Information based-analysis - leBIC-AMC

1

Cognitive Neuroscience Group

Titel Auteur

Information based-analysis

NeuroImaging

H.Steven Scholte

<

2

Activation based-analysis

• Overall activity of functional regions.

• Spatially smooth part of the signal is considered signal,

patchy part of the signal is considered noise.

• Data is smoothed with a kernel of 4-8 mm (for registration

and multiple comparison reduction).

• Voxels from a region of interest are averaged together.

• In effect, were are looking at activations with a relatively

large area of extend and are not interested in fine grained

activation.

Information based analysis

Classification

Correlation analysis

4

Information based-analysis: look at patterns

Is this a house or a face

5

Is this a house or a face

Is this a house or a face: use correlations

6

Information based-analysis vs Activity based analysis

1 2

3 4

5 6

7 8

9 10

Compare even runs with odd runs

7

Haxby et al., 2001

Haxby et al., 2001

8

Haxby et al., 2001

Kamatari & Tong, 2005

9



10

Op De Beeck, 2009

Freeman et al., 2011

11

Information analysis

Is multivariate analysis of data within an area

Looks at patterns of activation vs. activity between

conditions.

Probably reflects the functional organization of an area in

responding to a stimulus.


Classification


12

N-1 testing

test house

train house

test face

train face

Test house == house

Test face == face

Both correct

N-1 testing

test house

train house

test face

train face

Test house == face

Test face == face

One error

13

N-1 testing

test house

train house

test face

train face

Test house == house

Test face == face

Both correct

Support Vector Machine

Will be discussed in detail the next hour.

14

- In the design many exemplars of the same stimuli and trials are

presented.

- Perform a single trial analysis and isolate activity from a region

of interest (usually only does voxels that show a significant

loading from a f-test perspective).

- Use N-1 training and testing to get classifications.

- 20 trials classified. Lets say 4 errors. Percentage correct = 16/20

= 80%.

- Subjects are registered to each other on the basis of results (e.g.

the PPA of subject 1 - 0.8, the PPA of subject 2 = 0.75, the PPA

does 0.775).

Using neural networks and SVM


Classification


15

• Patterns of activation are

investigated on the basis of

their dissimilarity matrix.

Kriegeskorte et al., 2007

16



17

Tracking temporal changes

18

Data-driven clustering

In data-driven clustering the

elements are placed in such a

way in a 2D matrix that there

distance is minimized.


19

Human Monkey

First-order isomorphism

Second-order isomorphism

20

Conclusions

Information based-analysis - leBIC-AMC

Documents

Transcript of Information based-analysis - leBIC-AMC