FMRI Data Analysis: II. Advanced Data Analysis

1. FMRI Data Analysis:II. Advanced Data Analysis FMRI Undergraduate Course (PSY 181F) FMRI Graduate Course (NBIO 381, PSY 362) Dr. Scott Huettel, Course Director FMRI – Week 10 – Analysis II Scott Huettel, Duke University

2. Advanced Data Analyses • Complex modeling • Analyses of Connectivity • Functional Connectivity Analysis • Causality analysis • Across-subjects regularities • Independent Components Analysis • Prediction • Real-time analyses • Correlation techniques • Support Vector Machines FMRI – Week 10 – Analysis II Scott Huettel, Duke University

3. Big Concept • Your analysis model should not determined by your stimuli. • It should be determined by your hypothesis about the underlying cognitive processes. You can construct and test an arbitrarily complex model, if that model is justified by the brain processes. FMRI – Week 10 – Analysis II Scott Huettel, Duke University

4. They not only did analyses based on how much subjects won (top row), but also on how predictable was the subject’s decision (bottom row), which reflects how much reward the subject expected. Daw and colleagues (2006) used a “four-arm bandit” gambling task. In this task, subjects sometimes exploit a winning arm, and sometimes explore to learn about new arms. FMRI – Week 10 – Analysis II Scott Huettel, Duke University

5. Suppose that our experimental was based on the game show “Deal or No Deal”. How could we model the subject’s cognition? FMRI – Week 10 – Analysis II Scott Huettel, Duke University

6. Data-Driven Analyses • Broadly considered, they examine the data to identify coherent patterns. • Complement hypothesis-driven analyses (e.g., GLM) • The primary challenge: interpretation FMRI – Week 10 – Analysis II Scott Huettel, Duke University

7. Functional Connectivity Seed voxel in “b”. Colormap shows voxels with r > 0.35. Active Task Resting State! Biswal et al. (1995) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

8. Let’s just pick a voxel in the posterior cingulate and look at its connectivity. FMRI – Week 10 – Analysis II Scott Huettel, Duke University

9. PCC correlation is similar during active task and resting state. Resting-state connectivity (positive) for the posterior cingulate cortex (PCC, arrow). Resting-state connectivity (negative) for lateral prefrontal cortex. Greicius et al. (2003) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

10. Causality FMRI – Week 10 – Analysis II Scott Huettel, Duke University

11. Granger Causality “The basic "Granger Causality" definition is quite simple. Suppose that we have three terms, Xt, Yt, and Wt, and that we first attempt to forecast Xt+1 using past terms of Yt and Wt. We then try to forecast Xt+1 using past terms of Xt, Yt, and Wt. If the second forecast is found to be more successful, according to standard cost functions, then the past of Y appears to contain information helping in forecasting Xt+1 that is not in past Xt or Wt. … Thus, Yt would "Granger cause" Xt+1 if (a) Yt occurs before Xt+1 ; and (b) it contains information useful in forecasting Xt+1 that is not found in a group of other appropriate variables.” - Clive Granger, 2003 Nobel Laureate in Economics. FMRI – Week 10 – Analysis II Scott Huettel, Duke University

12. Konya (2006) Do changes in the exports of a country (Granger) cause changes in that country’s gross domestic product? That is, does export activity lead economic growth? FMRI – Week 10 – Analysis II Scott Huettel, Duke University

13. Simulated Activity (LFP) Influence Delayed by <100ms Fxy Fyx Fx,y FMRI gives information about correct causality (blue), but also introduces spurious simultaneous influence (red). Roebroeck et al. (2005) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

14. Red = Source Green = Inputs Blue = Targets Roebroeck et al. (2005) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

15. Intersubject Commonalities Hasson et al. (2004) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

16. Hasson et al. (2004) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

17. Hasson et al. (2004) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

18. The goal of ICA is to discover both the inputs and how they were mixed. Independent Components Analysis (ICA) Assumption: The observed data is the sum of a set of inputs which have been mixed together in an unknown fashion. McKeown, et al. (1998) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

