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Author Notes:

E-mail: xhu@bme.gatech.edu

Conceived and designed the experiments: GD CC XH.

Performed the experiments: PS MEL SH. Analyzed the data: GD ZL PS.

Contributed reagents/materials/analysis tools: GD.

Wrote the paper: GD ZL CC XH. Performed data pre-processing: ZL PS.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

The authors have declared that no competing interests exist.


Research Funding:

The authors acknowledge the support from National Institutes of Health (R01 EB002009 to XH) and Georgia Research Alliance.


  • Science & Technology
  • Multidisciplinary Sciences
  • Science & Technology - Other Topics
  • MRI

Recursive Cluster Elimination Based Support Vector Machine for Disease State Prediction Using Resting State Functional and Effective Brain Connectivity


Journal Title:



Volume 5, Number 12


, Pages e14277-e14277

Type of Work:

Article | Final Publisher PDF


Background: Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support vector machines (SVM) and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification. Methodology/Principal Findings: In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC) in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE) algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects. Conclusions/Significance: The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state classification is used for disease state prediction, our approach may aid the clinicians in performing more accurate diagnosis of diseases in situations where in non-neuroimaging biomarkers may be unable to perform differential diagnosis with c ertainty.

Copyright information:

© 2010 Deshpande et al.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 3.0 Unported License (http://creativecommons.org/licenses/by/3.0/).

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