20.9.7.4 Functional Magnetic Resonance, fMRI

Developing Human Connectome Project (dHCP),
2017
WWW Link. Dataset, fMRI. The imaging data includes structural imaging, structural connectivity data (diffusion MRI) and functional connectivity data (resting-state fMRI).

Ruttimann, U.E., Unser, M., Rawlings, R.R., Rio, D., Ramsey, N.F., Mattay, V.S., Hommer, D.W., Frank, J.A., Weinberger, D.R.,
Statistical Analysis of Functional MRI Data in the Wavelet Domain,
MedImg(17), No. 2, April 1998, pp. 142-154.
IEEE Top Reference. 9808
BibRef

Ruttimann, U.E., Ramsey, N.F., Hommer, D.W., Thevenaz, P., Lee, C.H.[Chul-Hee], Unser, M.,
Analysis of functional magnetic resonance images by wavelet decomposition,
ICIP95(I: 633-636).
IEEE DOI 9510
BibRef

Moser, E.[Ewald], Baumgartner, R.[Richard], Barth, M.[Markus], Windischberger, C.[Christian],
Explorative signal processing in functional MR imaging,
IJIST(10), No. 2, 1999, pp. 166-176. BibRef 9900

Nan, F.Y., Nowak, R.D.,
Generalized likelihood ratio detection for fMRI using complex data,
MedImg(18), No. 4, April 1999, pp. 320-329.
IEEE Top Reference. 0110
BibRef

Svensen, M., Kruggel, F., von Cramon, D.Y.,
Probabilistic modeling of single-trial fMRI data,
MedImg(19), No. 1, January 2000, pp. 25-35.
IEEE Top Reference. 0110
BibRef

Goutte, C., Nielsen, F.A., Hansen, K.H.,
Modeling the hemodynamic response in fMRI using smooth FIR filters,
MedImg(19), No. 12, December 2000, pp. 1188-1201.
IEEE Top Reference. 0110
BibRef

Godtliebseu, F., Chu, C.K.[Chih-Kang], Sorbye, S.H., Torheim, G.,
An estimator for functional data with application to MRI,
MedImg(20), No. 1, January 2001, pp. 36-44.
IEEE Top Reference. 0110
BibRef

Sole, A.F., Ngan, S.C.[Shing-Chung], Sapiro, G., Hu, X.P.[Xiao-Ping], Lopez, A.,
Anisotropic 2-D and 3-D averaging of fMRI signals,
MedImg(20), No. 2, February 2001, pp. 86-93.
IEEE Top Reference. 0110
BibRef

Belmonte, M., Yurgelun-Todd, D.A.,
Permutation testing made practical for functional magnetic resonance image analysis,
MedImg(20), No. 3, March 2001, pp. 243-248.
IEEE Top Reference. 0110
BibRef

Kruggel, F., von Cramon, D.Y.,
Nonlinear regression analysis of the hemodynamic response in functional MRI,
PRL(22), No. 11, September 2001, pp. 1247-1252.
Elsevier DOI 0108
BibRef

Solo, V., Purdon, P., Weisskoff, R., Brown, E.,
A signal estimation approach to functional MRI,
MedImg(20), No. 1, January 2001, pp. 26-35.
IEEE Top Reference. 0110
BibRef

Solo, V., Long, C.J., Brown, E.N., Aminoff, E., Bar, M., Saha, S.,
FMRI signal modeling using laguerre polynomials,
ICIP04(IV: 2431-2434).
IEEE DOI 0505
BibRef

Solo, V., Brown, E.N., Long, C.J.,
Spatial wavelets for temporally correlated FMRI,
ICIP03(II: 843-846).
IEEE DOI 0312
BibRef

Solo, V., Purdon, P., Brown, E., Weisskoff, R.,
Model Comparison for Functional MRI,
ICIP99(II:649-652).
IEEE DOI BibRef 9900
Earlier:
Regularization for functional MRI models,
ICIP98(I: 714-716).
IEEE DOI 9810
BibRef

Solo, V., Brown, E., and Weisskoff, R.,
A Signal Processing Approach to Functional MRI for Brain Mapping,
ICIP97(II: 121-123).
IEEE DOI BibRef 9700

Ulfarsson, M.O., Solo, V.,
Spatially Local and Temporally Smooth PCA for fMRI,
ICIP06(2853-2856).
IEEE DOI 0610
BibRef

Chuang, K.H.[Kai-Hsiang], Chiu, M.J.[Ming-Jang], Lin, C.C.[Chung-Chih], Chen, J.H.[Jyh-Horng],
Model-free functional MRI analysis using Kohonen clustering neural network and fuzzy C-means,
MedImg(18), No. 12, December 1999, pp. 1117-1128.
IEEE Top Reference. 0110
BibRef

von Tscharner, V., Thulborn, K.R.,
Specified-resolution wavelet analysis of activation patterns from BOLD contrast fMRI,
MedImg(20), No. 8, August 2001, pp. 704-714.
IEEE Top Reference. 0110
BibRef

Otte, M.,
Elastic registration of fMRI data using Bezier-spline transformations,
MedImg(20), No. 3, March 2001, pp. 193-206.
IEEE Top Reference. 0110
BibRef

Salli, E., Aronen, H.J., Savolainen, S., Korvenoja, A., Visa, A.,
Contextual clustering for analysis of functional MRI data,
MedImg(20), No. 5, May 2001, pp. 403-414.
IEEE Top Reference. 0110
BibRef

Freire, L., Roche, A., Mangin, J.F.,
What is the best similarity measure for motion correction in fMRI time series?,
MedImg(21), No. 5, May 2002, pp. 470-484.
IEEE Top Reference. 0206
BibRef

Lohmann, G., Bohn, S.,
Using replicator dynamics for analyzing fMRI data of the human brain,
MedImg(21), No. 5, May 2002, pp. 485-492.
IEEE Top Reference. 0206
BibRef

Polydorides, N., Lionheart, W.R.B., McCann, H.,
Krylov subspace iterative techniques: on the detection of brain activity with electrical impedance tomography,
MedImg(21), No. 6, June 2002, pp. 596-603.
IEEE Top Reference. 0208
BibRef

Desai, M., Mangoubi, R., Shah, J., Karl, W., Pien, H., Worth, A.J., Kennedy, D.N.,
Functional MRI activity characterization using response time shift estimates from curve evolution,
MedImg(21), No. 11, November 2002, pp. 1402-1412.
IEEE Top Reference. 0301
BibRef

Hossein-Zadeh, G.A., Soltanian-Zadeh, H., Ardekani, B.A.,
Multiresolution fMRI activation detection using translation invariant wavelet transform and statistical analysis based on resampling,
MedImg(22), No. 3, March 2003, pp. 302-314.
IEEE Abstract. 0306
BibRef

Hossein-Zadeh, G.A., Ardekani, B.A., Soltanian-Zadeh, H.,
Activation detection in fMRI using a maximum energy ratio statistic obtained by adaptive spatial filtering,
MedImg(22), No. 7, July 2003, pp. 795-805.
IEEE Abstract. 0308
BibRef

Jafari-Khouzani, K., Soltanian-Zadeh, H.,
Radon Transform Orientation Estimation for Rotation Invariant Texture Analysis,
PAMI(27), No. 6, June 2005, pp. 1004-1008.
IEEE Abstract. 0505
BibRef

Jafari-Khouzani, K., Soltanian-Zadeh, H.,
Rotation-Invariant Multiresolution Texture Analysis Using Radon and Wavelet Transforms,
IP(14), No. 6, June 2005, pp. 783-795.
IEEE DOI 0505
BibRef

Penny, W.D., Friston, K.,
Mixtures of general linear models for functional neuroimaging,
MedImg(22), No. 4, April 2003, pp. 504-514.
IEEE Abstract. 0306
BibRef

Gautama, T., Mandic, D.P., van Hulle, M.M.,
Signal nonlinearity in fMRI: a comparison between BOLD and MION,
MedImg(22), No. 5, May 2003, pp. 636-644.
IEEE Abstract. 0307
BibRef

Deleus, F., van Hulle, M.M.,
A Connectivity-Based Method for Defining Regions-of-Interest in fMRI Data,
IP(18), No. 8, August 2009, pp. 1760-1771.
IEEE DOI 0907
BibRef

Lindquist, M.A.[Martin A.], Zhang, C.H., Glover, G., Shepp, L., Yang, Q.X.,
A Generalization of the Two-Dimensional Prolate Spheroidal Wave Function Method for Nonrectilinear MRI Data Acquisition Methods,
IP(15), No. 9, August 2006, pp. 2792-2804.
IEEE DOI 0608
BibRef

Lindquist, M.A.[Martin A.],
Optimal data acquisition in fMRI using prolate spheroidal wave functions,
IJIST(13), No. 2, 2003, pp. 126-132.
WWW Link. 0308
BibRef

Meyer, F.G.,
Wavelet-based estimation of a semiparametric generalized linear model of fMRI time-series,
MedImg(22), No. 3, March 2003, pp. 315-322.
IEEE Abstract. 0306
BibRef

Meyer, F.G., Chinrungrueng, J.,
Analysis of event-related fMRI data using best clustering bases,
MedImg(22), No. 8, August 2003, pp. 933-939.
IEEE Abstract. 0308
BibRef

Meyer, F.G.[Francois G.], Shen, X.L.[Xi-Lin],
Classification of fMRI Time Series in a Low-Dimensional Subspace With a Spatial Prior,
MedImg(27), No. 1, January 2008, pp. 87-98.
IEEE DOI 0712
BibRef
Earlier:
Nonlinear Dimension Reduction and Activation Detection for fMRI Dataset,
MMBIA06(90).
IEEE DOI 0609
BibRef

Ciuciu, P., Poline, J.B., Marrelec, G., Idier, J., Pallier, C., Benali, H.,
Unsupervised robust nonparametric estimation of the hemodynamic response function for any FMRI experiment,
MedImg(22), No. 10, October 2003, pp. 1235-1251.
IEEE Abstract. 0310
BibRef

Orchard, J., Greif, C., Golub, G.H., Bjornson, B., Atkins, M.S.,
Simultaneous registration and activation detection for fMRI,
MedImg(22), No. 11, November 2003, pp. 1427-1435.
IEEE Abstract. 0311
BibRef

Chen, S., Bouman, C.A., Lowe, M.J.,
Clustered Components Analysis for Functional MRI,
MedImg(23), No. 1, January 2004, pp. 85-98.
IEEE Abstract. 0403
BibRef

Woolrich, M.W., Jenkinson, M., Brady, J.M., Smith, S.M.,
Fully Bayesian Spatio-Temporal Modeling of FMRI Data,
MedImg(23), No. 2, February 2004, pp. 213-231.
IEEE Abstract. 0403
BibRef

