Transcript Advanced Experimenta.. - Culham Lab Selection Page

Advanced Designs
Advanced designs and future directions
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parametric designs
factorial designs
adaptation designs (fMRA)
multivoxel pattern analysis (MVPA)
network and connectivity analyses
Parametric Designs
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
Saul Sternberg
• 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
An Example
Culham et al., 1998, J. Neuorphysiol.
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?
BOLD
Activity
Parametric variable
– Possible solution: show that under other
circumstances with lower overall activation, the BOLD
signal still saturates
Factorial Designs
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
Places
Brain
Activation
Objects
Unfamiliar
Familiar
Combinations are Possible
• Hypothetical examples
Places
Brain
Activation
Places
Objects
Objects
Unfamiliar
Familiar
Main effect of Stimuli
+
Main Effect of Familiarity
No interaction (parallel lines)
Unfamiliar
Familiar
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
Places
Brain
Activation
Objects
Unfamiliar
Familiar
Unfamiliar
Familiar
Unfamiliar
All these patterns indicate an interaction. Do they all support the theory
that this brain area encodes familiar places?
Familiar
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!
fMR Adaptation
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” neurons
Quiroga et al., 2005, Nature
Using fMR Adaptation to Study Tuning
• fMRI resolution is typically around 3 x 3 x 6 mm so each sample comes
from millions of neurons
Even though
there are
neurons tuned to
each object, the
population as a
whole shows no
preference
Activation
Neuron 3
likes
Brad Pitt
Activation
Neuron 2
likes
Julia Roberts
Activation
Activation
Neuron 1
likes
Jennifer Aniston
fMR Adaptation
• If you show a stimulus twice in a row, you get a
reduced response the second time
Unrepeated
Face
Trial

Repeated
Face
Trial

Activation
Hypothetical Activity in
Face-Selective Area (e.g., FFA)
Time
fMRI Adaptation
“different” trial:
500-1000 msec
“same” trial:
Slide modified from Russell Epstein
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?

Activation
Repeated
Individual,
Different
Viewpoint
Viewpoint selectivity:
• area codes the face
as different when
viewpoint changes
Viewpoint invariance:
• area codes the face
as the same despite
the viewpoint change
Time
Are scene representations in FFA viewpointinvariant or viewpoint-specific?
viewpointinvariant
=
viewpointspecific
=
Actual Results
LO
pFs (~=FFA)
Grill-Spector et al., 1999, Neuron
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
Multivoxel Pattern Analyses
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.
Network Analyses
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
MT+ motion complex
resting state scan (10 mins)
Step 2: Look for other areas that are show correlated activity in the same scan
V6 (another motion selective area
correlation with MT+: r > .8
Default Mode Network
Fox and Raichle, 2007, Nat. Rev. Neurosci.
• During resting state scans, there are two
networks in which areas are correlated with
each other and anticorrelated with areas in
the other network
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
Summary of Connectivity
EXTRA SLIDES
Statistical Approaches
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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
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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
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These contrasts can sometimes turn out in unexpected ways
Analysis of interactions involves looking at “differences between
differences”
With prior hypotheses:
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Perform planned contrasts for comparisons of interest
e.g., you might hypothesize that in area X:
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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 Approaches
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
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They went back to the movie clips
to find the common feature that
may have been driving the
intersubject consistency
Mental Chronometry
Mental chronometry
• study of the timing of neural events
• long history in psychology
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
Latency and Width
Menon & Kim, 1999, TICS
Mental Chronometry
Superior Parietal Cortex
Superior Parietal Cortex
Data: Richter et al., 1997, Neuroreport
Figures: Huettel, Song & McCarthy, 2004
Mental Chronometry
Vary ISI
Measure
Latency
Diff
Menon, Luknowsky & Gati, 1998, PNAS
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
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
Hand
actions
Visuospatial
tasks
Calculation
Monkey fMRI
Language
• 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
Limitations of Monkey fMRI
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concerns about anesthesia
awake monkeys move
monkeys require extensive training
concerns about interspecies contamination
“art of the barely possible” squared?
Social Cognitive Neuroscience
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