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Items: 1 to 20 of 101

1.

Locally Linear Embedding and fMRI Feature Selection in Psychiatric Classification.

Sidhu G.

IEEE J Transl Eng Health Med. 2019 Aug 20;7:2200211. doi: 10.1109/JTEHM.2019.2936348. eCollection 2019.

2.

Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD.

Sidhu GS, Asgarian N, Greiner R, Brown MR.

Front Syst Neurosci. 2012 Nov 9;6:74. doi: 10.3389/fnsys.2012.00074. eCollection 2012.

3.

A general prediction model for the detection of ADHD and Autism using structural and functional MRI.

Sen B, Borle NC, Greiner R, Brown MRG.

PLoS One. 2018 Apr 17;13(4):e0194856. doi: 10.1371/journal.pone.0194856. eCollection 2018.

4.

Dimensionality reduction of fMRI time series data using locally linear embedding.

Mannfolk P, Wirestam R, Nilsson M, Ståhlberg F, Olsrud J.

MAGMA. 2010 Dec;23(5-6):327-38. doi: 10.1007/s10334-010-0204-0. Epub 2010 Mar 13.

PMID:
20229085
5.

A novel fuzzy rough selection of non-linearly extracted features for schizophrenia diagnosis using fMRI.

Juneja A, Rana B, Agrawal RK.

Comput Methods Programs Biomed. 2018 Mar;155:139-152. doi: 10.1016/j.cmpb.2017.12.001. Epub 2017 Dec 6.

PMID:
29512494
6.

A framework for optimal kernel-based manifold embedding of medical image data.

Zimmer VA, Lekadir K, Hoogendoorn C, Frangi AF, Piella G.

Comput Med Imaging Graph. 2015 Apr;41:93-107. doi: 10.1016/j.compmedimag.2014.06.001. Epub 2014 Jun 9. Review.

PMID:
25008538
7.

A kernel machine-based fMRI physiological noise removal method.

Song X, Chen NK, Gaur P.

Magn Reson Imaging. 2014 Feb;32(2):150-62. doi: 10.1016/j.mri.2013.10.008. Epub 2013 Oct 19.

8.

Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI.

Shen H, Wang L, Liu Y, Hu D.

Neuroimage. 2010 Feb 15;49(4):3110-21. doi: 10.1016/j.neuroimage.2009.11.011. Epub 2009 Nov 18.

PMID:
19931396
9.

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.

Song S, Zhan Z, Long Z, Zhang J, Yao L.

PLoS One. 2011 Feb 16;6(2):e17191. doi: 10.1371/journal.pone.0017191.

10.

High classification accuracy for schizophrenia with rest and task FMRI data.

Du W, Calhoun VD, Li H, Ma S, Eichele T, Kiehl KA, Pearlson GD, Adali T.

Front Hum Neurosci. 2012 Jun 4;6:145. doi: 10.3389/fnhum.2012.00145. eCollection 2012.

11.

Effect of finite sample size on feature selection and classification: a simulation study.

Way TW, Sahiner B, Hadjiiski LM, Chan HP.

Med Phys. 2010 Feb;37(2):907-20.

12.

Accuracy of automated classification of major depressive disorder as a function of symptom severity.

Ramasubbu R, Brown MR, Cortese F, Gaxiola I, Goodyear B, Greenshaw AJ, Dursun SM, Greiner R.

Neuroimage Clin. 2016 Jul 27;12:320-31. doi: 10.1016/j.nicl.2016.07.012. eCollection 2016.

13.
14.

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
15.

Generative embedding for model-based classification of fMRI data.

Brodersen KH, Schofield TM, Leff AP, Ong CS, Lomakina EI, Buhmann JM, Stephan KE.

PLoS Comput Biol. 2011 Jun;7(6):e1002079. doi: 10.1371/journal.pcbi.1002079. Epub 2011 Jun 23.

16.

Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI).

Aradhya AMS, Subbaraju V, Sundaram S, Sundararajan N.

Conf Proc IEEE Eng Med Biol Soc. 2018 Jul;2018:5541-5544. doi: 10.1109/EMBC.2018.8513522.

PMID:
30441592
17.

LEICA: Laplacian eigenmaps for group ICA decomposition of fMRI data.

Liu C, JaJa J, Pessoa L.

Neuroimage. 2018 Apr 1;169:363-373. doi: 10.1016/j.neuroimage.2017.12.018. Epub 2017 Dec 13.

18.

Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction.

Pochet N, De Smet F, Suykens JA, De Moor BL.

Bioinformatics. 2004 Nov 22;20(17):3185-95. Epub 2004 Jul 1.

PMID:
15231531
19.

Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards.

Plitt M, Barnes KA, Martin A.

Neuroimage Clin. 2014 Dec 24;7:359-66. doi: 10.1016/j.nicl.2014.12.013. eCollection 2015.

20.

Alterations in regional homogeneity of resting-state brain activity in autism spectrum disorders.

Paakki JJ, Rahko J, Long X, Moilanen I, Tervonen O, Nikkinen J, Starck T, Remes J, Hurtig T, Haapsamo H, Jussila K, Kuusikko-Gauffin S, Mattila ML, Zang Y, Kiviniemi V.

Brain Res. 2010 Mar 19;1321:169-79. doi: 10.1016/j.brainres.2009.12.081. Epub 2010 Jan 4.

PMID:
20053346

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