Woolrich, M.W., Behrens, T.E.J., Beckmann, C.F., Smith, S.M.,
Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data,
MedImg(24), No. 1, January 2005, pp. 1-11.
IEEE Abstract. 0501
BibRef

Woolrich, M.W., Behrens, T.E.J.,
Variational Bayes Inference of Spatial Mixture Models for Segmentation,
MedImg(25), No. 10, October 2006, pp. 1380-1391.
IEEE DOI 0609
BibRef

Soltanian-Zadeh, H., Peck, D.J., Hearshen, D.O., Lajiness-O'Neill, R.R.,
Model-Independent Method for fMRI Analysis,
MedImg(23), No. 3, March 2004, pp. 285-296.
IEEE Abstract. 0403
BibRef

Wink, A.M., Roerdink, J.B.J.M.,
Denoising Functional MR Images: A Comparison of Wavelet Denoising and Gaussian Smoothing,
MedImg(23), No. 3, March 2004, pp. 374-387.
IEEE Abstract. 0403
BibRef

Beckmann, C.F., Smith, S.M.,
Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging,
MedImg(23), No. 2, February 2004, pp. 137-152.
IEEE Abstract. 0403
BibRef

Marrelec, G., Ciuciu, P., Pelegrini-Issac, M., Benali, H.,
Estimation of the Hemodynamic Response in Event-Related Functional MRI: Bayesian Networks as a Framework for Efficient Bayesian Modeling and Inference,
MedImg(23), No. 8, August 2004, pp. 959-967.
IEEE Abstract. 0409
BibRef

Liao, R.[Rui], Krolik, J.L.[Jeffrey L.], McKeown, M.J.[Martin J.],
An information-theoretic criterion for intrasubject alignment of FMRI time series: motion corrected independent component analysis,
MedImg(24), No. 1, January 2005, pp. 29-44.
IEEE Abstract. 0501
BibRef

Liao, R.[Rui], McKeown, M.J.[Martin J.], Krolik, J.L.[Jeffrey L.],
Improved Motion Correction of fMRI Time-Series Corrupted with Major Head Movement Using Extended Motion-Corrected Independent Component Analysis,
CVBIA05(346-355).
Springer DOI 0601
BibRef

Lu, Y., Jiang, T., Zang, Y.,
Single-Trial Variable Model for Event-Related fMRI Data Analysis,
MedImg(24), No. 2, February 2005, pp. 236-245.
IEEE Abstract. 0501
BibRef

Faisan, S., Thoraval, L., Armspach, J.P., Metz-Lutz, M.N., Heitz, F.,
Unsupervised Learning and Mapping of Active Brain Functional MRI Signals Based on Hidden Semi-Markov Event Sequence Models,
MedImg(24), No. 2, February 2005, pp. 263-276.
IEEE Abstract. 0501
BibRef
Earlier: A1, A2, A3, A5, Only:
Hidden semi-Markov event sequence models: application to brain functional MRI sequence analysis,
ICIP02(I: 880-883).
IEEE DOI 0210
BibRef

Brucher, M.[Matthieu], Heinrich, C.[Christian], Heitz, F.[Fabrice], Armspach, J.P.[Jean-Paul],
Unsupervised Nonlinear Manifold Learning,
ICIP07(II: 109-112).
IEEE DOI 0709
BibRef

Bogorodzki, P., Rogowska, J., Yurgelun-Todd, D.A.,
Structural Group Classification Technique Based on Regional fMRI BOLD Responses,
MedImg(24), No. 3, March 2005, pp. 389-398.
IEEE Abstract. 0501
BibRef

Sijbers, J.[Jan], den Dekker, A.J.[Arnold J.],
Generalized Likelihood Ratio Tests for Complex fMRI Data: A Simulation Study,
MedImg(24), No. 5, May 2005, pp. 604-611.
IEEE Abstract. 0505
BibRef

den Dekker, A.J., Poot, D.H.J., Bos, R., Sijbers, J.,
Likelihood-Based Hypothesis Tests for Brain Activation Detection From MRI Data Disturbed by Colored Noise: A Simulation Study,
MedImg(28), No. 2, February 2009, pp. 287-296.
IEEE DOI 0902
BibRef

Rajan, J.[Jeny], Poot, D.H.J.[Dirk H.J.], Juntu, J.[Jaber], Sijbers, J.[Jan],
Segmentation Based Noise Variance Estimation from Background MRI Data,
ICIAR10(I: 62-70).
Springer DOI 1006
BibRef

Turner, G.H., Twieg, D.B.,
Study of Temporal Stationarity and Spatial Consistency of fMRI Noise Using Independent Component Analysis,
MedImg(24), No. 6, June 2005, pp. 712-718.
IEEE Abstract. 0506
BibRef

Voultsidou, M., Dodel, S., Herrmann, J.M.,
Neural Networks Approach to Clustering of Activity in fMRI Data,
MedImg(24), No. 8, August 2005, pp. 987-996.
IEEE DOI 0508
BibRef

Chen, H., Yao, D., Chen, W., Chen, L.,
Delay Correlation Subspace Decomposition Algorithm and Its Application in fMRI,
MedImg(24), No. 12, December 2005, pp. 1647-1651.
IEEE DOI 0601
BibRef

Wang, Y., Rajapakse, J.C.,
Contextual Modeling of Functional MR Images With Conditional Random Fields,
MedImg(25), No. 6, June 2006, pp. 804-812.
IEEE DOI 0606
BibRef

Jeong, J.W.[Jeong-Won], Kim, T.S.[Tae-Seong], Kim, S.H.[Sung-Heon], Singh, M.[Manbir],
Application of independent component analysis with mixture density model to localize brain alpha activity in fMRI and EEG,
IJIST(14), No. 4, 2004, pp. 170-180.
DOI Link 0408
BibRef

Peeters, R.R.[Ronald R.], Kornprobst, P.[Pierre], Nikolova, M.[Mila], Sunaert, S.[Stefan], Vieville, T.[Thierry], Malandain, G.[Grégoire], Deriche, R.[Rachid], Faugeras, O.D.[Olivier D.], Ng, M.[Michael], van Hecke, P.[Paul],
The use of super-resolution techniques to reduce slice thickness in functional MRI,
IJIST(14), No. 3, 2004, pp. 131-138.
DOI Link 0408
BibRef

Shijie, W.[Wang], Limin, L.[Luo], Weiping, Z.[Zhou],
Robust Ordering of Independent Spatial Components of fMRI Data Using Canonical Correlation Analysis,
ICIAR06(II: 672-679).
Springer DOI 0610
BibRef

Bannister, P.R.[Peter R.], Brady, J.M.[J. Michael], Jenkinson, M.[Mark],
Integrating temporal information with a non-rigid method of motion correction for functional magnetic resonance images,
IVC(25), No. 3, March 2007, pp. 311-320.
Elsevier DOI 0701
BibRef
Earlier:
Non-Rigid Motion Estimation and Spatio-Temporal Realignment in FMRI,
Non-Rigid04(4).
IEEE DOI 0502
FMRI; Image registration; Non-rigid motion; Motion correction; Slice-timing correction BibRef

Gholipour, A.[Ali], Kehtarnavaz, N.[Nasser], Briggs, R.W.[Richard W.], Devous, M.[Michael], Gopinath, K.S.[Kaundinya S.],
Brain Functional Localization: A Survey of Image Registration Techniques,
MedImg(26), No. 4, April 2007, pp. 427-451.
IEEE DOI 0704
Survey, Registration. BibRef

Wang, D.F.[De-Feng], Shi, L.[Lin], Yeung, D.S.[Daniel S.], Tsang, E.C.C.[Eric C.C.], Heng, P.A.[Pheng Ann],
Ellipsoidal support vector clustering for functional MRI analysis,
PR(40), No. 10, October 2007, pp. 2685-2695.
Elsevier DOI 0707
fMRI; Activation detection; Support vector clustering; Ellipsoidal support vector clustering BibRef

Lukic, A.S., Wernick, M.N., Yang, Y., Hansen, L.K., Arfanakis, K., Strother, S.C.,
Effect of Spatial Alignment Transformations in PCA and ICA of Functional Neuroimages,
MedImg(26), No. 8, August 2007, pp. 1058-1068.
IEEE DOI 0709
BibRef

Lukic, A.S., Wernick, M.N., Tzikas, D.G., Chen, X.[Xu], Likas, A., Galatsanos, N.P., Yang, Y.Y.[Yong-Yi], Zhao, F.Q.[Fu-Qiang], Strother, S.C.,
Bayesian Kernel Methods for Analysis of Functional Neuroimages,
MedImg(26), No. 12, December 2007, pp. 1613-1624.
IEEE DOI 0712
BibRef

Rasmussen, P.M.[Peter M.], Hansen, L.K.[Lars K.], Madsen, K.H.[Kristoffer H.], Churchill, N.W.[Nathan W.], Strother, S.C.[Stephen C.],
Model sparsity and brain pattern interpretation of classification models in neuroimaging,
PR(45), No. 6, June 2012, pp. 2085-2100.
Elsevier DOI 1202
Neuroimaging; Pattern analysis; Classification; Machine learning; Regularization; Kernel methods; Sparsity; Model interpretation; NPAIRS resampling BibRef

Thirion, B., Pinel, P., Tucholka, A., Roche, A., Ciuciu, P., Mangin, J.F., Poline, J.B.,
Structural Analysis of fMRI Data Revisited: Improving the Sensitivity and Reliability of fMRI Group Studies,
MedImg(26), No. 9, September 2007, pp. 1256-1269.
IEEE DOI 0710
BibRef

Michel, V.[Vincent], Eger, E.[Evelyn], Keribin, C.[Christine], Poline, J.B.[Jean-Baptiste], Thirion, B.[Bertrand],
A supervised clustering approach for extracting predictive information from brain activation images,
MMBIA10(7-14).
IEEE DOI 1006
BibRef

Thirion, B.[Bertrand], Roche, A.[Alexis], Ciuciu, P.[Philippe], Poline, J.B.[Jean-Baptiste],
Improving Sensitivity and Reliability of fMRI Group Studies through High Level Combination of Individual Subjects Results,
MMBIA06(62).
IEEE DOI 0609
BibRef

Roche, A.[Alexis],
A Four-Dimensional Registration Algorithm With Application to Joint Correction of Motion and Slice Timing in fMRI,
MedImg(30), No. 8, August 2011, pp. 1546-1554.
IEEE DOI 1108
BibRef

Wotawa, N.[Nicolas], Thirion, B.[Bertrand], Castet, E.[Eric], Anton, J.L.[Jean-Luc], Faugeras, O.D.[Olivier D.],
Human Retinotopic Mapping Using fMRI,
INRIARR-5472, 2005.
HTML Version. BibRef 0500

