Lecture 8: Advanced Analysis in fMRI

Question 1

What are the different advanced analysis in fMRI? - (4)

Answer 1

• exploratory (intersubject correlation, independent component analysis)
• functional connectivity
• multivariate techniques (MVPA, pattern classification)
• voxel modelling

Question 2

Advanced analysis in fMRI is beyond simple yes-no hypothesis but towards more

Answer 2

open quesiton about “how the brain thinks”

Question 3

What is inter subject correlations? - (2)

Answer 3

Functional MRI time courses that are shared by different individuals while performing same experimental tasks or experiencing the same stimuli

looking at common pattern of activaitons across partiicpants

Question 4

In intersubject correlaiton they propose that

Answer 4

if a group of individuals all show the same activation, regardless of the experimental hypothesis, that shared activation likely reflects common mental processing.

Question 5

What does inter subject correlation process involve? - (2)

Answer 5

involves looking for regular patterns in the data and then interpreting those regularities based on knowledge of the experimental paradigm.

But unlike the approaches discussed elsewhere in the chapter, those regular patterns will be between subjects rather than within individual subjects.

Question 6

An example of intersubject correlation is

Answer 6

study by Hasson and colleagues

Question 7

What questions did Hasson et al 2004 wanted to ask - (3)

Answer 7

What happens in the brain when subjects watch a film?

What parts of the movie reliably correlate with increased BOLD signal (reverse correlation - what makes that BOLD signal fire in unconstrained way)? -

To what extent are these correlations regionally selective?

Question 8

Hasoon et al., 2004 had no - (2)

Answer 8

No model, very limited prior assumptions

Did not bring complex GLM and no idea experimental conditions started or finished as showing pps a movie clip

Question 9

What is Hassoon et al’s 2004 study interested in?

Answer 9

interested in understanding the brain processes that underlie perception in open-ended, natural settings, such
as when people freely perceive and attend to distinct parts of the complex,
changing world.

Question 10

What is the methodology of Hassoon et al’s 2004 study? - (4)

Answer 10

researchers showed five subjects a 30-minute excerpt from a classic western movie (Sergio Leone’s The Good, the Bad, and the Ugly).

Subjects were not instructed to perform any particular task while watching the movie, but to view it normally and describe the plot at the end of the experiment.

Then, using standard protocols for data collection, the researchers simply collected a time series of fMRI data while their subjects watched the movie.

For a control condition, they repeated the experiment with a separate group of
subjects who were lying in the scanner with their eyes closed,

Question 11

What did Hassoon et al’s 2004 study found? - basic findings - (5)

NON-SELECTIVE

Answer 11

They found significant across-subject correlations in about 30% of the cortical surface, including many parts of the visual cortex and the visual processing streams, as well as in regions within the frontal cortex in experimental condition

Large parts of cortex show correlated activity during the film across pps correlation

Within participants brain activity across large parts of brain is correlated with itself

For control condition resulted in only chance correlations between participants

These parts of brain doing something similiar during the movie

Question 12

What did Hassoon et al 2004 did in their data analysis? - (3)

Answer 12

The time course of the non-selective activity is similar in all subjects and across brain regions in the ventral occipital temporal cortex.

This pattern can be used as a regressor in a general linear model.

The unexplained (residual) variation can then be investigated for patterns of regional selectivity…

Question 13

What does this diagram show? - (5)

Answer 13

Five different participants and BOLD signal chance during the movie

Their peaks in similar places in different people

Overall average in red show consistent deviations from the average in experiment

Use red line as regressor and put in GLM and should see parts of the brain that is not explained by this regressor must be doing something different in each pp or each voxel

Question is whether those distinct things can be explained by non-selective activity and do they correlate between people? If they do then correlations telling about what part of brain is doing in two different people at same time

Question 14

What did they find after accounting for non-selective activity in large parts of cortex across different people Hassoon et al 2004? - (3)

Answer 14

Remaining residual activity that is coherent and correlated across different individuals (inter-subject correlation)

Having different time course for different regions – how do the regional time courses relate to function?

Strong voxels showing strong correlations between participants in a certain point of a movie

Question 15

What did Hassoon et al’s 2004 study found?

Intersubject correlations resulted from two different effects - (4)

Answer 15

First, activation throughout much of the brain,
including most visual regions, rose and fell in a similar pattern across all
subjects.

