capturing the secret dances in the brain
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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
• Neurons communicate using electrical and chemical signals
• Ions allow these signals to form
Brain Imaging Techniques
EEG MEG fMRI
Electroencephalogram
•Electrodes on scalp measure these voltages
•An EEG outputs the voltage and the locations
EEG of a Vertex wave from Stage I sleep
time
Voltage
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
Problems
• Problem A:– Classify the vectors according to orientations and
spatial positions
• Problem B:– Classify the vectors that dance in unison
Goal: to capture certain behaviour common to groups of vectors
Problem AClassify the vectors according to orientations and spatial positions
Input: Top 5% of Activity
Normalize the data onto a unit sphere
Classification
Output: Clusters
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.
x yL max x1 y1 , x2 y2 , x3 y3 n, (e.g.,n 5)
Problem A-NataliyaNext step: Refinement of clusters based on orientation. pairwise inner product < i, j >
12
3
4
56
25
6
3
1 4
Separation criterion: inner product >tol (e.g., tol=0.8).
Problem A-Two Layer Classification
• First, classify the voxels in connected spatial neighborhoods
• Second, refine each neighborhood according to orientations
Problem A-Two Layer Classification
Problem B• Classify the vectors that dance in unison
Dance in Unison???
Problem B
Doing the same thing at the same time?Doing different things at the same dance?
Algorithm 1
Problem B
• Spatial proximity, similar orientation, similar velocity
• Same two-layer classification algorithm!
• Critera for refining spatial clusters : orientation, velocity
Problem B-First Layer Results
Problem B-Second Layer Result Part I
Problem B-Second Layer Result Part II
Problem B: SVD Clustering
Problem B: Dominique
Problem B: Yousef
Problem B: Yousef
Problem B
ii
j
r J i t1
r J j t2
r J j t1
r J i t2
diff i ,diff j , diff i (r J i t2
r J i t1
), diff j (r J j t2
r J j t1
).
diffi
diffj
t1
tn
n time framesThe clustered vectors move along relatively the same trajectory with variation controlled by a user defined tolerance parameter.
Problem B: Nataliya
Problem B: Varvara (Clustering Using Cosine Similarity Measure)
v
Member of a
cluster
End
Compute Cosine for any two consecutive times for each voxel
Input-Data
Test condition
1
Test condition
m
Member of a cluster
Problem B: Varvara (Clustering Using Cosine Similarity Measure)
Dancing in unison means
-4
-2
0
2
4 1.1
1.2
1.3
1.4
1.5
1.6
2
2.5
3
3.5
4
Elevation Theta
Current Density Vectors Activity Over Time
Azimuth Phi
Mag
nitu
de r
Problem B: Varvara (Clustering Using Cosine Similarity Measure)
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