Deneux, T.[Thomas], Faugeras, O.D.[Olivier D.],
EEG-fMRI fusion of non-triggered data using Kalman filtering,
INRIARR-5760, 2005.
HTML Version. BibRef 0500
And:
Parameter estimation efficiency using nonlinear models in fMRI,
INRIARR-5758, 2005.
HTML Version. BibRef

Tabelow, K., Polzehl, J., Ulug, A.M., Dyke, J.P., Watts, R., Heier, L.A., Voss, H.U.,
Accurate Localization of Brain Activity in Presurgical fMRI by Structure Adaptive Smoothing,
MedImg(27), No. 4, April 2008, pp. 531-537.
IEEE DOI 0804
BibRef

Rajapakse, J.C., Wang, Y., Zheng, X., Zhou, J.,
Probabilistic Framework for Brain Connectivity From Functional MR Images,
MedImg(27), No. 6, June 2008, pp. 825-833.
IEEE DOI 0711
BibRef

Tuan, A.S.[August S.], Birn, R.M.[Rasmus M.], Bandettini, P.A.[Peter A.], Boynton, G.M.[Geoffrey M.],
Differential transient MEG and fMRI responses to visual stimulation onset rate,
IJIST(18), No. 1, 2008, pp. 17-28.
DOI Link 0806
BibRef

Kriegeskorte, N.[Nikolaus], Bodurka, J.[Jerzy], Bandettini, P.A.[Peter A.],
Artifactual time-course correlations in echo-planar fMRI with implications for studies of brain function,
IJIST(18), No. 5-6, 2008, pp. 345-349.
DOI Link 0804
BibRef

Lee, J.H.[Jong-Hwan], Lee, T.W.[Te-Won], Jolesz, F.A.[Ferenc A.], Yoo, S.S.[Seung-Schik],
Independent vector analysis (IVA) for group fMRI processing of subcortical area,
IJIST(18), No. 1, 2008, pp. 29-41.
DOI Link 0806
BibRef

Yoo, S.S.[Seung-Schik], Lee, J.H.[Jong-Hwan], O'Leary, H.[Heather], Panych, L.P.[Lawrence P.], Jolesz, F.A.[Ferenc A.],
Neurofeedback fMRI-mediated learning and consolidation of regional brain activation during motor imagery,
IJIST(18), No. 1, 2008, pp. 69-78.
DOI Link 0806
BibRef

Novatnack, J.[John], Cornea, N.[Nicu], Shokoufandeh, A.[Ali], Silver, D.[Deborah], Dickinson, S.J.[Sven J.], Kantor, P.[Paul], Bai, B.[Bing],
A generalized family of fixed-radius distribution-based distance measures for content-based fMRI image retrieval,
PRL(29), No. 12, 1 September 2008, pp. 1726-1732.
Elsevier DOI 0804
Content-based image retrieval; Brain imaging; fMRI image matching BibRef

Olafsson, V.T., Noll, D.C., Fessler, J.A.,
Fast Joint Reconstruction of Dynamic R_2* and Field Maps in Functional MRI,
MedImg(27), No. 9, September 2008, pp. 1177-1188.
IEEE DOI 0809
BibRef

Liao, W., Chen, H., Yang, Q., Lei, X.,
Analysis of fMRI Data Using Improved Self-Organizing Mapping and Spatio-Temporal Metric Hierarchical Clustering,
MedImg(27), No. 10, October 2008, pp. 1472-1483.
IEEE DOI 0810
BibRef

Ng, B.[Bernard], Abu Gharbieh, R.[Rafeef], Huang, X.M.[Xue-Mei], McKeown, M.J.[Martin J.],
Spatial Characterization of fMRI Activation Maps Using Invariant 3-D Moment Descriptors,
MedImg(28), No. 2, February 2009, pp. 261-268.
IEEE DOI 0902
BibRef
Earlier:
Characterizing fMRI Activations within Regions of Interest (ROIs) Using 3D Moment Invariants,
MMBIA06(63).
IEEE DOI 0609
BibRef

Lenglet, C.[Christophe], Prados, E.[Emmanuel], Pons, J.P.[Jean-Philippe], Deriche, R.[Rachid], Faugeras, O.D.[Olivier D.],
Brain Connectivity Mapping Using Riemannian Geometry, Control Theory, and PDEs,
SIIMS(2), No. 2, 2009, pp. 285-322.
DOI Link Brownian motion; diffusion process; control theory; partial differential equations; Riemannian manifolds; HamiltonJacobiBellman equations; level set; fast marching methods; anisotropic Eikonal equation; intrinsic distance function; brain connectivity mapping; diffusion tensor imaging BibRef 0900

Prados, E.[Emmanuel], Soatto, S.[Stefano], Lenglet, C.[Christophe], Pons, J.P.[Jean-Philippe], Wotawa, N.[Nicolas], Deriche, R.[Rachid], Faugeras, O.D.[Olivier D.],
Control Theory and Fast Marching Techniques for Brain Connectivity Mapping,
CVPR06(I: 1076-1083).
IEEE DOI 0606
BibRef
And: A1, A3, A4, A5, A6, A7, A2:
Control Theory and Fast Marching Methods for Brain Connectivity Mapping,
INRIARR-5845, 2006.
HTML Version. BibRef

Thirion, B.[Bertrand], Faugeras, O.D.[Olivier D.],
Activation Detection and Characterisation in Brain fMRI Sequences. Application to the study of monkey vision,
INRIARR-4213, June 2001.
HTML Version. 0211
BibRef
And:
Revisiting Non-Parametric Activation Detection on fMRI Time Series,
MMBIA01(xx-yy). 0110
BibRef

Venkataraman, A.[Archana], Rathi, Y., Kubicki, M.[Marek], Westin, C.F.[Carl-Fredrik], Golland, P.[Polina],
Joint Modeling of Anatomical and Functional Connectivity for Population Studies,
MedImg(31), No. 2, February 2012, pp. 164-182.
IEEE DOI 1202
BibRef
Earlier: A1, A3, A4, A5, Only:
Robust feature selection in resting-state fMRI connectivity based on population studies,
MMBIA10(63-70).
IEEE DOI 1006
BibRef

Venkataraman, A.[Archana], Kubicki, M.[Marek], Golland, P.[Polina],
From Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder,
MedImg(32), No. 11, 2013, pp. 2078-2098.
IEEE DOI 1312
biomedical MRI BibRef

Michel, V.[Vincent], Gramfort, A.[Alexandre], Varoquaux, G.[Gaël], Eger, E.[Evelyn], Keribin, C.[Christine], Thirion, B.[Bertrand],
A supervised clustering approach for fMRI-based inference of brain states,
PR(45), No. 6, June 2012, pp. 2041-2049.
Elsevier DOI 1202
fMRI; Brain reading; Prediction; Hierarchical clustering; Dimension reduction; Multi-scale analysis; Feature agglomeration BibRef

Michel, V.[Vincent], Gramfort, A.[Alexandre], Varoquaux, G.[Gaël], Eger, E.[Evelyn], Thirion, B.[Bertrand],
Total Variation Regularization for fMRI-Based Prediction of Behavior,
MedImg(30), No. 7, July 2011, pp. 1328-1340.
IEEE DOI 1107
BibRef

Wang, Y.M., Xia, J.[Jing],
Unified Framework for Robust Estimation of Brain Networks From fMRI Using Temporal and Spatial Correlation Analyses,
MedImg(28), No. 8, August 2009, pp. 1296-1307.
IEEE DOI 0909
BibRef

Liao, W., Marinazzo, D., Pan, Z., Gong, Q., Chen, H.,
Kernel Granger Causality Mapping Effective Connectivity on fMRI Data,
MedImg(28), No. 11, November 2009, pp. 1825-1835.
IEEE DOI 0911
BibRef

Kuncheva, L.I., Rodriguez, J.J., Plumpton, C.O., Linden, D.E.J., Johnston, S.J.,
Random Subspace Ensembles for fMRI Classification,
MedImg(29), No. 2, February 2010, pp. 531-542.
IEEE DOI 1002
BibRef

Plumpton, C.O.[Catrin O.], Kuncheva, L.I.[Ludmila I.], Oosterhof, N.N.[Nikolaas N.], Johnston, S.J.[Stephen J.],
Naive random subspace ensemble with linear classifiers for real-time classification of fMRI data,
PR(45), No. 6, June 2012, pp. 2101-2108.
Elsevier DOI 1202
Functional magnetic resonance imaging (fMRI); Online classification; Naive labelling; Classifier ensembles BibRef

Plumpton, C.O.[Catrin O.], Kuncheva, L.I.[Ludmila I.], Linden, D.E.J.[David E.J.], Johnston, S.J.[Stephen J.],
On-Line fMRI Data Classification Using Linear and Ensemble Classifiers,
ICPR10(4312-4315).
IEEE DOI 1008
BibRef

Lindquist, M.A.[Martin A.],
The benefits of rapid 3D fMRI,
IJIST(20), No. 1, March 2010, pp. 14-22.
DOI Link 1003
BibRef

Lee, J.H.[Jin Hyung],
Balanced steady state free precession fMRI,
IJIST(20), No. 1, March 2010, pp. 23-30.
DOI Link 1003
BibRef

Raizada, R.D.S.[Rajeev D. S.], Kriegeskorte, N.[Nikolaus],
Pattern-information fMRI: New questions which it opens up and challenges which face it,
IJIST(20), No. 1, March 2010, pp. 31-41.
DOI Link 1003
BibRef

Song, A.W.[Allen W.], Truong, T.K.[Trong-Kha],
Apparent diffusion coefficient dependent fMRI: Spatiotemporal characteristics and implications on calibrated fMRI,
IJIST(20), No. 1, March 2010, pp. 42-50.
DOI Link 1003
BibRef

Vincent, T.[Thomas], Risser, L.[Laurent], Ciuciu, P.[Philippe],
Spatially Adaptive Mixture Modeling for Analysis of fMRI Time Series,
MedImg(29), No. 4, April 2010, pp. 1059-1074.
IEEE DOI 1003
BibRef

Risser, L.[Laurent], Idier, J.[Jerome], Ciuciu, P.[Philippe], Vincent, T.[Thomas],
Fast bilinear extrapolation of 3D ising field partition function. application to fMRI image analysis,
ICIP09(833-836).
IEEE DOI 0911
BibRef

Grana, M.[Manuel], Savio, A.M.[Alexandre M.], Garcia-Sebastian, M.[Maite], Fernandez, E.[Elsa],
A lattice computing approach for on-line fMRI analysis,
IVC(28), No. 7, July 2010, pp. 1155-1161.
Elsevier DOI 1006
fMRI; Lattice computing; Lattice Associative Memories; Linear mixing model BibRef

