a probabilistic model of functional

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A PROBABILISTIC MODEL OF FUNCTIONALBRAIN CONNECTIVITY NETWORK FOR

DISCOVERING NOVEL BIOMARKERS

Jiang Bian1, Mengjun Xie2, Umit Topaloglu1 and Josh Cisler3

1Division of Biomedical Informatics3Brain Image Research Center

University of Arkansas for Medical Sciences

2Computer ScienceUniversity of Arkansas at Little Rock

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OutlineBrain connectivity networks and real-world complex networks

A probabilistic model of the human brain connectom

from zero and one to a probabilistic model

using network characteristics as potential biomarkers

Evaluation and results

Discussion and future work

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brain connectivity networksBrain can be modeled as a massive parallel computing network.

Three types of brain connectivity networks:

structure network, constrained by physical structure of thebrain (MRI, DTI)

functional network, coherence or correlation between brainregions (resting-state fMRI/MEG)

effective network, covariance modeling, Granger causality(tasked-based EEG/fMRI)

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complex networksMany real-world complexnetworks including the brain

networks have similartopological features:

small-world: high clusteringcoefficient, but relatively low

characteristic path length(Watts and Strogatz, 1998)

scale-free: highly connectedhubs (Barabsi and Albert, 1999)

an image of the Internet from the Internet...

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functional brain connectivity

images from the internet...

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functional brain networksfMRI data gives a set oftimecourses of the BOLDsignals of each voxel (e.g.,

3mm3) within the gray matter

Through computing thecovariance of two voxelstimecourses, we can infer

whether the two are functionallyconnected

Typically, neuroscientists workwith ROIs (a collection of voxels)

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a probabilistic modelExisting connectivity studies assume temporal stabilityof twoROIs, as they compute the correlation coefficient using the entire

timecoursesWe need to consider the temporal fluctuations of theconnectivities between ROIs, as hinted by recent neurosciencestudies (Stamoulis et. al., 2010 and Whitlow et. al., 2011)

Therefore, we propose to consider the frequency of functionalconnections between brain regions over time and regard thefrequentconnections as strong and important to theoperation of the overall brain network

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a probabilistic model1 0 1 1 0 0 1 0

s1-2 = 4/8 = 0.5{ se(i,j) = |e(i,j)|/|TW|

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conventional vs. probabilistic

a) conventional graph

b) strong-edge graphfrequency graph

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network characteristics as

biomarkersA number of studies (e.g., Calhoun et. al., 2008, Craddock et. al., 2009) haveshown that neural networkorganization patterns can be used

to differentiate disease states.Network characteristics and the topology of a network can bequantitatively measured as clustering coefficient, node

betweenness centrality, characteristic path length, etc.

Why not build aprediction model using these network featuresto tell whether I have xyz?

Bonus: the probabilistic model vs. the conventional method

+

=

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evaluation methodsWe study the subjects of Major Depression Disorder (MDD)

19 health women with no MDD history in the controlgroup; 19 women with

MDD (6 with current MDD, 13 with MDD history) as the diseasedgroup;

MDD was characterized with the Structured Clinical Interview for DSM-IVAxis I Disorders (SCID).

Both groups underwent 7.2 mins resting-state fMRI scan.

We selected 11 ROIs of the default mode network (DMN) based on previousneuroscience studies.

PCC, rPG, lPG, SCC, rPC, lPC, rITG, lITG, rSFG, lSFG, MPC

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network measuresFor each subject's brain connectivity network, we compute thefollowing network indices:

degree (or strength in a weighted graph),

betweenness centrality,

closeness centrality,

local and global clustering coefficient, and

global characteristic path length.

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SVM classifiersWe use the network features to build Support Vector Machines(SVMs) for both the conventional and ourprobabilistic model.

Standard SVM techniques have been considered: scaling, grid-basedkernel parameter search and feature selection using F-test.

Evaluation methods:

Bootstrap the sample 1000 times and during each iteration: 2/3 isused for training, and the rest1/3 is kept for testing.

We measure the prediction accuracy and the ROC area under thecurve (AUC) at each iteration and take the average as the final result.

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results

a) the conventional model

(Accuracy: 76%, AUC: 0.87)

b) the strong-edge model

(Accuracy: 89%, AUC: 0.96)

vs.

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discussionA number of free variables can influence the accuracy of brain network models andconsequently affect the classifiers' performance.

In the conventional method:

the choice of the correlation coefficient threshold Tthat determines whether we shalldraw an edge between two voxels (ROIs);

determined by the targeted density, and we choose (0.37 ~ 0.5) followed existingstudies.

In the strong-edge (probabilistic) model:

the choice of the densityvalue is relaxed and we can choose a high density value (0.76);

however, we introduce the Sminsup that determines the cut-off frequency for defining anedge to be strong (less importantfor the classifier)

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discussion and future workA number of biomarkers are identified, e.g., the betweenness centrality of rPG; and theclustering coefficient of lSFG.

These biomarkers have plausible explanations within the context of neuroscience.

e.g., Additionally, the link between the left superior frontal gyrus and the right insula isalso reduced... (Tao et al. 2011, Nature)

Future work:

Test the theory on other brain diseases (preliminary results on a drug-addict dataset are

promising).

Can we work on the voxel-level data?

Pros: No expert needed to pick a ROI-based neural network

Cons: Computationally expensive.

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Q & [email protected]
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a probabilistic model of functional

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