Combining Graph and Machine Learning Methods to Analyze Differences in Functional Connectivity Across Sex

R Casanova*, 1 , C.T Whitlow2, B Wagner2, M.A Espeland1, J.A Maldjian2
1 Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
2 Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA

Article Metrics

CrossRef Citations:
Total Statistics:

Full-Text HTML Views: 1147
Abstract HTML Views: 625
PDF Downloads: 303
Total Views/Downloads: 2075
Unique Statistics:

Full-Text HTML Views: 449
Abstract HTML Views: 377
PDF Downloads: 222
Total Views/Downloads: 1048

Creative Commons License
© Casanova et al; Licensee Bentham Open

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Department of Biostatistical Sciences, Wake Forest University Health Sciences, Winston-Salem, NC, 27157 USA; Tel: 336- 716 8309; Fax: 336-716-6427; E-mail:


In this work we combine machine learning methods and graph theoretical analysis to investigate gender associated differences in resting state brain network connectivity. The set of all correlations computed from the fMRI resting state data is used as input features for classification. Two ensemble learning methods are used to perform the detection of the set of discriminative edges between groups (males vs. females) of brain networks: 1) Random Forest and 2) an ensemble method based on least angle shrinkage and selection operator (lasso) regressors. Permutation testing is used not only to assess significance of classification accuracy but also to evaluate significance of feature selection. Finally, these methods are applied to data downloaded from the Connectome Project website. Our results suggest that gender differences in brain function may be related to sexually dimorphic regional connectivity between specific critical nodes via gender-discriminative edges.

Keywords: Resting state fMRI, Machine learning, Graph theory, Regularization, GLMNET, Random forest.