ADVANCED DESIGNS AND FUTURE DIRECTIONS

1. ADVANCED DESIGNSAND FUTURE DIRECTIONS

2. Advanced designs and future directions • parametric designs • factorial designs • adaptation designs • mental chronometry • network and connectivity analyses • monkey fMRI • social cognitive neuroscience

3. Subtraction Logic Cognitive subtraction originated with reaction time experiments (F. C. Donders, a Dutch physiologist). Measure the time for a process to occur by comparing two reaction times, one which has the same components as the other + the process of interest. Example: T1: Hit a button when you see a light T2: Hit a button when the light is green but not red T3: Hit the left button when the light is green and the right button when the light is red T2 – T1 = time to make discrimination between light color T3 – T2 = time to make a decision F. C. Donders Assumption of pure insertion: You can insert a component process into a task without disrupting the other components. Widely criticized (we’ll come back to this when we talk about parametric studies) Source: Jody Culham’s fMRI for Dummies web site

4. Parametric Designs • 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

5. Why are parametric designs useful in fMRI? • As we’ve seen, the assumption of pure insertion is often false • (A + B) - (B) = A • The assumptions of subtraction logic are less susceptible to these problems • (A + A) - (A) = A • (A + A + A) - (A + A) = 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

6. An Example Culham, Kanwisher & Cavanagh, 2001, Neuron

7. 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

8. Statistics • Statistics for parametric designs can be more complicated How do you describe these functions?

9. 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)

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

11. 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

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

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

14. 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

15. Combinations are Possible • Hypothetical examples Places Places Brain Activation Objects Objects 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

16. 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?

17. 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?

18. Statistical Approaches • Without prior hypotheses: • Do an Analysis of Variance (ANOVA) to tease apart main effects and interactions • 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 • however… if you really want to say that F vs. U is greater for P than O, you have to look for the difference of differences [(FP-UP) - (FO-UO)], i.e., the interaction

19. 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 • Some recommend factorial designs in lieu of localizers (Friston et al., 2006, Neuroimage)

20. 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!

21. Using fMR Adaptation to Study Tuning • Example: We know that neurons in the monkey superior temporal sulcus can be tuned viewpoint • Question: Are neurons in the human fusiform face area also tuned to viewpoint? Here is an example of one neuron in monkey STS. This particular neuron responds best to profile views of faces. Tuning curve for this one neuron Neuronal activity 60° 90° 120° 150° 180° Viewing angle

22. Using fMR Adaptation to Study Tuning • Intuitive idea: Can we just compare responses to faces of different orientations in different blocks? • No, because the neural response depends not just on the tuning of neurons but the number of neurons with that tuning • For example, if there are equal numbers of neurons tuned to different directions, we will have equal responses across all conditions Tuning curves for three different neurons  We need another way to investigate tuning Neuronal activity 60° 120° 180° -60° 0° -30° 30° 90° 150° Viewing angle

23. Repeated Face Trial  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 Time

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

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

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. 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

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

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. Mental chronometry • study of the timing of neural events • long history in psychology

32. 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

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

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

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

36. 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

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 • This is sometimes called functional connectivity analyses

38. Connectivity Analyses • 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

39. Data Driven Analyses • Hasson et al. (2004, Science) showed subjects clips from a movie and found voxels which showed significant time correlations between subjects

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

41. 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

42. Monkey fMRI Hand actions Visuospatial tasks • 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

43. Limitations of Monkey fMRI • concerns about anesthesia • awake monkeys move • monkeys require extensive training • concerns about interspecies contamination • “art of the barely possible” squared?

44. 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

45. 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