Muckli, L.[Lars],
What are we missing here? Brain imaging evidence for higher cognitive functions in primary visual cortex V1,
IJIST(20), No. 2, June 2010, pp. 131-139.
DOI Link 1006
BibRef

Kim, S.[Seyoung], Smyth, P., Stern, H.,
A Bayesian Mixture Approach to Modeling Spatial Activation Patterns in Multisite fMRI Data,
MedImg(29), No. 6, June 2010, pp. 1260-1274.
IEEE DOI 1007
BibRef

Pendse, G.V., Baumgartner, R., Schwarz, A.J., Coimbra, A., Borsook, D., Becerra, L.,
A Statistical Framework for Optimal Design Matrix Generation With Application to fMRI,
MedImg(29), No. 9, September 2010, pp. 1573-1611.
IEEE DOI 1003
BibRef

Peterson, M.F.[Matthew F.], Das, K.[Koel], Sy, J.L.[Jocelyn L.], Li, S.[Sheng], Giesbrecht, B.[Barry], Kourtzi, Z.[Zoe], Eckstein, M.P.[Miguel P.],
Ideal observer analysis for task normalization of pattern classifier performance applied to EEG and fMRI data,
JOSA-A(27), No. 12, December 2010, pp. 2670-2683.
WWW Link. 1101
BibRef

Lee, K., Tak, S., Ye, J.C.,
A Data-Driven Sparse GLM for fMRI Analysis Using Sparse Dictionary Learning With MDL Criterion,
MedImg(30), No. 5, May 2011, pp. 1076-1089.
IEEE DOI 1105
BibRef

Kalberlah, C.[Christian], Chen, Y.[Yi], Heinzle, J.[Jakob], Haynes, J.D.[John-Dylan],
Beyond topographic representation: Decoding visuospatial attention from local activity patterns in the human frontal cortex,
IJIST(21), No. 2, June 2011, pp. 201-210.
DOI Link 1101
BibRef

Lee, J.H.[Jong-Hwan], Hashimoto, R.[Ryuichiro], Wible, C.G.[Cynthia G.], Yoo, S.S.[Seung-Schik],
Investigation of spectrally coherent resting-state networks using non-negative matrix factorization for functional MRI data,
IJIST(21), No. 2, June 2011, pp. 211-222.
DOI Link 1101
BibRef

Anderson, A.[Ariana], Bramen, J.[Jennifer], Douglas, P.K.[Pamela K.], Lenartowicz, A.[Agatha], Cho, A.[Andrew], Culbertson, C.[Chris], Brody, A.L.[Arthur L.], Yuille, A.L.[Alan L.], Cohen, M.S.[Mark S.],
Large sample group independent component analysis of functional magnetic resonance imaging using anatomical atlas-based reduction and bootstrapped clustering,
IJIST(21), No. 2, June 2011, pp. 223-231.
DOI Link 1101
BibRef

Leech, R., Leech, D.,
Testing for Spatial Heterogeneity in Functional MRI Using the Multivariate General Linear Model,
MedImg(30), No. 6, June 2011, pp. 1293-1302.
IEEE DOI 1101
BibRef

Li, X., Coyle, D., Maguire, L., McGinnity, T.M., Benali, H.,
A Model Selection Method for Nonlinear System Identification Based fMRI Effective Connectivity Analysis,
MedImg(30), No. 7, July 2011, pp. 1365-1380.
IEEE DOI 1107
BibRef

Blaschko, M.B.[Matthew B.], Shelton, J.A.[Jacquelyn A.], Bartels, A.[Andreas], Lampert, C.H.[Christoph H.], Gretton, A.[Arthur],
Semi-supervised kernel canonical correlation analysis with application to human fMRI,
PRL(32), No. 11, 1 August 2011, pp. 1572-1583.
Elsevier DOI 1108
Canonical correlation analysis; Semi-supervised learning; fMRI BibRef

Chiew, M., Graham, S.J.,
BOLD Contrast and Noise Characteristics of Densely Sampled Multi-Echo fMRI Data,
MedImg(30), No. 9, September 2011, pp. 1691-1703.
IEEE DOI 1109
BibRef

Monir, S.M.[Syed Muhammad], Siyal, M.Y.[Mohammed Yakoob],
Iterative adaptive spatial filtering for noise-suppression in functional magnetic resonance imaging time-series,
IJIST(21), No. 3, September 2011, pp. 260-270.
DOI Link 1109
BibRef

Roujol, S., Ries, M., Moonen, C.T.W., de Senneville, B.D.[B. Denis],
Automatic Nonrigid Calibration of Image Registration for Real Time MR-Guided HIFU Ablations of Mobile Organs,
MedImg(30), No. 10, October 2011, pp. 1737-1745.
IEEE DOI 1110
BibRef

Aguerre, C., Desbarats, P., de Senneville, B.D.[B. Denis], Herigault, G., Dilharreguy, B., Moonen, C.T.W.,
A Method for Large Vessels/Brain Activity Colocalization,
ICIP06(2849-2852).
IEEE DOI 0610
BibRef

de Senneville, B.D., Desbarats, P., Ries, M., Moonen, C.T.W., Grenier, N.,
Automatic Region Tracking for MR Glomerular Filtration Rate Analysis,
ICIP06(2837-2840).
IEEE DOI 0610
BibRef

Aguerre, C., Desbarats, P., Dilharreguy, B., Moonen, C.T.W.,
3dD animation of cerebral activity using both spatial and temporal fMRI information,
3DIM03(103-109).
IEEE DOI 0311
BibRef

Abrahamsen, T.J.[Trine Julie], Hansen, L.K.[Lars Kai],
Sparse non-linear denoising: Generalization performance and pattern reproducibility in functional MRI,
PRL(32), No. 15, 1 November 2011, pp. 2080-2085.
Elsevier DOI 1112
Kernel PCA; Pre-image estimation; Denoising; Sparsity; Reproducibility; Functional MRI BibRef

Kim, E.[Eunwoo], Han, Y.[Yeji], Park, H.[Hyunwook],
New fMRI analysis method for multiple stimuli using reference estimation,
IJIST(21), No. 4, December 2011, pp. 315-322.
DOI Link 1112
BibRef

Björnsdotter, M.[Malin], Wessberg, J.[Johan],
Clustered sampling improves random subspace brain mapping,
PR(45), No. 6, June 2012, pp. 2035-2040.
Elsevier DOI 1202
BibRef
Earlier:
A Memetic Algorithm for Selection of 3D Clustered Features with Applications in Neuroscience,
ICPR10(1076-1079).
IEEE DOI 1008
fMRI; Random subspace; Feature selection; Brain mapping BibRef

Rodriguez, P.A.[Pedro A.], Calhoun, V.D.[Vince D.], Adali, T.[Tülay],
De-noising, phase ambiguity correction and visualization techniques for complex-valued ICA of group fMRI data,
PR(45), No. 6, June 2012, pp. 2050-2063.
Elsevier DOI 1202
fMRI; ICA; Group analysis; Phase ambiguity; De-noising; Visualization BibRef

Cabral, C.[Carlos], Silveira, M.[Margarida], Figueiredo, P.[Patricia],
Decoding visual brain states from fMRI using an ensemble of classifiers,
PR(45), No. 6, June 2012, pp. 2064-2074.
Elsevier DOI 1202
fMRI; Retinotopic mapping; Visual localizer; Brain decoding; Machine learning; Ensemble of classifiers BibRef

Afonso, D.[David], Figueiredo, P.[Patrícia], Sanches, J.M.[João Miguel],
Automatic HyperParameter Estimation in fMRI,
IbPRIA11(117-125).
Springer DOI 1106
BibRef

Afonso, D.[David], Sanches, J.M.[João Miguel], Lauterbach, M.H.[Martin H.],
Robust brain activation detection in functional MRI,
ICIP08(2960-2963).
IEEE DOI 0810
BibRef

Seghouane, A.K.[Abd-Krim], Shah, A.,
HRF Estimation in fMRI Data With an Unknown Drift Matrix by Iterative Minimization of the Kullback-Leibler Divergence,
MedImg(31), No. 2, February 2012, pp. 192-206.
IEEE DOI 1202
BibRef

Shah, A., Seghouane, A.K.[Abd-Krim],
An Integrated Framework for Joint HRF and Drift Estimation and HbO/HbR Signal Improvement in fNIRS Data,
MedImg(33), No. 11, November 2014, pp. 2086-2097.
IEEE DOI 1411
brain BibRef

Seghouane, A.K.[Abd-Krim], Ong, J.L.[Ju Lynn],
A Bayesian model selection approach to fMRI activation detection,
ICIP10(4401-4404).
IEEE DOI 1009
BibRef

Kim, T.S.[Taek Soo], Lee, J.[Jongho], Lee, J.H.[Jin Hyung], Glover, G.H.[Gary H.], Pauly, J.M.[John M.],
Analysis of the BOLD characteristics in pass-band bSSFP fMRI,
IJIST(22), No. 1, March 2012, pp. 23-32.
DOI Link 1202
BibRef

Wang, Z.[Ze], Li, Z.J.[Zheng-Jun], Pluta, J.[John], Detre, J.A.[John A.],
Improving fMRI activation detection sensitivity using intervoxel coherence mapping,
IJIST(22), No. 1, March 2012, pp. 33-36.
DOI Link 1202
BibRef

Jang, J.H.[Joon Hwan], Yun, J.Y.[Je-Yeon], Jung, W.H.[Wi Hoon], Shim, G.[Geumsook], Byun, M.S.[Min Soo], Hwang, J.Y.[Jae Yeon], Kim, S.N.[Sung Nyun], Choi, C.H.[Chi-Hoon], Kwon, J.S.[Jun Soo],
The impact of genetic variation in COMT and BDNF on resting-state functional connectivity,
IJIST(22), No. 1, March 2012, pp. 97-102.
DOI Link 1202
catechol-O-methyl transferase. brain-derived neurotrophic factor. BibRef

Hu, X., Li, K., Han, J., Hua, X., Guo, L., Liu, T.,
Bridging the Semantic Gap via Functional Brain Imaging,
MultMed(14), No. 2, 2012, pp. 314-325.
IEEE DOI 1204
BibRef

Anderson, M.,
This is your brain on fMRI,
Spectrum(49), No. 5, May 2012, pp. 25-26.
IEEE DOI 1202
Geek Life paper BibRef

Kim, Y.H.[Yong-Hwan], Lee, J.H.[Jong-Hwan],
Group inference of default-mode networks from functional magnetic resonance imaging data: comparison of random- and mixed-effects group statistics,
IJIST(22), No. 2, June 2012, pp. 121-131.
DOI Link 1202
BibRef

