1. Capturing the Secret Dances in the Brain
“Detecting current density vector coherent movement”

2. A problem proposed by:
Cerebral Diagnosis

3. The Brain The most complex organ 85 % Water 100 billion nerve cells
Signal speed may reach upto 429 km/hr

4. Neuronal Communication
Neurons communicate using electrical and chemical signals
Ions allow these signals to form

5. Brain Imaging Techniques
EEG
MEG
fMRI

6. Electroencephalogram
Electrodes on scalp measure these voltages
An EEG outputs the voltage and the locations

7. EEG of a Vertex wave from Stage I sleep
Voltage
time 7

8. Inverse Problem Solving using eLoreta
The EEG collects the amplitudes
Inverse Problem Solving allows the computation of an electrical field vector
Output is current density vectors at voxels

9. Problems
Goal: to capture certain behaviour common to groups of vectors
Problem A:
Classify the vectors according to orientations and spatial positions
Problem B:
Classify the vectors that dance in unison

10. Problem A Input: Top 5% of Activity
Classify the vectors according to orientations and spatial positions
Input: Top 5% of Activity
Normalize the data onto a unit sphere
Classification
Output: Clusters

11. Classification
Initialization: Statistical algorithm to group into 4 clusters as suggested by the data.
Refinement: Partition each cluster into subsets of spatially related voxels via
where x and y are physical coordinates of a pair of voxels.

12. Problem A-Nataliya
Next step: Refinement of clusters based on orientation.
pairwise
inner product < i, j > 5 5 2 6 2 6 4 1 4 1 3 3
Separation criterion: inner product >tol (e.g., tol=0.8).

13. Problem A-Two Layer Classification
First, classify the voxels in connected spatial neighborhoods
Second, refine each neighborhood according to orientations

14. Problem A-Two Layer Classification

15. Problem B
Classify the vectors that dance in unison

16. Problem B Dance in Unison??? Doing the same thing at the same time?
Doing different things at the same dance?

17. Problem B
Algorithm 1
Spatial proximity, similar orientation, similar velocity
Same two-layer classification algorithm!
Critera for refining spatial clusters : orientation, velocity

18. Problem B-First Layer Results

19. Problem B-Second Layer Result Part I

20. Problem B-Second Layer Result Part II

22. Problem B: SVD Clustering

23. Problem B: Dominique

24. Problem B: Yousef

25. Problem B: Yousef

26. Problem B
The proposed distance that determines current density vectors dancing in unison is the inner product of normalized differences
diffi
diffj i j
n time frames
The clustered vectors move along relatively the same trajectory with variation controlled by a user defined tolerance parameter.

27. Problem B: Nataliya

28. Problem B: Varvara (Clustering Using Cosine Similarity Measure)

29. Problem B: Varvara (Clustering Using Cosine Similarity Measure)
Member of a cluster
End
Compute Cosine for any two consecutive times for each voxel
Input-Data
Test condition 1
Test condition m
Dancing in unison means

30. Problem B: Varvara (Clustering Using Cosine Similarity Measure)

31. Conclusions:
In this project we tried to observe whether or not any pattern exists in the CDVs data at a fixed time, and over a time interval.
During this very short period of time we were able to solve the two problems in more than one way.
Data whose magnitudes are more that 95% of the maximum magnitudes in the given range were observed.
Next step: validation with other random data, refine models that already work