated Individual, Different
Expression
Repeated Expression,
Different Individual
Why is adaptation useful?
• Now we can ask what it takes for stimulus to be considered the “same” in an area
• For example, do face-selective areas care about viewpoint?
TimeA
ctiv
atio
n
Repeated Individual, Different Viewpoint
Viewpoint invariance:• area codes the face as the same despite the viewpoint change
Viewpoint selectivity:• area codes the face as different when viewpoint changes
=
=viewpoint-specific
viewpoint-invariant
Are scene representations in FFA viewpoint-invariant or viewpoint-specific?
Problems• The basis for effect is not well-understood
– this is seen in the many terms used to describe it• fMR adaptation (fMR-A)• priming• repetition suppression
• The effect could be due to many factors such as:– repeated stimuli are processed more “efficiently”
• more quickly?• with fewer action potentials?• with fewer neurons involved?
– repeated stimuli draw less attention– repeated stimuli may not have to be encoded into memory– repeated stimuli affect other levels of processing with input to area
demonstrating adaptation (data from Vogels et al.)– subjects may come to expect repetitions and their predictions may be
violated by novel stimuli (Summerfield et al., 2008, Nat. Neurosci.)
Problems• Adaptation effects can be quite unreliable
– variability between labs and studies– even effects that are well-established in
neurophysiology and psychophysics don’t always replicate in fMRA
• e.g., orientation selectivity in primary visual cortex– David Heeger suggests that it may be critical to
control attention
• The effect may also depend on other factors– e.g., time elapsed from first and second presentation
• days, hours, minutes, seconds, milliseconds?• number of intervening items
Perhaps voxels contain useful information
• In traditional fMRI analyses, we average across the voxels within an area, but these voxels may contain valuable information
• In traditional fMRI analyses, we assume that an area encodes a stimulus if it responds more, but perhaps encoding depends on pattern of high and low activation instead
• But perhaps there is information in the pattern of activation across voxels
Coding in Voxel Patterns
• Simple experiment: Show subjects pictures of different objects (e.g., shoes vs. bottles) on different trials of different runs
Simple Correlation Analysis
• Measure within-category correlations– within bottles (B1:B2)– within shoes (S1:S2)
• Measure between-category correlations– between bottles: shoes (B1: S2; S1: B2)
• If within-category correlations > between-category correlations, conclude that area encodes different stimuli
Decoding Algorithms
• Train algorithm to distinguish two object categories on a training set
• Test success of algorithm on distinguishing two object categories on a test set
• If algorithm succeeds better than chance, conclude that area encodes different stimuli
Norman et al., 2006, Trends Cogn. Sci.
Networks and Connectivity
• In the analyses we have investigated so far, we have been considering brain areas in isolation
• More sophisticated statistical techniques have now become available to investigate networks of activation
Anatomical Connectivity
• white matter tracts join two areas• can be measured by using tracers in other species• can be measured in living human brains with diffusion
tensor imaging (DTI)
Catani et al., 2003, Brain
Functional Connectivity• Areas show correlations in activation• Those areas may or may not be directly interconnected
Step 1: Extract time course from area of interest
Step 2: Look for other areas that are show correlated activity in the same scan
MT+ motion complexresting state scan (10 mins)
V6 (another motion selective areacorrelation with MT+: r > .8
Default Mode Network
• During resting state scans, there are two networks in which areas are correlated with each other and anticorrelated with areas in the other network
Fox and Raichle, 2007, Nat. Rev. Neurosci.
Effective Connectivity
• Activation in one area may affect activation in another
• Some techniques require an a priori model of the anatomical connections between two areas– can be dubious, especially given limited knowledge of
human anatomical connectivity
• Other techniques are model-free (e.g., Granger causality modelling)
Example of Effective Connecivity• Subjects watched a moving pattern passively or paid attention to its
speed• With attention, activity in the primary visual cortex had a greater
effect on the motion-selective area MT+/V5
Friston et al., 1997, Neuroimage
Statistical Approaches• In a 2 x 2 design, you can make up to six comparisons between
pairs of conditions (A1 vs. A2, B1 vs. B2, A1 vs. B1, A2 vs. B2, A1 vs. B2, A2 vs. B1). This is a lot of comparisons (and if you do six comparisons with p < .05, your overall p value is .05 x 6 = .3 which is high). How do you decide which to perform?
Statistical Approaches
• Without prior hypotheses:1. Do an Analysis of Variance (ANOVA) to tease apart main
effects and interactions
2. If any of these are significant, do post hoc t-tests to determine where the differences arise• These contrasts can sometimes turn out in unexpected ways
• Analysis of interactions involves looking at “differences between differences”
• With prior hypotheses:– Perform planned contrasts for comparisons of interest – e.g., you might hypothesize that in area X:
• FP > UP but FO = UO
• You could test this using just two contrasts
Problems• The basis for effect is not well-understood
– this is seen in the many terms used to describe it• fMR adaptation (fMR-A)• priming• repetition suppression
• The effect could be due to many factors such as:– repeated stimuli are processed more “efficiently”
• more quickly?• with fewer action potentials?• with fewer neurons involved?
– repeated stimuli draw less attention– repeated stimuli may not have to be encoded into
memory
Data Driven Analyses• Hasson et al. (2004, Science) showed subjects clips from a movie and
found voxels which showed significant time correlations between subjects
Reverse correlation
• They went back to the movie clips to find the common feature that may have been driving the intersubject consistency
Mental chronometry
• study of the timing of neural events• long history in psychology
Variability of HRF Between AreasPossible 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
Mental Chronometry
Data: Richter et al., 1997, NeuroreportFigures: Huettel, Song & McCarthy, 2004
Superior Parietal Cortex Superior Parietal Cortex
Mental Chronometry
Menon, Luknowsky & Gati, 1998, PNAS
Vary ISI
MeasureLatency
Diff
Challenges
• Works best with stimuli that have strong differences in timing (on the order of seconds)
• It can be challenging to reliably quantify the latency in noisy signals
Monkey fMRI
• compare physiology to neuroimaging (e.g., Logothetis et al., 2001)
• enables interspecies comparisons– missing link between monkey neurophysiology and human
neuroimaging– species differs but technique constant
Monkey fMRI
• might provide clues as to how brain evolved– compare locations of expected regions– study locations of human functions like math, language, social processing
• e.g., ventral premotor cortex in macaque may be precursor to Broca’s area in human
• could tell neurophysiologists where to stick electrodes
Calculation Language
Handactions
Visuospatial tasks
Limitations of Monkey fMRI
• concerns about anesthesia • awake monkeys move• monkeys require extensive training• concerns about interspecies contamination• “art of the barely possible” squared?
Social Cognitive Neuroscience
• find neural substrates of social behaviors– e.g., theory of mind, imitation/mirror responses,
attributions, emotions, empathy, cheater detection, cooperation/competition…
• biggest predictor of brain:body size ratio is social group size
Example
Phelps et al., 2000, Journal of Cognitive Neuroscience• White American subjects viewed pictures of unfamiliar black faces• amygdala activation was correlated with two implicit measures of
racism but not with explicit racial attitudes• difference went away when famous black faces were tested