Ng, B., Hamarneh, G., Abugharbieh, R.,
Modeling Brain Activation in fMRI Using Group MRF,
MedImg(31), No. 5, May 2012, pp. 1113-1123.
IEEE DOI 1202
BibRef

Cassidy, B., Long, C.J., Rae, C., Solo, V.,
Identifying fMRI Model Violations With Lagrange Multiplier Tests,
MedImg(31), No. 7, July 2012, pp. 1481-1492.
IEEE DOI 1208
BibRef

Cassidy, B., Rae, C., Solo, V.,
Brain Activity: Connectivity, Sparsity, and Mutual Information,
MedImg(34), No. 4, April 2015, pp. 846-860.
IEEE DOI 1504
Analytical models BibRef

Cassidy, B., Bowman, F.D., Rae, C., Solo, V.,
On the Reliability of Individual Brain Activity Networks,
MedImg(37), No. 2, February 2018, pp. 649-662.
IEEE DOI 1802
Biomedical imaging, Reliability, Spatial resolution, Spatiotemporal phenomena, Brain modeling, topology BibRef

Jenatton, R.[Rodolphe], Gramfort, A.[Alexandre], Michel, V.[Vincent], Obozinski, G.[Guillaume], Eger, E.[Evelyn], Bach, F.[Francis], Thirion, B.[Bertrand],
Multiscale Mining of fMRI Data with Hierarchical Structured Sparsity,
SIIMS(5), No. 3 2012, pp. 835-856.
DOI Link 1208
BibRef

Khaliq, A.A.[Amir A.], Qureshi, I.M.[Ijaz M.], Shah, J.A.[Jawad A.],
Unmixing functional magnetic resonance imaging data using matrix factorization,
IJIST(22), No. 4, December 2012, pp. 195-199.
DOI Link 1211
BibRef

Chaari, L.[Lotfi], Vincent, T.[Thomas], Forbes, F.[Florence], Dojat, M., Ciuciu, P.[Philippe],
Fast Joint Detection-Estimation of Evoked Brain Activity in Event-Related fMRI Using a Variational Approach,
MedImg(32), No. 5, May 2013, pp. 821-837.
IEEE DOI 1305
BibRef
Earlier: A1, A3, A2, A5, Only:
Robust voxel-wise joint detection estimation of brain activity in fMRI,
ICIP12(1273-1276).
IEEE DOI 1302
BibRef

Ahmad, F.[Fayyaz], Lee, N.[Namgil], Kim, E.[Eunwoo], Kim, S.H.[Sung-Ho], Park, H.W.[Hyun-Wook],
A shrinkage method for causal network detection of brain regions,
IJIST(23), No. 2, 2013, pp. 140-146.
DOI Link fMRI, regions of interest, VAR model, shrinkage, partial correlation 1307
BibRef

Nagahara, S.[Shizue], Oida, T.[Takenori], Kobayashi, T.[Tetsuo],
An Explanation of Signal Changes in DW-fMRI: Monte Carlo Simulation Study of Restricted Diffusion of Water Molecules Using 3D and Two-Compartment Cortical Cell Models,
IEICE(E96-D), No. 6, June 2013, pp. 1387-1393.
WWW Link. 1306
BibRef

Yeh, M.Y.[Mei-Yu], Wu, C.W.[Changwei W.], Kuan, W.C.[Wan-Chun], Wei, P.S.[Pei-Shan], Wan, Y.L.[Yung-Liang], Wai, Y.Y.[Yau-Yau], Weng, H.H.[Hsu-Huei], Liu, H.L.[Ho-Ling],
Variations in BOLD response latency estimated from event-related fMRI at 3T: Comparisons between gradient-echo and Spin-echo,
IJIST(23), No. 3, 2013, pp. 215-221.
DOI Link 1309
functional MRI BibRef

Plumpton, C.O.[Catrin Oliver],
Semi-supervised ensemble update strategies for on-line classification of fMRI data,
PRL(37), No. 1, 2014, pp. 172-177.
Elsevier DOI 1402
Semi-supervised learning BibRef

Kim, D.C.[Dong-Chan], Jung, Y.J.[Yong Ju], Han, Y.[Yeji], Choi, J.[Joonsung], Kim, E.[Eunwoo], Jeong, B.S.[Bum-Seok], Ro, Y.M.[Yong Man], Park, H.W.[Hyun-Wook],
fMRI analysis of excessive binocular disparity on the human brain,
IJIST(24), No. 1, 2014, pp. 94-102.
DOI Link 1403
intraparietal sulcus BibRef

Bruce, I.P., Rowe, D.B.,
Quantifying the Statistical Impact of GRAPPA in fcMRI Data With a Real-Valued Isomorphism,
MedImg(33), No. 2, February 2014, pp. 495-503.
IEEE DOI 1403
biomedical MRI BibRef

Chou, C.A., Kampa, K., Mehta, S.H., Tungaraza, R.F., Chaovalitwongse, W.A., Grabowski, T.J.,
Voxel Selection Framework in Multi-Voxel Pattern Analysis of fMRI Data for Prediction of Neural Response to Visual Stimuli,
MedImg(33), No. 4, April 2014, pp. 925-934.
IEEE DOI 1404
Accuracy BibRef

Li, X.F.[Xing-Feng],
Functional Magnetic Resonance Imaging Processing,

Hinds, O.[Oliver], Wighton, P.[Paul], Tisdall, M.D.[M. Dylan], Hess, A.[Aaron], Breiter, H.[Hans], van der Kouwe, A.J.W.[André J.W.],
Neurofeedback using functional spectroscopy,
IJIST(24), No. 2, 2014, pp. 138-148.
DOI Link 1405
biofeedback, spectroscopy, fMRI BibRef

Ting, C.M., Seghouane, A.K., Salleh, S.H., Noor, A.M.,
Estimating Effective Connectivity from fMRI Data Using Factor-based Subspace Autoregressive Models,
SPLetters(22), No. 6, June 2015, pp. 757-761.
IEEE DOI 1411
Brain modeling BibRef

Song, X.[Xiaomu], Panych, L.P.[Lawrence P.], Chou, Y.H.[Ying-Hui], Chen, N.K.[Nan-Kuei],
A study of long-term fMRI reproducibility using data-driven analysis methods,
IJIST(24), No. 4, 2014, pp. 339-349.
DOI Link 1411
reproducibility, wavelet, support vector machine, quantitative fMRI BibRef

Ramezani, M., Marble, K., Trang, H., Johnsrude, I.S., Abolmaesumi, P.,
Joint Sparse Representation of Brain Activity Patterns in Multi-Task fMRI Data,
MedImg(34), No. 1, January 2015, pp. 2-12.
IEEE DOI 1502
biomedical MRI BibRef

Abolghasemi, V.[Vahid], Ferdowsi, S.[Saideh], Sanei, S.[Saeid],
Fast and incoherent dictionary learning algorithms with application to fMRI,
SIViP(9), No. 1, January 2015, pp. 147-158.
WWW Link. 1503
BibRef

Sreenivasan, K.R., Havlicek, M., Deshpande, G.,
Nonparametric Hemodynamic Deconvolution of fMRI Using Homomorphic Filtering,
MedImg(34), No. 5, May 2015, pp. 1155-1163.
IEEE DOI 1505
Biological system modeling BibRef

Afshin-Pour, B., Shams, S.M., Strother, S.,
A Hybrid LDA+gCCA Model for fMRI Data Classification and Visualization,
MedImg(34), No. 5, May 2015, pp. 1031-1041.
IEEE DOI 1505
Accuracy BibRef

Huang, J.[Jie], Zhu, D.C.[David C.],
Exploring human brain neuronal currents with phase MRI,
IJIST(25), No. 2, 2015, pp. 172-178.
DOI Link 1506
neuronal current, phase MRI, ncMRI, BOLD BibRef

Han, J., Chen, C., Shao, L., Hu, X., Han, J., Liu, T.,
Learning Computational Models of Video Memorability from fMRI Brain Imaging,
Cyber(45), No. 8, August 2015, pp. 1692-1703.
IEEE DOI 1506
Brain models BibRef

Dahne, S., Bieszmann, F., Samek, W., Haufe, S., Goltz, D., Gundlach, C., Villringer, A., Fazli, S., Muller, K.,
Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data,
PIEEE(103), No. 9, September 2015, pp. 1507-1530.
IEEE DOI 1509
Brain models BibRef

Al-Baddai, S., Al-Subari, K., Tome, A.M., Volberg, G., Lang, E.W.,
A Combined EMD-ICA Analysis of Simultaneously Register EEG-fMRI Data,
BMVA(2015), No. 2, 2015, pp. 1-15.
PDF File. 1509
BibRef

Han, J., Ji, X., Hu, X., Guo, L., Liu, T.,
Arousal Recognition Using Audio-Visual Features and FMRI-Based Brain Response,
AffCom(6), No. 4, October 2015, pp. 337-347.
IEEE DOI 1512
Behavioral science BibRef

d'Souza, D.V.[Dany V.], Auer, T.[Tibor], Frahm, J.[Jens], Strasburger, H.[Hans], Lee, B.B.[Barry B.],
Dependence of chromatic responses in V1 on visual field eccentricity and spatial frequency: an fMRI study,
JOSA-A(33), No. 3, March 2016, pp. A53-A64.
DOI Link 1603
Color vision; Vision system neurophysiology ; Psychophysics BibRef

Park, J.W.[Jang-Woo], Kim, Y.T.[Yang-Tae], Yun, B.J.[Byoung-Ju], Jin, S.U.[Sung-Uk], Lee, S.H.[Sang-Hoon], Ahn, S.H.[Shi-Hyun], Min, Y.[Yusun], Jung, T.D.[Tae-Du], Lee, H.J.[Hui Joong], Chang, Y.M.[Yong-Min],
Stereoscopic 3D objects evoke stronger saliency for nonverbal working memory: An fMRI study,
IJIST(26), No. 1, 2016, pp. 76-84.
DOI Link 1604
stereoscopic objects BibRef

Calhoun, V.D., Adali, T.,
Time-Varying Brain Connectivity in fMRI Data: Whole-brain data-driven approaches for capturing and characterizing dynamic states,
SPMag(33), No. 3, May 2016, pp. 52-66.
IEEE DOI 1605
Big data BibRef

Kumar, A., Lin, F., Rajapakse, J.C.,
Mixed Spectrum Analysis on fMRI Time-Series,
MedImg(35), No. 6, June 2016, pp. 1555-1564.
IEEE DOI 1606
Brain models BibRef

