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Capturing the Secret Dances in the Brain

Capturing the Secret Dances in the Brain. “Detecting current density vector coherent movement”. Cerebral Diagnosis. A problem proposed by:. The Brain. The most complex organ 85 % Water 100 billion nerve cells Signal speed may reach upto 429 km/hr. Neuronal Communication.

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Capturing the Secret Dances in the Brain

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

  2. Cerebral Diagnosis A problem proposed by:

  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

  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 • 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 Doing the same thing at the same time? Doing different things at the same dance? Dance in Unison???

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

  18. Problem B-First Layer Results

  19. Problem B-Second Layer Result Part I

  20. Problem B-Second Layer Result Part II

  21. Problem B: SVD Clustering

  22. Problem B: Dominique

  23. Problem B: Yousef

  24. Problem B: Yousef

  25. 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.

  26. Problem B: Nataliya

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

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

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

  30. 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

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