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Items: 1 to 20 of 97

1.

Sparse logistic regression for whole-brain classification of fMRI data.

Ryali S, Supekar K, Abrams DA, Menon V.

Neuroimage. 2010 Jun;51(2):752-64. doi: 10.1016/j.neuroimage.2010.02.040. Epub 2010 Feb 24.

2.

Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns.

De Martino F, Valente G, Staeren N, Ashburner J, Goebel R, Formisano E.

Neuroimage. 2008 Oct 15;43(1):44-58. doi: 10.1016/j.neuroimage.2008.06.037. Epub 2008 Jul 11.

PMID:
18672070
3.

Sparse regularization techniques provide novel insights into outcome integration processes.

Mohr H, Wolfensteller U, Frimmel S, Ruge H.

Neuroimage. 2015 Jan 1;104:163-76. doi: 10.1016/j.neuroimage.2014.10.025. Epub 2014 Oct 22.

4.

Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data.

Zhang C, Song S, Wen X, Yao L, Long Z.

J Neurosci Methods. 2015 Apr 30;245:15-24. doi: 10.1016/j.jneumeth.2014.12.021. Epub 2015 Feb 11.

PMID:
25681758
5.

Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks.

Yourganov G, Schmah T, Churchill NW, Berman MG, Grady CL, Strother SC.

Neuroimage. 2014 Aug 1;96:117-32. doi: 10.1016/j.neuroimage.2014.03.074. Epub 2014 Apr 4.

PMID:
24705202
6.

Interpretable whole-brain prediction analysis with GraphNet.

Grosenick L, Klingenberg B, Katovich K, Knutson B, Taylor JE.

Neuroimage. 2013 May 15;72:304-21. doi: 10.1016/j.neuroimage.2012.12.062. Epub 2013 Jan 5.

7.

A multiple kernel learning approach to perform classification of groups from complex-valued fMRI data analysis: application to schizophrenia.

Castro E, Gómez-Verdejo V, Martínez-Ramón M, Kiehl KA, Calhoun VD.

Neuroimage. 2014 Feb 15;87:1-17. doi: 10.1016/j.neuroimage.2013.10.065. Epub 2013 Nov 10.

8.

Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns.

Yamashita O, Sato MA, Yoshioka T, Tong F, Kamitani Y.

Neuroimage. 2008 Oct 1;42(4):1414-29. doi: 10.1016/j.neuroimage.2008.05.050. Epub 2008 Jun 6.

9.

SACICA: a sparse approximation coefficient-based ICA model for functional magnetic resonance imaging data analysis.

Wang N, Zeng W, Chen L.

J Neurosci Methods. 2013 May 30;216(1):49-61. doi: 10.1016/j.jneumeth.2013.03.014. Epub 2013 Apr 4.

PMID:
23563324
10.

Detecting stable distributed patterns of brain activation using Gini contrast.

Langs G, Menze BH, Lashkari D, Golland P.

Neuroimage. 2011 May 15;56(2):497-507. doi: 10.1016/j.neuroimage.2010.07.074. Epub 2010 Aug 13.

11.

Multiclass fMRI data decoding and visualization using supervised self-organizing maps.

Hausfeld L, Valente G, Formisano E.

Neuroimage. 2014 Aug 1;96:54-66. doi: 10.1016/j.neuroimage.2014.02.006. Epub 2014 Feb 12.

PMID:
24531045
12.

Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty.

Ryali S, Chen T, Supekar K, Menon V.

Neuroimage. 2012 Feb 15;59(4):3852-61. doi: 10.1016/j.neuroimage.2011.11.054. Epub 2011 Dec 1.

13.

Discovering brain regions relevant to obsessive-compulsive disorder identification through bagging and transduction.

Parrado-Hernández E, Gómez-Verdejo V, Martínez-Ramón M, Shawe-Taylor J, Alonso P, Pujol J, Menchón JM, Cardoner N, Soriano-Mas C.

Med Image Anal. 2014 Apr;18(3):435-48. doi: 10.1016/j.media.2014.01.006. Epub 2014 Feb 3.

PMID:
24556078
14.

Sparse network-based models for patient classification using fMRI.

Rosa MJ, Portugal L, Hahn T, Fallgatter AJ, Garrido MI, Shawe-Taylor J, Mourao-Miranda J.

Neuroimage. 2015 Jan 15;105:493-506. doi: 10.1016/j.neuroimage.2014.11.021. Epub 2014 Nov 15.

15.

Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm.

Belilovsky E, Gkirtzou K, Misyrlis M, Konova AB, Honorio J, Alia-Klein N, Goldstein RZ, Samaras D, Blaschko MB.

Comput Med Imaging Graph. 2015 Dec;46 Pt 1:40-6. doi: 10.1016/j.compmedimag.2015.03.007. Epub 2015 Mar 28.

PMID:
25861834
16.

Discriminative analysis of brain function at resting-state for attention-deficit/hyperactivity disorder.

Zhu CZ, Zang YF, Liang M, Tian LX, He Y, Li XB, Sui MQ, Wang YF, Jiang TZ.

Med Image Comput Comput Assist Interv. 2005;8(Pt 2):468-75.

PMID:
16685993
17.

Support vector clustering for brain activation detection.

Wang D, Shi L, Yeung DS, Heng PA, Wong TT, Tsang EC.

Med Image Comput Comput Assist Interv. 2005;8(Pt 1):572-9.

PMID:
16685892
18.

Tree-guided sparse coding for brain disease classification.

Liu M, Zhang D, Yap PT, Shen D.

Med Image Comput Comput Assist Interv. 2012;15(Pt 3):239-47.

19.

Boost up the detection sensitivity of ASL perfusion fMRI through support vector machine.

Wang Z, Childress AR, Detre JA.

Conf Proc IEEE Eng Med Biol Soc. 2006;1:1006-9.

PMID:
17946435
20.

A spatial mixture approach to inferring sub-ROI spatio-temporal patterns from rapid event-related fMRI data.

Shen Y, Mayhew S, Kourtzi Z, Tino P.

Med Image Comput Comput Assist Interv. 2013;16(Pt 2):657-64.

PMID:
24579197

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