19. Principal Components Analysis (PCA) finds a set of components that are uncorrelated. The first principal component gives the direction of maximal variance in the data. Value of Component 1 Value of Component 1 The assumption of temporal non-correlation can be violated by some forms of structure in the data. The assumption of spatial non-correlation is violated when a given voxel contributes to more than one process. FMRI – Week 10 – Analysis II Scott Huettel, Duke University

20. “The brain is not orthogonal!” Cf. Makeig, others. FMRI – Week 10 – Analysis II Scott Huettel, Duke University

21. Breathing Breathing Visual Cortex Heartbeat Head Motion? Vascular Oscillations? McKeown et al, (2003) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

22. MELODIC (FSL’s version of ICA) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

23. An Example MELODIC ICA output FMRI – Week 10 – Analysis II Scott Huettel, Duke University

24. Limitations of ICA • Cannot test hypotheses • Provides no criterion for significance • Relies on interpretations drawn by the researchers FMRI – Week 10 – Analysis II Scott Huettel, Duke University

25. Predicting Behavior and Thoughts FMRI – Week 10 – Analysis II Scott Huettel, Duke University

26. Brain Pong The specific method of training varies from subject to subject. "But it can be really different from one person to the next. We have one subject, a musician, who can vividly imagine the sight and sound of a concert, and that's a very specific brain region," says Sorger. If the musician wants the ping-pong bat to move up the screen, he adds more and more musicians to his mental orchestra, increasing the intensity of his vision to a crescendo. To move the bat back down the screen, he clears his mind of such thoughts until the bat rests at the base of the screen. It is just like visualizing a volume control, says Goebel. FMRI – Week 10 – Analysis II Scott Huettel, Duke University

27. Real-Time fMRI Caria et al., (2007) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

28. Pattern Classification Kamitani & Tong, (2006) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

29. Simple Correlations Goal: To determine whether there are category-specific spatial maps. Procedure: Look at brain response to category in odd runs, then see how well that pattern is replicated in even runs. Why is this a limited approach? Haxby et al., (2001) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

30. (Logistic) Regression for Behavior Knutson et al., (2007) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

31. Support Vector Machines (SVM) SVM approaches train on a (large) set of voxels to develop a multidimensional classifier that predicts behavior. Cox & Savoy (2003) Norman et al (2006) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

32. Key Steps of a Prediction Model • Voxel Selection: Whole brain or ROI? • Set aside part of your data • Train the classifier (on part of the data) • E.g., runs 1-5 (of 6) • Cross-validate the classifier • Test how well a classifier trained on 1-4 predicts 5 • Determine an optimal classifier • Pray • Test that classifier on the omitted data FMRI – Week 10 – Analysis II Scott Huettel, Duke University

33. Cox & Savoy (2003) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

34. Using a large number of voxels in a classifier provides potentially very good predictive power. The pattern of voxels provides much more information than ROI-based approaches. Cox & Savoy (2003) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

35. Looking in visual cortex, Kamitani and Tong could predict the direction of moving dots with great precision. Kamitani & Tong, (2006) FMRI – Week 10 – Analysis II Scott Huettel, Duke University

36. Haynes et al. (2007) looked at medial prefrontal activation while people were making a decision. They could predict, with >70% accuracy, what people would choose. FMRI – Week 10 – Analysis II Scott Huettel, Duke University

37. The pattern of voxel activation in just two brain regions (within the parietal cortex) can identify whether a person is making a decision under risk or under delay with>80% accuracy. Courtesy of John Clithero and McKell Carter FMRI – Week 10 – Analysis II Scott Huettel, Duke University

38. Data-Driven Analyses: Beautiful or Seductive? Non-Predictive Predictive Standard GLM-Based Approaches GLM with Blinding, Split-Samples, etc. Hypothesis-Driven Exploratory Data-Driven Approaches Predictive Data-Driven Approaches Data-Driven FMRI – Week 10 – Analysis II Scott Huettel, Duke University