FMRI Data Analysis II Advanced Data Analysis FMRI
- Slides: 36
FMRI Data Analysis: II. Advanced Data Analysis FMRI Graduate Course (NBIO 381, PSY 362) Dr. Scott Huettel, Course Director FMRI – Week 10 – Analysis II Scott Huettel, Duke University
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
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
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. 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. FMRI – Week 10 – Analysis II Scott Huettel, Duke University
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
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
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
Let’s just pick a voxel in the posterior cingulate and look at its connectivity. FMRI – Week 10 – Analysis II Scott Huettel, Duke University
Resting-state connectivity (positive) for the posterior cingulate cortex (PCC, arrow). Resting-state connectivity (negative) for lateral prefrontal cortex. PCC correlation is similar during active task and resting state. FMRI – Week 10 – Analysis II Greicius et al. (2003) Scott Huettel, Duke University
Causality FMRI – Week 10 – Analysis II Scott Huettel, Duke University
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
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
Influence Delayed by <100 ms Simulated Activity (LFP) 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
Red = Source Green = Inputs Blue = Targets Roebroeck et al. (2005) FMRI – Week 10 – Analysis II Scott Huettel, Duke University
Intersubject Commonalities Hasson et al. (2004) FMRI – Week 10 – Analysis II Scott Huettel, Duke University
Hasson et al. (2004) FMRI – Week 10 – Analysis II Scott Huettel, Duke University
Hasson et al. (2004) FMRI – Week 10 – Analysis II Scott Huettel, Duke University
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. The goal of ICA is to discover both the inputs and how they were mixed. Mc. Keown, et al. (1998) FMRI – Week 10 – Analysis II Scott Huettel, Duke University
Value of Component 1 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 The assumption of temporal noncorrelation 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
“The brain is not orthogonal!” Cf. Makeig, others. FMRI – Week 10 – Analysis II Scott Huettel, Duke University
Visual Cortex Heartbeat Breathing Head Motion? Vascular Oscillations? Mc. Keown et al, (2003) FMRI – Week 10 – Analysis II Scott Huettel, Duke University
MELODIC (FSL’s version of ICA) FMRI – Week 10 – Analysis II Scott Huettel, Duke University
Examples of MELODIC ICA output A B C D FMRI – Week 10 – Analysis II Scott Huettel, Duke University
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
Predicting Behavior and Thoughts FMRI – Week 10 – Analysis II Scott Huettel, Duke University
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
Real-Time f. MRI Caria et al. , (2007) FMRI – Week 10 – Analysis II Scott Huettel, Duke University
Simple Correlations Goal: To determine whethere 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
(Logistic) Regression for Behavior Knutson et al. , (2007) FMRI – Week 10 – Analysis II Scott Huettel, Duke University
Key Steps of Pattern Classification • Feature Selection: Whole brain or ROI? • Training and Testing Sets – Set aside part of your data for a Final Test • Train the classifier on the Training Set – E. g. , runs 1 -5 (of 6) • Cross-validate the classifier on the Testing Set – Test how well a classifier trained on 1 -4 predicts 5 • Determine an optimal classifier • Pray • Test that classifier on the Final Test FMRI – Week 10 – Analysis II Scott Huettel, Duke University
Training Set … A B A A B B A Category Labels A B A A B B A … Time Points (T) Feature Selection Voxels (V) A Category B A A A B A B B B B A A A Voxel V 1 A B B B B Voxel V 2 Generalization FMRI – Week 10 – Analysis II A A B A A A B B A A B B A A A B B B Voxel V 1 Category A B B Voxel V 2 Testing Set Pattern Classification Scott Huettel, Duke University
Cox & Savoy (2003) FMRI – Week 10 – Analysis II Scott Huettel, Duke University
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
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
Time since intention onset (s) Soon et al. , 2007 FMRI – Week 10 – Analysis II Scott Huettel, Duke University
Data-Driven Analyses: Beautiful or Seductive? Data-Driven Hypothesis-Driven Non-Predictive FMRI – Week 10 – Analysis II Predictive Standard GLM with GLM-Based Blinding, Split. Approaches Samples, etc. Exploratory Predictive Data-Driven Approaches Scott Huettel, Duke University
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