Capturing the Secret Dances in the Brain

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Capturing the Secret Dances in the Brain

• Detecting current density vector coherent
  movement

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Cerebral Diagnosis

• A problem proposed by

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The Brain

• The most complex organ
• 85 Water
• 100 billion nerve cells
• Signal speed may reach upto 429 km/hr

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Neuronal Communication

• Neurons communicate using electrical and chemical
  signals
• Ions allow these signals to form

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Brain Imaging Techniques
EEG
MEG
fMRI
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Electroencephalogram

• Electrodes on scalp measure these voltages
• An EEG outputs the voltage and the locations

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EEG of a Vertex wave from Stage I sleep
Voltage
time
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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

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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

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Problem A
Classify the vectors according to orientations
and spatial positions
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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.

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Problem A-Nataliya
Next step Refinement of clusters based on
orientation.
pairwise
inner product lt i, j gt
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5
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2
6
1
4
4
1
3
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Separation criterion inner product gttol (e.g.,
tol0.8).
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Problem A-Two Layer Classification

• First, classify the voxels in connected spatial
  neighborhoods
• Second, refine each neighborhood according to
  orientations

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Problem A-Two Layer Classification
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Problem B

• Classify the vectors that dance in unison

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Problem B

• Dance in Unison???

Doing the same thing at the same time? Doing
different things at the same dance?
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Problem B

• Algorithm 1

• Spatial proximity, similar orientation, similar
  velocity
• Same two-layer classification algorithm!
• Critera for refining spatial clusters
  orientation, velocity

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Problem B-First Layer Results
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Problem B-Second Layer Result Part I
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Problem B-Second Layer Result Part II
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Problem B SVD Clustering
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Problem B Dominique
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Problem B Yousef
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Problem B Yousef
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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.
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Problem B Nataliya
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Problem B Varvara (Clustering Using Cosine
Similarity Measure)
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Problem B Varvara (Clustering Using Cosine
Similarity Measure)
Dancing in unison means
 
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Problem B Varvara (Clustering Using Cosine
Similarity Measure)
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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