To interpret this activation, the researchers used a reverse correlation
approach: they identified time points when this collection of regions showed
maximum activation and then evaluated what was happening in the movie
at those times.

They found that this non-spatially-selective component tended to have highest amplitude during the most surprising and evocative points in the movie (e.g., gunshots, explosions, or unexpected plot twists); thus, it could reflect a broad increase in arousal.

Second, there were spatially selective in-
tersubject correlations that had a unique time course in each of several brain
regions.

Question 16

What does this figure show of Hasson et al 2004 study?

Answer 16

common activation in the fusiform gyrus tended to increase in response to movie scenes that involved a close view of a face,

Question 17

Essentially what Hassoon et al 2004 found that functional anatomy revealed that

Answer 17

some regions are specifically active during qualitatively different shots

Question 18

What did Hassoon et al 2004 finding some regions are specifically active during qualiatively different shots using reverse correaltion?

Answer 18

This technique of reverse correlation might have some validity for showing what is going on in brain without bringing specific experimental design and assumptions

Question 19

What is reverse correlation based on - (2)

Answer 19

Single-unit studies with monkeys

Monkey shown a lot of stimuli and researchers asking what makes that neuron fire?

Question 20

What does this diagram show of Hasoon et al 2004 study? - (3)

Answer 20

Peaks in activity in posterior collateral sulcus (adjacent to parahippocampal “place area”) correlate with interior scenes/landscapes.

It’s worth bearing in mind that we are only seeing the shots where these regions do respond

Some subjectivity in interpretation of what regions do since movie is not conrolled

Question 21

What does this diagram show of Hasoon et al 2004 study - (4)

Answer 21

Peaks in activity in the mid post-central sulcus in somatosensory cortex seem to correspond to scenes involving manipulation with their fingers.

A new finding? “Mirror neurons”?

As you are watching someone touching something in movie generating same neural activity if touching the same thing

Because of subjective element it is hard to be sure (had to put arrows)

Question 22

What is advantages of exploratory approaches (ISC)? - (3)

Answer 22

Few assumptions = not biasing what we can discover

Tasks less constrained by design considerations; allows more “ecologically valid” tasks

Rich data = every single voxel was scanned and telling us we have not guessed

Question 23

What is disadvantages of exploratory approaches like ISC? - (3)

Answer 23

Extraneous variables poorly controlled e.g., selective activity there is big face or explosion so not controlled , no attempt to separate diff influences on brain activity –> anything can be affected

Subjective element in interpreting/highlighting results

Requires analytical innovation = requires whole new data analysis to analyse natural viewing

Question 24

Typical fMRI data analyses (e.g., using FSL) are based on fitting a

Answer 24

complex GLM model to the data.

Question 25

In many studies the GLM model is complex and may include - (2)

Answer 25

untested and/or implicit) assumptions like linearity but way different tasks are fractioned and combined into different thought processes Forcing ourselves an answer we are expecting to see

Question 26

The implementation of the GLM in typical fMRI data analysis relies on many

Answer 26

assumptions about the way neural activity leads to a characteristic pattern of change in blood flow and how these changes is add up to produce the observed BOLD signal.

Question 27

Alternative models are not typically tested

Answer 27

against one another.

Question 28

Independent component analysis provides a way of doing a

Answer 28

“model-free” exploratory analysis

Question 29

ICA is more systematic exploratory approach than - (2)

Answer 29

ISC , has a certain toolbox but avoid fitting complex GLM model to data

Question 30

Independent componen analysis introduced by McKoewn assumes that - (2)

Answer 30

fMRI data consists of spatially overlapping components, each with independent spatial pattern and unique time course that is the components contribute differently to the overall four-dimensional time course at different points in time

Question 31

What does this diagram show of ICA?

Answer 31

Schematic example, thicker lines indicate when specific components contribute relatively heavily to specific time points

Question 32

What does the term ' independent' means in ICA? - (2)

Answer 32

algorithm minimizes the overlap between the components; Each ICA component is independent of all other components

Question 33

What does ICA offer?

Answer 33

a principled mathematical technique for identifying unknown and statistical independent signals (in a mixture)

Question 34

What does ICA look for?

Answer 34

signals that are statistically independent of one another --> one signal tells nothing about the other (low mutual information).