Ahmad, F.[Fayyaz], Hussain, A.[Attique], Chaudhary, S.U.[Safee Ullah], Ahmad, I.[Iftikhar], Ramay, S.M.[Shahid M.],
A novel method for detection of voxels for decision making: An fMRI study,
IJIST(26), No. 2, 2016, pp. 163-167.
DOI Link 1606
fMRI, cluster analysis, Brodmann areas, SPM BibRef

Wen, X.T.[Xiao-Tong], Kang, M.X.[Ming-Xuan], Yao, L.[Li], Zhao, X.J.[Xiao-Jie],
Real-time ballistocardiographic artifact reduction using the k-teager energy operator detector and multi-channel referenced adaptive noise cancelling,
IJIST(26), No. 3, 2016, pp. 209-215.
DOI Link 1609
EEG-fMRI BibRef

Atluri, G., MacDonald, III, A., Lim, K.O., Kumar, V.,
The Brain-Network Paradigm: Using Functional Imaging Data to Study How the Brain Works,
Computer(49), No. 10, October 2016, pp. 65-71.
IEEE DOI 1609
biomedical MRI BibRef

Zhang, J.J.[Jian-Jia], Zhou, L.P.[Lu-Ping], Wang, L.[Lei],
Subject-adaptive Integration of Multiple SICE Brain Networks with Different Sparsity,
PR(63), No. 1, 2017, pp. 642-652.
Elsevier DOI 1612
Brain network integration BibRef

Ahmad, F.[Fayyaz], Ahmad, I.[Iftikhar], Nisa, Z.[Zaibun], Ramay, S.M.[Shahid Mahmood],
Exploration of connectivity with SEM: An fMRI study of resting state,
IJIST(26), No. 4, 2016, pp. 264-269.
DOI Link 1701
functional magnetic resonance imaging BibRef

Seghouane, A.K., Iqbal, A.,
Sequential Dictionary Learning From Correlated Data: Application to fMRI Data Analysis,
IP(26), No. 6, June 2017, pp. 3002-3015.
IEEE DOI 1705
Algorithm design and analysis, Approximation algorithms, Brain modeling, Correlation, Data analysis, Dictionaries, Image coding, Functional magnetic resonance imaging (fMRI), correlation, dictionary learning, regularization, sequential update, sparsity See also Approach for Sequential Dictionary Learning in Nonuniform Noise, An. BibRef

Seghouane, A.K., Iqbal, A.,
Basis Expansion Approaches for Regularized Sequential Dictionary Learning Algorithms With Enforced Sparsity for fMRI Data Analysis,
MedImg(36), No. 9, September 2017, pp. 1796-1807.
IEEE DOI 1709
BibRef
And:
Learning dictionaries from correlated data: Application to fMRI data analysis,
ICIP16(2340-2344)
IEEE DOI 1610
biomedical MRI, data analysis, medical signal processing, basis expansion approach, classical dictionary learning algorithm, fMRI data analysis, functional magnetic resonance imaging, learned dictionary atom, BibRef

Seghouane, A.K., Iqbal, A.,
CSMSDL: A common sequential dictionary learning algorithm for multi-subject FMRI data sets analysis,
ICIP17(4113-4117)
IEEE DOI 1803
biomedical MRI, data analysis, medical image processing, CSMSDL, common sequential dictionary learning algorithm, sparsity BibRef

Hu, C., Reeves, S., Peters, D.C., Twieg, D.,
An Efficient Reconstruction Algorithm Based on the Alternating Direction Method of Multipliers for Joint Estimation of R_2^* and Off-Resonance in fMRI,
MedImg(36), No. 6, June 2017, pp. 1326-1336.
IEEE DOI 1706
Cost function, Data models, Image reconstruction, Imaging, Nonlinear distortion, Trajectory, ADMM, BOLD fMRI, geometric distortion, rosette BibRef

Tang, D.H.[Dong-Hui], Tao, S.[Shuang], Ma, J.[Jinlian], Hu, P.J.[Pei-Jun], Long, D.[Dan], Wang, J.[Jun], Kong, D.X.[De-Xing],
The effect of short cardio on inhibitory control ability of obese people,
IJIST(27), No. 4, 2017, pp. 345-353.
DOI Link 1712
functional connectivity (FC), functional magnetic resonance, inhibitory control, obesity, regional homogeneity (ReHo) BibRef

Bolton, T.A.W., Tarun, A., Sterpenich, V., Schwartz, S., van de Ville, D.,
Interactions Between Large-Scale Functional Brain Networks are Captured by Sparse Coupled HMMs,
MedImg(37), No. 1, January 2018, pp. 230-240.
IEEE DOI 1801
biomedical MRI, brain, deconvolution, hidden Markov models, medical image processing, complex temporal dynamics, total activation BibRef

Olafsson, V.T., Noll, D.C., Fessler, J.A.,
Fast Spatial Resolution Analysis of Quadratic Penalized Least-Squares Image Reconstruction With Separate Real and Imaginary Roughness Penalty: Application to fMRI,
MedImg(37), No. 2, February 2018, pp. 604-614.
IEEE DOI 1802
Cost function, Discrete Fourier transforms, Image reconstruction, Spatial resolution, Localimpulse response, functional MRI (fMRI), separate real and imaginary regularization BibRef

Wettenhovi, V.V.[Ville-Veikko], Kolehmainen, V.[Ville], Huttunen, J.[Joanna], Kettunen, M.[Mikko], Gröhn, O.[Olli], Vauhkonen, M.[Marko],
State Estimation with Structural Priors in fMRI,
JMIV(60), No. 2, February 2018, pp. 174-188.
Springer DOI 1802
BibRef

Fang, J., Xu, C., Zille, P., Lin, D., Deng, H.W., Calhoun, V.D., Wang, Y.P.,
Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation,
MedImg(37), No. 4, April 2018, pp. 860-870.
IEEE DOI 1804
Computational efficiency, Correlation, Genetics, Neuroimaging, Testing, Imaging genetics, SNP, distance correlation, fMRI, projected distance correlation BibRef

Ting, C.M., Ombao, H., Samdin, S.B., Salleh, S.H.,
Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models,
MedImg(37), No. 4, April 2018, pp. 1011-1023.
IEEE DOI 1804
Brain modeling, Covariance matrices, Estimation, Hidden Markov models, Load modeling, Reactive power, large VAR models BibRef

Chen, C.X.[Chun-Xiao], Wang, J.[Jing], Lu, X.[Xiong], Liu, Y.P.[Yu-Pin], Chen, X.[Xin],
Assessment of 3DTV-related fatigue with resting-state fMRI,
SP:IC(64), 2018, pp. 99-106.
Elsevier DOI 1804
3DTV, Resting state, ALFF, Visual fatigue, Recognition function, Modularity BibRef

Cai, B., Zille, P., Stephen, J.M., Wilson, T.W., Calhoun, V.D., Wang, Y.P.,
Estimation of Dynamic Sparse Connectivity Patterns From Resting State fMRI,
MedImg(37), No. 5, May 2018, pp. 1224-1234.
IEEE DOI 1805
Brain modeling, Correlation, Estimation, Minimization, Time series analysis, Sparse model, brain development, resting state fMRI BibRef

Huang, W., Bolton, T.A.W., Medaglia, J.D., Bassett, D.S., Ribeiro, A., van de Ville, D.,
A Graph Signal Processing Perspective on Functional Brain Imaging,
PIEEE(106), No. 5, May 2018, pp. 868-885.
IEEE DOI 1805
Adaptive systems, Brain modeling, Extraterrestrial measurements, Graph theory, Magnetic resonance imaging, Neuroimaging, neuroimaging BibRef

Solo, V., Poline, J., Lindquist, M.A., Simpson, S.L., Bowman, F.D., Chung, M.K., Cassidy, B.,
Connectivity in fMRI: Blind Spots and Breakthroughs,
MedImg(37), No. 7, July 2018, pp. 1537-1550.
IEEE DOI 1808
biomedical MRI, brain, diseases, neurophysiology, stochastic processes, fMRI, functional brain network analysis, system identification BibRef

Huang, H., Hu, X., Zhao, Y., Makkie, M., Dong, Q., Zhao, S., Guo, L., Liu, T.,
Modeling Task fMRI Data Via Deep Convolutional Autoencoder,
MedImg(37), No. 7, July 2018, pp. 1551-1561.
IEEE DOI 1808
biomedical MRI, brain, independent component analysis, learning (artificial intelligence), medical image processing, unsupervised BibRef

Zöller, D.M., Bolton, T.A.W., Karahanoglu, F.I., Eliez, S., Schaer, M., Van De Ville, D.,
Robust Recovery of Temporal Overlap Between Network Activity Using Transient-Informed Spatio-Temporal Regression,
MedImg(38), No. 1, January 2019, pp. 291-302.
IEEE DOI 1901
Functional magnetic resonance imaging, Technological innovation, Transient analysis, Brain, spatio-temporal regression BibRef

Farouj, Y., Karahanoglu, F.I., van de Ville, D.,
Deconvolution of Sustained Neural Activity From Large-Scale Calcium Imaging Data,
MedImg(39), No. 4, April 2020, pp. 1094-1103.
IEEE DOI 2004
Calcium, Deconvolution, Imaging, Noise measurement, Numerical models, Transient analysis, Neurons, Temporal deconvolution, l1-minimization BibRef

Seghouane, A., Shokouhi, N.,
Consistent Estimation of Dimensionality for Data-Driven Methods in fMRI Analysis,
MedImg(38), No. 2, February 2019, pp. 493-503.
IEEE DOI 1902
Functional magnetic resonance imaging, Eigenvalues and eigenfunctions, Covariance matrices, Data models, ICA BibRef

Wang, H.[Han], Zhao, S.J.[Shi-Jie], Dong, Q.L.[Qing-Lin], Cui, Y.[Yan], Chen, Y.W.[Yao-Wu], Han, J.W.[Jun-Wei], Xie, L.[Li], Liu, T.M.[Tian-Ming],
Recognizing Brain States Using Deep Sparse Recurrent Neural Network,
MedImg(38), No. 4, April 2019, pp. 1058-1068.
IEEE DOI 1904
Task analysis, Brain modeling, Recurrent neural networks, Functional magnetic resonance imaging, Logic gates, Data models, brain networks BibRef

