fmri – week 10 – analysis ii scott huettel, duke university fmri data analysis: ii. advanced...

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

FMRI Data Analysis:II. Advanced Data Analysis

FMRI Undergraduate Course (PSY 181F) FMRI Graduate Course (NBIO 381, PSY

362)

Dr. Scott Huettel, Course Director


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


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.


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.


Suppose that our experimental was based on the game show

“Deal or No Deal”.

How could we model the subject’s cognition?


Data-Driven Analyses

• Broadly considered, they examine the data to identify coherent patterns.

• Complement hypothesis-driven analyses (e.g., GLM)

• The primary challenge: interpretation


Functional Connectivity

Seed voxel in “b”. Colormap shows

voxels with r > 0.35.

Biswal et al. (1995)

Active Task Resting State!


Let’s just pick a voxel in the posterior cingulate and look at its connectivity.


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. Greicius et al. (2003)


Causality


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.


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?

Konya (2006)


Roebroeck et al. (2005)

Simulated Activity (LFP)

Fxy

Fyx

Fx,y

Influence Delayed by

<100ms

FMRI gives information about correct causality

(blue), but also introduces spurious

simultaneous influence (red).


Roebroeck et al. (2005)

Red = Source

Green = Inputs

Blue = Targets


Intersubject Commonalities

Hasson et al. (2004)


Hasson et al. (2004)


Independent Components Analysis (ICA)

McKeown, et al. (1998)

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.


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

Valu

e o

f C

om

pon

en

t 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.


“The brain is not orthogonal!”

Cf. Makeig, others.


McKeown et al, (2003)

Visual Cortex

Heartbeat

Breathing

Breathing

Head Motion?

Vascular Oscillations?


MELODIC (FSL’s version of ICA)


An Example MELODIC ICA output


Limitations of ICA

• Cannot test hypotheses• Provides no criterion for significance• Relies on interpretations drawn by

the researchers


Predicting Behavior and Thoughts

https://subscribe.hearstmags.com/subscribe/splits/popularmechanics/pop_site_ur_home


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.

Brain Pong


Caria et al., (2007)

Real-Time fMRI


Kamitani & Tong, (2006)

Pattern Classification


Simple Correlations

Haxby et al., (2001)

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?


(Logistic) Regression for Behavior

Knutson et al., (2007)


Support Vector Machines (SVM)

Cox & Savoy (2003)

Norman et al (2006)

SVM approaches train on a (large) set of voxels to

develop a multidimensional classifier that predicts

behavior.


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


Cox & Savoy (2003)


Cox & Savoy (2003)

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.


Kamitani & Tong, (2006)

Looking in visual cortex, Kamitani and Tong could predict the direction of moving dots with great precision.


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.


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


Data-Driven Analyses: Beautiful or Seductive?

Standard GLM-Based

Approaches

GLM with Blinding,

Split-Samples,

etc.

Exploratory Data-Driven

Approaches

Predictive Data-Driven

Approaches

Data

-Dri

ven

Hyp

oth

esis

-Dri

ven Non-Predictive Predictive

fmri – week 10 – analysis ii scott huettel, duke university fmri data analysis: ii. advanced...

Documents

past xt

forecasting xt

past terms of xt

granger cause xt

fmri data analysis

observed data

past terms of yt

datadriven analysesbroadly