1. Advanced Designs

2. Advanced designs and future directions parametric designs factorial designs adaptation designs (fMRA) multivoxel pattern analysis (MVPA) network and connectivity analyses

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 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

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? –Possible solution: show that under other circumstances with lower overall activation, the BOLD signal still saturates Parametric variable BOLD Activity

9. Factorial Designs

10. 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 –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 UnfamiliarFamiliar Brain Activation Objects Places

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

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 Activation 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 fMRI resolution is typically around 3 x 3 x 6 mm so each sample comes from millions of neurons

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

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

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? Time Activation  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

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

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

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. Multivoxel Pattern Analyses

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 Step 2: Look for other areas that are show correlated activity in the same scan MT+ motion complex resting state scan (10 mins) V6 (another motion selective area correlation with MT+: r >.8

40. 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.

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. EXTRA SLIDES

45. 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?

46. 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

47. 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

48. Data-Driven Approaches

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

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

51. Mental Chronometry

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

53. 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

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

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

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

57. 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

58. Monkey fMRI

59. 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

60. 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 CalculationLanguage Hand actions Visuospatial tasks

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

62. Social Cognitive Neuroscience

63. 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

64. 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