Zhao, S.J.[Shi-Jie], Cui, Y.[Yan], Chen, Y.W.[Yao-Wu], Zhang, X.[Xin], Zhang, W.[Wei], Liu, H.[Huan], Han, J.W.[Jun-Wei], Guo, L.[Lei], Xie, L.[Li], Liu, T.M.[Tian-Ming],
Exploring Brain Hemodynamic Response Patterns via Deep Recurrent Autoencoder,
MBIA19(66-74).
Springer DOI 1912
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Bhinge, S., Mowakeaa, R., Calhoun, V.D., Adali, T.,
Extraction of Time-Varying Spatiotemporal Networks Using Parameter-Tuned Constrained IVA,
MedImg(38), No. 7, July 2019, pp. 1715-1725.
IEEE DOI 1907
Functional magnetic resonance imaging, Feature extraction, Estimation, Data models, Adaptation models, fMRI analysis BibRef

Raut, Y.[Yudhishthir], Gamad, R.S., Bansod, P.P.,
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Liao, W., Li, J., Ji, G., Wu, G., Long, Z., Xu, Q., Duan, X., Cui, Q., Biswal, B.B., Chen, H.,
Endless Fluctuations: Temporal Dynamics of the Amplitude of Low Frequency Fluctuations,
MedImg(38), No. 11, November 2019, pp. 2523-2532.
IEEE DOI 1911
Functional magnetic resonance imaging, Brain, Time series analysis, Fluctuations, Neuromodulation, temporal dynamics BibRef

Hourani, O.[Osama], Charkari, N.M.[Nasrollah Moghadam], Jalili, S.[Saeed],
Voxel selection framework based on meta-heuristic search and mutual information for brain decoding,
IJIST(29), No. 4, 2019, pp. 663-676.
DOI Link 1911
classification, computer heuristics, functional magnetic resonance imaging, information theory, voxel selection BibRef

Ruttorf, M., Kristensen, S., Schad, L.R., Almeida, J.,
Transcranial Direct Current Stimulation Alters Functional Network Structure in Humans: A Graph Theoretical Analysis,
MedImg(38), No. 12, December 2019, pp. 2829-2837.
IEEE DOI 1912
Tools, Functional magnetic resonance imaging, Electronics packaging, Brain, Task analysis, transcranial direct current stimulation BibRef

Almodóvar-Rivera, I., Maitra, R.,
Fast Adaptive Smoothing and Thresholding for Improved Activation Detection in Low-Signal fMRI,
MedImg(38), No. 12, December 2019, pp. 2821-2828.
IEEE DOI 1912
Smoothing methods, Thresholding (Imaging), Functional magnetic resonance imaging, Correlation, Limiting, TFCE BibRef

Wang, T.[Ting], Wu, X.[Xi], Jiang, J.[Jiefeng], Liu, C.[Chang], Zhu, M.[Ming],
Functional neural interactions during adaptive reward learning: An functional magnetic resonance imaging study,
IJIST(30), No. 1, 2020, pp. 92-103.
DOI Link 2002
adaptive reward learning, functional magnetic resonance imaging, learning rate, psychophysiological interaction analysis BibRef

Wang, M., Zhang, D., Huang, J., Yap, P., Shen, D., Liu, M.,
Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation,
MedImg(39), No. 3, March 2020, pp. 644-655.
IEEE DOI 2004
Functional magnetic resonance imaging, Diseases, Data models, Sparse matrices, Matrix decomposition, Medical diagnosis, fMRI BibRef

Dai, M., Zhang, Z., Srivastava, A.,
Analyzing Dynamical Brain Functional Connectivity as Trajectories on Space of Covariance Matrices,
MedImg(39), No. 3, March 2020, pp. 611-620.
IEEE DOI 2004
Trajectory, Covariance matrices, Measurement, Functional magnetic resonance imaging, Task analysis, dimension reduction BibRef

Gupta, K.O., Chatur, P.N.,
Gradient self-weighting linear collaborative discriminant regression classification for human cognitive states classification,
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Kuang, L., Lin, Q., Gong, X., Cong, F., Wang, Y., Calhoun, V.D.,
Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint,
MedImg(39), No. 4, April 2020, pp. 844-853.
IEEE DOI 2004
Functional magnetic resonance imaging, Delay effects, Frequency-domain analysis, Data models, Spatiotemporal phenomena, spatiotemporal constraints BibRef

Xiao, L., Wang, J., Kassani, P.H., Zhang, Y., Bai, Y., Stephen, J.M., Wilson, T.W., Calhoun, V.D., Wang, Y.,
Multi-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data,
MedImg(39), No. 5, May 2020, pp. 1746-1758.
IEEE DOI 2005
Functional magnetic resonance imaging, Correlation, Learning systems, Sparse matrices, Feature extraction, similarity matrix BibRef

Sauvage, J., Porée, J., Rabut, C., Férin, G., Flesch, M., Rosinski, B., Nguyen-Dinh, A., Tanter, M., Pernot, M., Deffieux, T.,
4D Functional Imaging of the Rat Brain Using a Large Aperture Row-Column Array,
MedImg(39), No. 6, June 2020, pp. 1884-1893.
IEEE DOI 2006
3D flow imaging, functional imaging, matrix array, ultrafast ultrasound BibRef

Sundaram, P., Luessi, M., Bianciardi, M., Stufflebeam, S., Hämäläinen, M., Solo, V.,
Individual Resting-State Brain Networks Enabled by Massive Multivariate Conditional Mutual Information,
MedImg(39), No. 6, June 2020, pp. 1957-1966.
IEEE DOI 2006
Functional connectivity, multivariate, conditional mutual information, graphical model, fMRI, brain networks BibRef

Cai, J., Wang, Y., Liu, A., McKeown, M.J., Wang, Z.J.,
Novel Regional Activity Representation With Constrained Canonical Correlation Analysis for Brain Connectivity Network Estimation,
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IEEE DOI 2007
Brain modeling, Functional magnetic resonance imaging, Correlation, Clustering algorithms, Mathematical model, fMRI BibRef

Huang, J., Zhou, L., Wang, L., Zhang, D.,
Attention-Diffusion-Bilinear Neural Network for Brain Network Analysis,
MedImg(39), No. 7, July 2020, pp. 2541-2552.
IEEE DOI 2007
Functional magnetic resonance imaging, Diffusion tensor imaging, Feature extraction, epilepsy BibRef

Ni, X.Y.[Xiu-Yan], Gao, T.[Tian], Wu, T.T.[Ting-Ting], Fan, J.[Jin], Chen, C.[Chao],
Learning Human Cognition via fMRI Analysis Using 3d Cnn and Graph Neural Network,
MBIA19(93-101).
Springer DOI 1912
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Wang, Z.[Ze],
Mapping the Spatio-temporal Functional Coherence in the Resting Brain,
MBIA19(39-48).
Springer DOI 1912
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Suo, W.[Wei], Hu, X.T.[Xin-Tao], Yan, B.W.[Bo-Wei], Sun, M.Y.[Meng-Yang], Guo, L.[Lei], Han, J.W.[Jun-Wei], Liu, T.M.[Tian-Ming],
3d Convolutional Long-Short Term Memory Network for Spatiotemporal Modeling of fMRI Data,
MBIA19(75-83).
Springer DOI 1912
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Lin, Y., Li, J., Wang, H.,
DCNN-GAN: Reconstructing Realistic Image from fMRI,
MVA19(1-6)
DOI Link 1911
biomedical MRI, brain, convolutional neural nets, feature extraction, image reconstruction, Training BibRef

Wang, T., Kamata, S.,
Classification of Structural MRI Images in ADHD Using 3D Fractal Dimension Complexity Map,
ICIP19(215-219)
IEEE DOI 1910
Attention deficit hyperactive disorder, 3D fractal dimension based complexity map, Structural MRI, 3D convolutional neural network BibRef

Yamin, A., Dayan, M., Squarcina, L., Brambilla, P., Murino, V., Diwadkar, V., Sona, D.,
Analysis of Dynamic Brain Connectivity Through Geodesic Clustering,
CIAP19(II:640-648).
Springer DOI 1909
dynamic functional connectivity. BibRef

Brahim, A.[Abdelbasset], El Hassani, M.H.[Mehdi Hajjam], Farrugia, N.[Nicolas],
Classification of Autism Spectrum Disorder Through the Graph Fourier Transform of fMRI Temporal Signals Projected on Structural Connectome,
CAIPWS19(45-55).
Springer DOI 1909
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Li, D., Du, C., Huang, L., Chen, Z., He, H.,
Multi-label Semantic Decoding from Human Brain Activity,
ICPR18(3796-3801)
IEEE DOI 1812
Semantics, Functional magnetic resonance imaging, Decoding, Brain modeling, Neurons, Training BibRef

Koutras, P., Panagiotaropoulou, G., Tsiami, A., Maragos, P.,
Audio-Visual Temporal Saliency Modeling Validated by fMRI Data,
Cognitive18(2081-208110)
IEEE DOI 1812
Videos, Visualization, Computational modeling, Brain modeling, Predictive models, Functional magnetic resonance imaging, Data models BibRef

Tu, T., Koss, J., Sajda, P.,
Relating Deep Neural Network Representations to EEG-fMRI Spatiotemporal Dynamics in a Perceptual Decision-Making Task,
Cognitive18(2066-20666)
IEEE DOI 1812
Electroencephalography, Face, Functional magnetic resonance imaging, Training, Automobiles, Spatiotemporal phenomena BibRef

Vergara, V.M., Yu, Q., Calhoun, V.D.,
Graph Modularity and Randomness Measures: A Comparative Study,
Southwest18(33-36)
IEEE DOI 1809
Correlation, Toy manufacturing industry, Functional magnetic resonance imaging, Graph theory, functional connectivity BibRef

Parmar, H.S., Liu, X., Xie, H., Nutter, B., Mitra, S., Long, R., Antani, S.,
f-Sim: A quasi-realistic fMRI simulation toolbox using digital brain phantom and modeled noise,
Southwest18(37-40)
IEEE DOI 1809
Functional magnetic resonance imaging, Time series analysis, Task analysis, Mathematical model, Brain modeling, Correlation, functional connectivity patterns BibRef

Liu, X., Xie, H., Nutter, B., Mitra, S.,
High-homogeneity functional parcellation of human brain for investigating robust functional connectivity,
Southwest18(1-4)
IEEE DOI 1809
Functional magnetic resonance imaging, Correlation, Bandwidth, Brain, Clustering algorithms, Time series analysis, rsfMRI, homogeneity BibRef

Yaesoubi, M., Silva, R.F., Calhoun, V.D., Calhoun, V.D.,
In-between and cross-frequency dependence-based summarization of resting-state fMRI data,
Southwest18(93-96)
IEEE DOI 1809
Frequency modulation, Functional magnetic resonance imaging, Time-frequency analysis, Transforms, Oscillators, Bandwidth, Canonical correlation analysis BibRef

Qadar, M.A., Seghouane, A.,
PCCA: A Projection CCA Method for Effective FMRI Data Analysis,
ICIP18(3388-3392)
IEEE DOI 1809
Functional magnetic resonance imaging, Discrete cosine transforms, Correlation, Data analysis, Standards, regularization BibRef

