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A probabilistic model of functional brain connectivity network for discovering novel biomarkers.

Bian J, Xie M, Topaloglu U, Cisler JM - AMIA Jt Summits Transl Sci Proc (2013)

Bottom Line: The evaluation results of the proposed strong-edge network model is quite promising.The biomarkers derived from the strong-edge model have achieved much higher prediction accuracy of 89% (ROCAUC: 0.96) in distinguishing depression subjects from healthy controls in comparison with the conventional network model (accuracy: 76%, ROC-AUC: 0.87).These novel biomarkers have the high potential of being applied clinically in diagnosing neurological and psychiatric brain diseases with noninvasive neuroimaging technologies.

View Article: PubMed Central - PubMed

Affiliation: University of Arkansas for Medical Sciences, Little Rock, AR;

ABSTRACT
Graph theoretical analyses of functional brain connectivity networks have been limited to a static view of brain activities over the entire timeseries. In this paper, we propose a new probabilistic model of the functional brain connectivity network, the strong-edge model, which incorporates the temporal fluctuation of neurodynamics. We also introduce a systematic approach to identifying biomarkers based on network characteristics that quantitatively describe the organization of the brain network. The evaluation results of the proposed strong-edge network model is quite promising. The biomarkers derived from the strong-edge model have achieved much higher prediction accuracy of 89% (ROCAUC: 0.96) in distinguishing depression subjects from healthy controls in comparison with the conventional network model (accuracy: 76%, ROC-AUC: 0.87). These novel biomarkers have the high potential of being applied clinically in diagnosing neurological and psychiatric brain diseases with noninvasive neuroimaging technologies.

No MeSH data available.


Related in: MedlinePlus

The mean accuracies and the ROC curves for classifiers using (a) the probabilistic strong-edge model (density = 0.76, n = 10, step = 30, and Sminsup = 0.8) V.S. (b) the conventional model.
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f2-amia_tbi_2013_021: The mean accuracies and the ROC curves for classifiers using (a) the probabilistic strong-edge model (density = 0.76, n = 10, step = 30, and Sminsup = 0.8) V.S. (b) the conventional model.

Mentions: To reduce bias, we bootstrap the SVM classifier 1000 times. During each iteration, we randomly split the samples into two datasets, 2/3 (26 subjects) used for training and the other 1/3 (12 subjects) left as test samples. For simplicity, we maintain the two datasets balanced (e.g., the testing dataset contains 6 positive subjects and 6 negative subjects). We then train an SVM model using only the training set (including the F-test feature selection process), and measure the classifier’s performance on the independent test samples. The final prediction accuracy and the ROC-AUC (a higher ROC-AUC value indicates the classifier has better sensitivity and specificity) value are averaged over all iterations. As shown in Figure 2, using the strong-edge model, the binary SVM classification of subjects with MDD yields a prediction accuracy of 89% and a ROC-AUC value of 0.96, while the classification model based on the conventional graph-theory approach yields a lower prediction accuracy of 76% and a ROC-AUC value of 0.87. Identifying and analyzing the features (i.e., network characteristics) that drive the multivariate predictor provides mechanistic information as to aspects of brain functional (dys)organization that characterize the depression state. The features derived from the strong-edge model that most strongly aided in group discrimination are the betweenness centrality of rPG, and the clustering coefficient of lSFG; while the best features learned from the conventional model are the betweenness centrality of rPG, lITG, and lSFG, the clustering coefficient of rPG, lITG, and rSFG, and the closeness centrality of rPC, lPC, rlTG, and llTG, indicating specific neural organizational patterns that differentiate MDD.


A probabilistic model of functional brain connectivity network for discovering novel biomarkers.

Bian J, Xie M, Topaloglu U, Cisler JM - AMIA Jt Summits Transl Sci Proc (2013)

The mean accuracies and the ROC curves for classifiers using (a) the probabilistic strong-edge model (density = 0.76, n = 10, step = 30, and Sminsup = 0.8) V.S. (b) the conventional model.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3814494&req=5

f2-amia_tbi_2013_021: The mean accuracies and the ROC curves for classifiers using (a) the probabilistic strong-edge model (density = 0.76, n = 10, step = 30, and Sminsup = 0.8) V.S. (b) the conventional model.
Mentions: To reduce bias, we bootstrap the SVM classifier 1000 times. During each iteration, we randomly split the samples into two datasets, 2/3 (26 subjects) used for training and the other 1/3 (12 subjects) left as test samples. For simplicity, we maintain the two datasets balanced (e.g., the testing dataset contains 6 positive subjects and 6 negative subjects). We then train an SVM model using only the training set (including the F-test feature selection process), and measure the classifier’s performance on the independent test samples. The final prediction accuracy and the ROC-AUC (a higher ROC-AUC value indicates the classifier has better sensitivity and specificity) value are averaged over all iterations. As shown in Figure 2, using the strong-edge model, the binary SVM classification of subjects with MDD yields a prediction accuracy of 89% and a ROC-AUC value of 0.96, while the classification model based on the conventional graph-theory approach yields a lower prediction accuracy of 76% and a ROC-AUC value of 0.87. Identifying and analyzing the features (i.e., network characteristics) that drive the multivariate predictor provides mechanistic information as to aspects of brain functional (dys)organization that characterize the depression state. The features derived from the strong-edge model that most strongly aided in group discrimination are the betweenness centrality of rPG, and the clustering coefficient of lSFG; while the best features learned from the conventional model are the betweenness centrality of rPG, lITG, and lSFG, the clustering coefficient of rPG, lITG, and rSFG, and the closeness centrality of rPC, lPC, rlTG, and llTG, indicating specific neural organizational patterns that differentiate MDD.

Bottom Line: The evaluation results of the proposed strong-edge network model is quite promising.The biomarkers derived from the strong-edge model have achieved much higher prediction accuracy of 89% (ROCAUC: 0.96) in distinguishing depression subjects from healthy controls in comparison with the conventional network model (accuracy: 76%, ROC-AUC: 0.87).These novel biomarkers have the high potential of being applied clinically in diagnosing neurological and psychiatric brain diseases with noninvasive neuroimaging technologies.

View Article: PubMed Central - PubMed

Affiliation: University of Arkansas for Medical Sciences, Little Rock, AR;

ABSTRACT
Graph theoretical analyses of functional brain connectivity networks have been limited to a static view of brain activities over the entire timeseries. In this paper, we propose a new probabilistic model of the functional brain connectivity network, the strong-edge model, which incorporates the temporal fluctuation of neurodynamics. We also introduce a systematic approach to identifying biomarkers based on network characteristics that quantitatively describe the organization of the brain network. The evaluation results of the proposed strong-edge network model is quite promising. The biomarkers derived from the strong-edge model have achieved much higher prediction accuracy of 89% (ROCAUC: 0.96) in distinguishing depression subjects from healthy controls in comparison with the conventional network model (accuracy: 76%, ROC-AUC: 0.87). These novel biomarkers have the high potential of being applied clinically in diagnosing neurological and psychiatric brain diseases with noninvasive neuroimaging technologies.

No MeSH data available.


Related in: MedlinePlus