Question 35

Explaining ICA in terms of paints - (2):

Answer 35

If we had a collection of mixed brown paints and knew made by mixing some other paints Try out and work what colours went in mixing the brown

Question 37

What does ICA assumes

Answer 37

that some distinct signals have been mixed together to make the observed data

Question 38

In ICA, to find out what must have gone into mixtures, we reverse the process by

Answer 38

changing the mixing matrix until unknown sources are distinct as possible

Question 39

For a large number of datapoints (e.g., time series) in ICA, it uses mutual information which

Answer 39

measures how much knowing the value of x tells you about the corresponding value of y

Question 40

In ICA, the "sliders" in diagram are moved until the

Answer 40

mutual information between x and y (the putative sources) is minimized.

Question 41

The ICA algorithm takes its input the four dimensional fMRI data set (changes in intensity of all voxels over time) and exracts 2 things - (2)

Answer 41

1. set of n independent spatiotemporal patterns in original data (components) 2. Mixing matrix that charactercises the relative contribution of each of those patterns to t time points in original data

Question 42

What is mixing matrix?

Answer 42

A statistical description of how a set of hypothesised sources (e.g., components) combine to form the observed data

Question 43

The ICA algorithm identifies the combination of

Answer 43

components that explain the original data and tthat are maximally independent

Question 44

The ICA components correspond to

Answer 44

spatially distributed sets of voxels; same voxels can be a member of more than one component and voxel's overall time course of activation can be spilt among these components

Question 45

The components in ICA reflect

Answer 45

any systematic change in BOLD signal such as stimulus driven activation produced by internally generated rhoughts, physiological processes like head motion, respiration and other nusiance variation - figure below

Question 46

What does this diagram show? - (5)

Answer 46

Extracting task-related and non-task-related components using ICA. In this session, the subject viewed photographs of faces and of photographs of houses in alter- nating 40-s blocks. A series of components, four of which are shown here, were extracted using ICA. (A) Within voxels in the fusiform gyrus and the dorsal parietal cortex, there was a task-related modulation of activation. Other components identified by ICA were associated with non-task-related vari- ability, such as a transient scanner artifact (B), high-frequency physiological noise around vessels in the base of the brain (C), and head motion (D), which was evident as characteristicchanges in activation along the edge of the brain.

Question 47

The most common ICA is spatial ICA which emphasise

Answer 47

spatial independence by minimising redundancy in spatial maps of componenets

Question 48

Researchers can also conduct temporal ICA which

Answer 48

minimise the reduancy in the time course of the components

Question 49

ICA can also be used to remove components who temporal or spatial properities suggest

Answer 49

task unrelated noise

Question 50

Limitation of ICA is when it produces a single set of components for full participant sample is that

Answer 50

different participants' brains will have subtle differences in their cunctional organisaiton, a component extracted from full data set may not match well to components evident in any one participants' data

Question 51

Functional connectivity answers the question of to what extent

Answer 51

different parts of the brain are doing similar things at the same time

Question 52

Functional connectivity in other words is to what extent

Answer 52

different parts of the brain showing correlated changes in BOLD signal

Question 53

In functional connectivity,

Answer 53

You take the activation of a specific part of the brain (the seed region) and extract BOLD signal and use BOLD signal as regressor in GLM and reveal those part of brain that show similar pattern of activation in seed region

Question 54

Diagram of functional connectivity A set of regions where brain activity is active during rest - (2)

Answer 54

Default mode network which parts of brain where activity is correlated with each other and regions often active while participants don't have a task to do At rest variation in visual networks

Question 55

Functional connectivity is done instrinically meaning

Answer 55

done when participants is not doing any task

Question 56

Fascinating thing is when looking at pps brain at rest

Answer 56

different parts of brain will fluctature in activity in a spontaneous way but one tha captures relationship between different brain regions

Question 57

What is neurosynth? - (2)

Answer 57

Useful for functional connectivity and archives results from many studies Can look at areas you are interested in like hippocampus using coordinates

Question 58

What does this functional connectivity study by Smith et al 2009 show? - (4)

Answer 58

ICA yields similar components for resting state (left) and task-related (right) data sets. These components can be loosely associated with the distinct computations being undertaken in the task related dataset. When doing an experiment that explicility manipulates some cognitive process, find same subset of networks in operation than rest Showing different parts of brain don't act alone but act in concert - regions in back work together with regions in front

