Community Detection in Brain Networks

10/30/2015 1 Manas Gaur (SUNY Albany) Project Update

Manas Gaur

State University of New York, Albany

[email protected]

• Objective

• Data Statistics

• Network Construction

• Evaluation Plan

• Experimental Results

10/30/2015 Manas Gaur (SUNY Albany) Project Update 2

• 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.


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.


• 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.



Columns :140 time Frames

Row : 117 Brain Regions

Time : when 1st brain region was scan by

the MRI Scanner

• 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.


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


Correlation of 1

Community 1

Correlation of 1

Community 2

Negative Coorelation


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

Positive Communities


• 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.

• 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.



Probable communities using the p value < 0.05 Contours or clusters when the edges where not filtered


Large Connectivity Less Connectivity


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

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


Community Detection in Brain Networks

Data & Analytics

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