1. Advanced Designs
fMRI: Theory and Practice
Spring 2010

2. Advanced designs and future directions
parametric designs
factorial designs
adaptation designs (fMRA)
multivoxel pattern analysis (MVPA)
network and connectivity analyses
Intersubject correlation
Mental chronometry
Monkey fMRI

3. Parametric Designs

4. 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

5. 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

6. An Example
Culham et al., 1998, J. Neuorphysiol.

7. Analysis of Parametric Designs
parametric variant:
passive viewing and tracking of 1, 2, 3, 4 or 5 balls

8. 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

9. Factorial Designs

10. 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)

11. Factorial Designs Main effects Interactions Difference between columns
Difference between rows
Interactions
Difference between columns depending on status of row (or vice versa)

12. 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

13. Main Effect of Familiarity
In the precuneus, familiar objects generated more activation than unfamiliar objects

14. Interaction of Stimuli and Familiarity
In the posterior cingulate, familiarity made a difference for places but not objects

15. 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

16. 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
Unfamiliar
Familiar

17. Combinations are Possible
Hypothetical examples
Places
Places
Brain
Activation
Objects
Brain
Activation
Objects
Unfamiliar
Familiar
Unfamiliar
Familiar
Unfamiliar
Familiar
Unfamiliar
Familiar
Main effect of Stimuli +
Main Effect of Familiarity
No interaction (parallel lines)
Main effect of Stimuli +
Main effect of Familiarity
Interaction

18. 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
Familiar
All these patterns indicate an interaction. Do they all support the theory that this brain area encodes familiar places?

19. Problems Interactions become hard to interpret
One 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!

20. fMR Adaptation

21. 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

22. 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
Neuron 1
likes
Jennifer Aniston
Neuron 2
likes
Julia Roberts
Neuron 3
likes
Brad Pitt
Even though there are neurons tuned to each object, the population as a whole shows no preference
Activation
Activation
Activation
Activation

23. fMR Adaptation
If you show a stimulus twice in a row, you get a reduced response the second time
Hypothetical Activity in
Face-Selective Area (e.g., FFA)
Unrepeated
Face
Trial 
Activation
Repeated
Face
Trial 
Time

24. fMRI Adaptation “different” trial: “same” trial: 500-1000 msec
Slide modified from Russell Epstein

25. 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

26. 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?
Viewpoint selectivity:
area codes the face as different when viewpoint changes
Repeated Individual,
Different Viewpoint 
Activation
Viewpoint invariance:
area codes the face as the same despite the viewpoint change
Time

27. Are scene representations in FFA viewpoint-invariant or viewpoint-specific?=
viewpoint-invariant =
viewpoint-specific

28. Actual Results LO
pFs (~=FFA)
Grill-Spector et al., 1999, Neuron

29. 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.)

30. 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

31. Multi-Voxel Pattern Analyses (MVPA)

32. 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

33. Coding in Voxel Patterns
Simple experiment: Show subjects pictures of different objects (e.g., shoes vs. bottles) on different trials of different runs

34. 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

35. 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.

36. Network Analyses

37. 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

38. 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

39. 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

40. 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

41. 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)

42. 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

43. Summary of Connectivity

44. Inter-subject Correlations

45. Intersubject correlation
Hasson et al. (2004, Science) showed subjects clips from a movie and found voxels which showed significant time correlations between subjects

46. Reverse correlation
They went back to the movie clips to find the common feature that may have been driving the intersubject consistency

47. Mental Chronometry study of the timing of neural events
long history in psychology

48. Latency and Width
Menon & Kim, 1999, TICS

49. Mental Chronometry Superior Parietal Cortex Superior Parietal Cortex
Data: Richter et al., 1997, Neuroreport
Figures: Huettel, Song & McCarthy, 2004

50. Mental Chronometry Menon, Luknowsky & Gati, 1998, PNAS Vary ISI
Measure
Latency
Diff
Menon, Luknowsky & Gati, 1998, PNAS

51. 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

52. Monkey fMRI

53. 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

54. Monkey fMRI might provide clues as to how brain evolved
Hand
actions
Visuospatial tasks
Calculation
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

55. Limitations of Monkey fMRI
concerns about anesthesia
cost
monkeys require extensive training
concerns about interspecies contamination
What does monkey really see?

56. Social Cognitive Neuroscience

57. 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

58. 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