Question 59

With classic mass univarate analysis they compare

Answer 59

responses to conditions in each voxel independently

Question 60

With multi-variate pattern analysis (MVPA) it compares

Answer 60

patterns of responses of many voxels to different conditions

Question 61

MVPA in MEG it stands for.. but in fMRI

Answer 61

multivariate but sometimes in fMRI it stands for multi voxel

Question 62

What does this diagram of univarate analysis - show? - (2)

Answer 62

Estimate the signal change during condition A and estimate signal change during condition B and identify set of voxels where activation is significantly greater in condition A and B In order to calculate pattern of voxels more active in A than B , looking at each voxel in turn and do same for every voxel in brain

Question 63

What does this MVPA diagram show? - (3)

Answer 63

red areas more active on average than blue areas in different conditions in experiment Look at different patterns and compare with each other Use correlation to measure similarity of responses in different conditions in same part of brain in same set of voxels

Question 64

What does this diagram show in MVPA? - (10)

Answer 64

Participants viewing different categories of images like objects, faces,places Look at pattern of activity elicited in each task like top could be faces and bottom could be pattern of activity in place task We can compare patterns of activity both within and between categories Between = compare activity we seen during face condition to place condition and look whether those patterns correlate with each other Within = Look at 2 different sets of trials from face condition and compare those and see if they correlate They divide experiment and analyse pattern of activity in even and odd runs What is typically found is if we look at same category stimuli (within) , there is positive correlation as same response to same stimuli In between with face vs place we see negative correlation as pattern of response different to different stimulus type Not looking at overall activity (represented by number) but correlation between 2 diff patterns and their similarity But we can use MVPA to find out what the categories are (instead of defining above) so look at particular part of brain and say can brain tell diff between two diff tasks

Question 65

One of the first studies that introduced idea of MVPA is by

Answer 65

Haxby et al., (2001)

Question 66

already known that FFA responds to faces and PPA responds to places But Haxby et al., (2001) wanted to investigate - (3)

Answer 66

Is that the whole story that special individual parts of brain are distinct If we ignore these brain regions and looked elsewhere would we still find that patterns of actiity across brain care about those stimulus types Looking whether isolated or modulated or part of bigger pattern --> distributed representations took place over whole brain

Question 67

Haxby et al 2001 methods for faces and houses and results - (7)

Answer 67

Had different classes of stimuli like faces, places, cats, scissors, chairs Looked at pattern similarity For every class of stimulus they compare within and between category responses in terms of pattern of activity they elicted (not overall activity but similarity between patterns) e.g., faces look similarity in responses to 2 different sets of faces r= 0.81 or look at correlation between set of faces and houses which is negative ( r = - 0.40) Pattern more similar within category than between as strong positive correlation with faces and modest negative correlation with face and houses as well as with other categories of stimuli over whole area of occipital temporal cortex even when FFA is removed Similar pattern for houses over whole area even when PPA is removed Suggests that not accurate to think of modular selective regions

Question 68

What did Haxby et al 2001 suggest at the end of study? - (2)

Answer 68

There is object form tography that whole bottom of ventral surface is organised in tographical way So that different categories are slightly diff places but distributed representation that tells us what we are looking at

Question 69

What did Haxby and colleagues show with cats and faces?

Answer 69

Cats are moderately correlated with faces (between)

Question 70

What does this diagram show in MVPA? - (4)

Answer 70

If we consider many exp conditions We can create a matrix between different pair of experimental conditions and how brain processes different tasks and if it processes similarliy or differently Looking at pattern of activation greater in one condition than other condition F and B more correlated than condition F and A so brain may doing something similar in condition F and B almost same tasks

Question 71

What is representational similarity analysis? - (4)

Answer 71

Ask participant to experience different stimuli or tasks like responses to faces, umbrellas etc.. Look at pattern of activation in set of voxels (in table) and those patterns will have a pattern of similarity/disimilarity Tend to calculate disimilarity tend to be 1 - correlation So for any pair of stimulus condition think about overall similarity of patterns in the different conditions which allows to produce matrix across diff task conditions and use matrix to decide what the brain is doing in different tasks with various models/explanations

Question 72

In RSA, can create many matrices such as - (5)

Answer 72

Matrix based on similarity of behavioural responses in given task as how long RT to sort faces vs objects Does behavioural matrix similar to brain matrix as if it does then maybe behaviour is being driven by this part of brain that has similar RSA Do same with computational model and think whether this part of brain is face detector which would respond similar way to different faces and respond disimilar to other categories Or look at whether pattern of brain activity could be explained in characteristics of stimuli like low level like how similar images are in underlying pixels and another matrix Which explanation fits best the pattern of similarity we are seeing in experiment

Question 73

In RSA we can use to test models of how we think

Answer 73

the brain works

Question 74

Is pattern classification (brain reading) a multivariate technique?

