Sparse network-based models for patient classification using fMRI

Proceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013

Volumn , Issue , 2013, Pages 66-69

  Rosa, Maria J ^a   Portugal, Liana ^a,b   Shawe Taylor, John ^a   Mourao Miranda, Janaina ^a,c  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b FLUMINENSE FEDERAL UNIVERSITY   (Brazil)

^c KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

fMRI; functional brain connectivity; graphical LASSO; L1 norm SVM; major depression disorder; sparse models

Indexed keywords

BRAIN CONNECTIVITY; FMRI; GRAPHICAL LASSOS; L1 NORM; MAJOR DEPRESSIONS; SPARSE MODELS;

FACE RECOGNITION; MAGNETIC RESONANCE IMAGING; PRASEODYMIUM ALLOYS; SUPPORT VECTOR MACHINES;

FUNCTIONAL NEUROIMAGING;

EID: 84885206854     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/PRNI.2013.26     Document Type: Conference Paper

References (12)

1. C. H. Chen, J. Suckling, C. Ooi, C. H. Fu, S. C. Williams, N. D. Walsh, M. T. Mitterschiffthaler, E. M. Pich, and E. Bullmore. Functional coupling of the amygdala in depressed patients treated with antidepressant medication. Neuropsychopharmacology, 33(8):1909-1918, Jul 2008
2. R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin. LIBLINEAR: A library for large linear classification. The Journal of Machine Learning Research, 9:1871-1874, 2008
3. J. Friedman, T. Hastie, and R. Tibshirani. Sparse inverse covariance estimation with the graphical LASSO. Biostatistics, 9(3):432-441, 2008
4. C. Fu, J. Mourao-Miranda, S. Costafreda, A. Khanna, A. Marquand, S. Williams, and M. Brammer. Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol. Psychiatry, 63(7):656-662, 2008
5. S. Huang, J. Li, L. Sun, J. Ye, A. Fleisher, T. Wu, K. Chen, and E. Reiman. Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation. Neuroimage, 50(3):935-949, Apr 2010
6. K. Li, L. Guo, J. Nie, G. Li, and T. Liu. Review of methods for functional brain connectivity detection using fMRI. Comp. Med. Im. and Graph., 33(2):131, 2009
7. P. M. Rasmussen, L. K. Hansen, K. H. Madsen, N. W. Churchill, and S. C. Strother. Model sparsity and brain pattern interpretation of classification models in neuroimaging. Pattern Recognition, 2011
8. A. Stuhrmann, T. Suslow, and U. Dannlowski. Facial emotion processing in major depression: a systematic review of neuroimaging findings. Biol Mood Anxiety Disord, 1(1):10, 2011
9. M. P. van den Heuvel and H. E. Hulshoff Pol. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol, 20(8):519-534, 2010
10. Gaël Varoquaux, Alexandre Gramfort, Jean Baptiste Poline, and Bertrand Thirion. Brain covariance selection: better individual functional connectivity models using population prior. arXiv preprint arXiv:1008. 5071, 2010
11. I. M. Veer, C. F. Beckmann, M. J. van Tol, L. Ferrarini, J. Milles, D. J. Veltman, A. Aleman, M. A. van Buchem, N. J. van der Wee, and S. A. Rombouts. Whole brain resting-state analysis reveals decreased functional connectivity in major depression. Front Syst Neurosci, 4, 2010
12. C. Wee, P. Yap, D. Zhang, L. Wang, and D. Shen. Constrained sparse functional connectivity networks for MCI classification. MICCAI 2012, pages 212-219, 2012.