This document provides a project update on analyzing resting fMRI time series data to identify time-evolving brain communities and clusters between normal and patient groups. It discusses applying maximum overlap discrete wavelet transform to the 117x140 time series data matrix to generate a 117x117 correlation matrix. Different experimental conditions are proposed to evaluate clusters, including sampling frequency, correlation threshold, and centrality measures. Preliminary results show 7 small connected communities identified after sampling, making time-evolving clusters easier to assess than from a single large community.

1. 10/30/2015 1Manas Gaur (SUNY Albany) Project Update
Manas Gaur
State University of New York, Albany
mgaur@albany.edu

2. • Objective
• Data Statistics
• Network Construction
• Evaluation Plan
• Experimental Results
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3. • Among all the communities in the sparse
correlation coefficient graph, generated from
time series data, identify time evolving
communities or clusters from patient to
normal person.
• Visualize the sparse graph depicting
interaction between different brain regions
when comparing normal person and patient.
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4. Data Statistics
• The data involved is a processed time series
data of resting FMRI images.
• The data is used in MATLAB is stored in a
structure of filename and val.
• The “filename” is the Resting FMRI image in
NIfTI format and the “val” is the time series
matrix.
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5. • Each resting FMRI time series data is specified
in a matrix of the order 140 X 117.
• 140 are the number of scans of a single
region of the brain and 117 are number
regions of the brain.
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Columns :140 time Frames
Row : 117
Brain Regions
Time : when 1st brain
region was scan by
the MRI Scanner

7. • Given the time series data {117X140}, we sample each time
scan with sampling frequency 1/10. { part 1 Evaluation plan}
• Apply Maximum overlap Discrete Wavelet transform and
generate 117 X 117 correlation matrix.
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Time Series Data
117 X 140
Apply MODWT
Correlation Matrix :
row (brain region) and
column(brain region)
Sampling the time per
scan per region with
sampling period 1/10
The Flow of work has already been done
Proposed work

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Correlation
of 1
Community
1
Correlation
of 1
Community
2
Negative
Coorelation

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X and Y are Brain region and The
Graph shows 7 communities formed (
contours after edges were filtered) Negative Communities
Positive Communities

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• Seeing the figure, its intuitive that sampling the time scan with a
sampling frequency 1/10 has generated 7 small connected
components or communities.
• Its easier to assess the time evolving clusters from normal
person to patient in these small communities than a single large
community.
• Comparison between different clusters or brain networks can be
done using Minimum Spanning Tree as it does not involve all the
connections of the network but it still provide similar
information about the network topology.
• In our experiment we are considering 5 different conditions and
also evaluating the different clusters based on the p value.
• We plot a graph with y axis showing the p values of the clusters
in the graph and x axis shows the clusters in the graph.

11. • Different experimental conditions that will be taken
into considerations are
• Scale Factor in MODWT
• Sampling Frequency of the time per scan per region.
• If we alter the correlation threshold over infinitesimal
small range.
• Use of different correlation function (Kendall, Tau) or
betweenness measure .
• Minimum Spanning Tree and All shortest path
measures.
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Probable communities using the p value < 0.05 Contours or clusters when the edges
where not filtered

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Large Connectivity Less Connectivity

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a.)
b.)
Minimum spanning Trees a.) after sampling (117X35000) b.) original data (117 X 117)

15. • There is no information in the dataset about
which FMRI time series data is of a normal
person and which is of a patient.
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