Answer 74

Yes

Question 75

Is RSA multivariate technique?

Answer 75

Yes

Question 76

What does this diagram show in pattern classification? - (10)

Answer 76

researchers want to identify voxels whose activation predicts whether the subject is looking at photographs of animals or plants. At the first stage, feature selection, the researchers identify a subset of voxels for subsequent analyses. A typical feature set consists of the activation intensity for each voxel on each trial. The feature set splits into a training set, from which the pattern classifier will be derived, and a testing set that provides a novel test of the generalization of the classifier. Shown here is a simplified example of pattern classification using two features (i.e., two voxels) and two trial categories (A and B). The activation values of those two voxels on each trial are shown as a two-dimensional plot. Note that fMRI pattern classification involves many more dimensions and thus a much higher-dimensional space. In the common technique of support vector machines, the pattern classification algorithm attempts to identify the surface that maximally distinguishes the two categories. Here, a linear classifier optimally sepa- rates the two stimulus categories. Once a classifier has been identified, it is tested on the novel training set to ensure that the classification rule can be generalized to untested data.

Question 77

Example of pattern classification in Haynes et al 2007 - (4)

Answer 77

Has 2 phases of word 'select' and after certain period of time participants given 2 numbers and have to decide to add or subtract the new numbers in green phase Then see 2 numbers and add/subtract and then after a period of time see 4 answers and press button which one is right one A computer is trained to recognize patterns of activity that predict whether participants will add or subtract (Haynes et al., Current Biology, 2007). Using activity in the green areas predictions are up to ~70% accurate --> used search light technique by looking at chunk of brain

Question 78

What is voxel modelling? - (5)

Answer 78

Another advanced technique of what is happening in brain and trying to use that to predict and decode what patterns of activity mean (similar to pattern classification but different) but works on individual voxels Carry data of pps looking at picture for long time and produce diff brain activity Using pattern of activity we have time courses that we can attempt to explain by modelling diff characteristics of images they are looking at So for any given voxel say if that pattern of activity in voxel responding to activity to loads of different properities like responding to at top side of screen or to top left? Was it responding to surfaces? Semantics to movies? We can create a model and estimate how model works and get best fitting model so present picture able to predict pattern of activity in each voxel Collect second data from pps looking at diff model and using model we produced and generating predictions of pattern of activity in each voxel

Question 79

Example of study using voxel modelling - Gallant - (3)

Answer 79

Showing movie to pps Then different movie to pps When the participant watches a new video, the model decodes what they are seeing by finding video clips that would produce similar patterns of activity and combining them.

Question 80

Which of the following options gives one advantage and one disadvantage of exploratory and data-driven approaches (when compared to typical hypothesis-driven experiments employing e.g., cognitive subtraction): A. fewer assumptions, less control over task/stimulus B. potentially greater ecological validity, more assumptions C. objective interpretation, more assumptions D objective interpretation, potentially reduced ecological validity

Answer 80

A

Question 81

Independent Component Analysis allows a principled “model-free” approach to fMRI analysis, in which statistically independent spatial patterns of activation are identified. Which of the following statements is FALSE with respect to ICA: A. Task-related activity can be detected B. Motion-related artefacts can be detected C. The researcher must provide explanatory variables corresponding to task manipulations D. Components in the resting-state are similar to those seen in task-fMRI

Answer 81

C - ICA is model free

Question 82

Haxby et al (2001) introduced a simple form of Multi-Voxel Pattern Analysis which used correlation to assess the: A. average BOLD signal change in each distinct task B. similarity of spatial patterns of response to different stimuli C. statistical relationships between timeseries in different brain regions D. voxel-level responses to specific task stimulus features

Answer 82

B

Question 83

Which statement best describes a step in a “brain reading” analysis (e.g., used by Haynes et al., 2007) to predict whether a participant would add or subtract a pair of numbers based on activity observed prior to the decision: A. A pattern classifier is trained to recognize patterns of activity associated with different conditions B. The similarity of responses to different conditions is used to construct a matrix C The response of each voxel is modelled D Voxels outside the most category-selective regions show correlated patterns of activity when processing stimuli from the same category

Answer 83

A