Rahimpour, A., Dadashi, A., Soltanian-Zadeh, H., Setarehdan, S.K.,
Classification of fNIRS based brain hemodynamic response to mental arithmetic tasks,
IPRIA17(113-117)
IEEE DOI 1712
brain, haemodynamics, infrared spectroscopy, medical signal processing, principal component analysis, near-infrared spectroscopy BibRef

Zhou, Y.[Yu], Mei, X.[Xue], Li, W.W.[Wei-Wei], Huang, J.[Jin],
Classification of resting-state fMRI datasets based on graph kernels,
ICIVC17(665-669)
IEEE DOI 1708
Kernel, Support vector machines, dynamic FNC, fMRI, graph encoding, graph kernels, static, FNC BibRef

Faria, F.A., Cappabianco, F.A.[Fabio A.], Li, C.S.R.[Chiang-Shan R.], Ide, J.S.,
Information fusion for cocaine dependence recognition using fMRI,
ICPR16(1107-1112)
IEEE DOI 1705
Complexity theory, Correlation, Drugs, Feature extraction, Learning systems, Magnetic resonance imaging, Pattern, recognition BibRef

Kim, J.H.[Jung Hwan], Taylor, A.[Amanda], Ress, D.[David],
Simple Signed-Distance Function Depth Calculation Applied to Measurement of the fMRI BOLD Hemodynamic Response Function in Human Visual Cortex,
CompIMAGE16(216-228).
Springer DOI 1704
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Melzi, S.[Simone], Mella, A.[Alessandro], Squarcina, L.[Letizia], Bellani, M.[Marcella], Perlini, C.[Cinzia], Ruggeri, M.[Mirella], Altamura, C.A.[Carlo Alfredo], Brambilla, P.[Paolo], Castellani, U.[Umberto],
Functional Maps for Brain Classification on Spectral Domain,
SeSAME16(25-36).
Springer DOI 1703
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Dai, M., Zhang, Z., Srivastava, A.,
Testing Stationarity of Brain Functional Connectivity Using Change-Point Detection in fMRI Data,
DIFF-CV16(981-989)
IEEE DOI 1612
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Arya, Z.[Zobair], Griffanti, L.[Ludovica], Mackay, C.E.[Clare E.], Jenkinson, M.[Mark],
Iterative Dual LDA: A Novel Classification Algorithm for Resting State fMRI,
MLMI16(279-286).
Springer DOI 1611
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Wang, J.J.[Jian-Jia], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
Euler-Lagrange Network Dynamics,
EMMCVPR17(424-438).
Springer DOI 1805
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Wang, J.J.[Jian-Jia], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
Minimising Entropy Changes in Dynamic Network Evolution,
GbRPR17(255-265).
Springer DOI 1706
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fMRI Activation Network Analysis Using Bose-Einstein Entropy,
SSSPR16(218-228).
Springer DOI 1611
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Wang, L.[Lei], Li, D.P.[Dan-Ping], He, T.C.[Tian-Cheng], Wong, S.T.C.[Stephen T. C.], Xue, Z.[Zhong],
Transductive Maximum Margin Classification of ADHD Using Resting State fMRI,
MLMI16(221-228).
Springer DOI 1611
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Abrol, V., Sharma, P., Roohi, S.F., Sao, A.K., Kassim, A.A.,
Fast and robust FMRI unmixing using hierarchical dictionary learning,
ICIP16(714-718)
IEEE DOI 1610
Algorithm design and analysis BibRef

Panagiotaropoulou, G., Koutras, P., Katsamanis, A., Maragos, P., Zlatintsi, A., Protopapas, A., Karavasilis, E., Smyrnis, N.,
FMRI-based perceptual validation of a computational model for visual and auditory saliency in videos,
ICIP16(699-703)
IEEE DOI 1610
Brain modeling BibRef

Riaz, A.[Atif], Alonso, E.[Eduardo], Slabaugh, G.[Greg],
Phenotypic Integrated Framework for Classification of ADHD Using fMRI,
ICIAR16(217-225).
Springer DOI 1608
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Nuñez-Garcia, M.[Marta], Simpraga, S.[Sonja], Jurado, M.A.[Maria Angeles], Garolera, M.[Maite], Pueyo, R.[Roser], Igual, L.[Laura],
FADR: Functional-Anatomical Discriminative Regions for Rest fMRI Characterization,
MLMI15(61-68).
Springer DOI 1511
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de La Pava, I.[Iván], Mejía, J., Álvarez-Meza, A., Álvarez, M.A.[Mauricio A.], Orozco, Á.A.[Álvaro A.], Henao, Ó.A.[Óscar A.],
A Hierarchical K-Nearest Neighbor Approach for Volume of Tissue Activated Estimation,
CIARP16(125-133).
Springer DOI 1703
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de La Pava, I.[Iván], Gómez, V.[Viviana], Álvarez, M.A.[Mauricio A.], Henao, Ó.A.[Óscar A.], Daza-Santacoloma, G.[Genaro], Orozco, Á.A.[Álvaro A.],
A Gaussian Process Emulator for Estimating the Volume of Tissue Activated During Deep Brain Stimulation,
IbPRIA15(691-699).
Springer DOI 1506
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Du, W.[Wei], Fu, G.S.[Geng-Shen], Calhoun, V.D.[Vince D.], Adah, T.[Tulay],
Performance of complex-valued ICA algorithms for fMRI analysis: Importance of taking full diversity into account,
ICIP14(3612-3616)
IEEE DOI 1502
Algorithm design and analysis BibRef

Fu, G.S.[Geng-Shen], Du, W.[Wei], Adah, T.[Tulay],
Entropy rate estimation for vector processes: Application to complex FMRI analysis,
ICIP14(1867-1871)
IEEE DOI 1502
Entropy BibRef

Wang, L.[Lei], Zhang, J.J.[Jian-Jia], Zhou, L.P.[Lu-Ping], Tang, C., Li, W.Q.[Wan-Qing],
Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices,
ICCV15(4570-4578)
IEEE DOI 1602
Computer vision BibRef

Zhang, J.J.[Jian-Jia], Zhou, L.P.[Lu-Ping], Wang, L.[Lei], Li, W.Q.[Wan-Qing],
Exploring Compact Representation of SICE Matrices for Functional Brain Network Classification,
MLMI14(59-67).
Springer DOI 1410
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Hanson, E.A., Westlye, E., Lundervold, A.,
A PCA-based thresholding strategy for group studies of brain connectivity: with applications to resting state fMRI,
Southwest14(61-64)
IEEE DOI 1406
biomedical MRI BibRef

Ekmekci, O.[Omer], Firat, O.[Orhan], Ozay, M.[Mete], Oztekin, I.[Ilke], Vural, F.T.Y.[Fatos T.Yarman], Oztekin, U.[Uygar],
Mesh learning for object classification using fMRI measurements,
ICIP13(2631-2634)
IEEE DOI 1402
Functional Magnetic Resonance Imaging (fMRI) BibRef

Georgieva, P.[Petia], Nuntal, N.[Nuttapod], de la Torre, F.[Fernando],
Robust Principal Component Analysis for Improving Cognitive Brain States Discrimination from fMRI,
IbPRIA13(165-172).
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Maheshwari, H.K.[Harish Kumar], Siyal, M.Y.[Muhammad Yakoob],
Correntropy coefficient analysis of fMRI using reference model,
ICARCV12(396-400).
IEEE DOI 1304
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Khalid, M.U., Shah, A., Seghouane, A.,
Adaptive 2DCCA Based Approach for Improving Spatial Specificity of Activation Detection in Functional MRI,
DICTA12(1-6).
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Pedregosa, F.[Fabian], Cauvet, E.[Elodie], Varoquaux, G.[Gaël], Pallier, C.[Christophe], Thirion, B.[Bertrand], Gramfort, A.[Alexandre],
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predict severity of disease BibRef

Ruiz, M.J.[Mathieu J.], Hupé, J.M.[Jean-Michel], Dojat, M.[Michel],
Use of Pattern-Information Analysis in Vision Science: A Pragmatic Examination,
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MultiVoxel Pattern Analysis in fMRI BibRef

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MLMI12(184-192).
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MBIA12(51-60).
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Zhang, T.[Tuo], Guo, L.[Lei], Chen, H.[Hanbo], Hu, X.[Xintao], Li, K.[Kaiming], Liu, T.M.[Tian-Ming],
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Yan, J.W.[Jing-Wen], Risacher, S.L.[Shannon L.], Kim, S.[Sungeun], Simon, J.C.[Jacqueline C.], Li, T.[Taiyong], Wan, J.[Jing], Wang, H.[Hua], Huang, H.[Heng], Saykin, A.J.[Andrew J.], Shen, L.[Li],
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MBIA12(1-17).
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Baker, M.[Mary], Kapse, K.[Kushal], McMahon, A.[Allison], O'Boyle, M.[Michael],
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fMRI analysis. BibRef

Verdoolaege, G., Vlerick, L.[Leslie], Achten, E.[Eric],
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ICIP12(1245-1248).
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ICIP11(1589-1592).
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Liu, W.[Wei], Awate, S.P.[Suyash P.], Anderson, J.S.[Jeffrey S.], Yurgelun-Todd, D.[Deborah], Fletcher, P.T.[P. Thomas],
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Ng, B.[Bernard], Abugharbieh, R.[Rafeef],
Generalized group sparse classifiers with application in fMRI brain decoding,
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Functional Brain Mapping by Methods of Evolutionary Natural Selection,
CAIP11(II: 293-299).
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Fernandes, J.M.[José Maria], Tafula, S.[Sérgio], Silva Cunha, J.P.[João Paulo],
3D-Video-fMRI: 3D Motion Tracking in a 3T MRI Environment,
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Rustandi, I.[Indrayana],
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Estimation of the Ising field parameter thanks to the exact partition function,
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Chen, X.H.[Xiao-Hui], Wang, Z.J.[Z. Jane], McKeown, M.J.[Martin J.],
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ICIP10(589-592).
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Verdoolaege, G.[Geert], Rosseel, Y.[Yves],
Activation detection in event-related fMRI through clustering of wavelet distributions,
ICIP10(4393-4396).
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A Brain Computer Interface for Communication Using Real-Time fMRI,
ICPR10(3665-3669).
IEEE DOI 1008
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Emeriau, S., Blanchard, F., Poline, JB., Pierot, L., Bittar, E.,
Connectivity feature extraction for spatio-functional clustering of fMRI data,
IPTA10(38-43).
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Brain, Cortex, Brain Waves, EEG Analysis, Electroencephalogram .