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.,
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9808
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Unser, M.,
Analysis of functional magnetic resonance images by wavelet
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ICIP95(I: 633-636).
IEEE DOI
9510
BibRef
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Windischberger, C.[Christian],
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IJIST(10), No. 2, 1999, pp. 166-176.
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IEEE Top Reference.
0110
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0110
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0110
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0110
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Permutation testing made practical for functional magnetic resonance
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0110
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Kruggel, F.,
von Cramon, D.Y.,
Nonlinear regression analysis of the
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PRL(22), No. 11, September 2001, pp. 1247-1252.
Elsevier DOI
0108
BibRef
Solo, V.,
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Brown, E.,
A signal estimation approach to functional MRI,
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0110
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Long, C.J.,
Brown, E.N.,
Aminoff, E.,
Bar, M.,
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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],
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0110
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von Tscharner, V.,
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Specified-resolution wavelet analysis of activation patterns from BOLD
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IEEE Top Reference.
0110
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Otte, M.,
Elastic registration of fMRI data using Bezier-spline transformations,
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0110
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Aronen, H.J.,
Savolainen, S.,
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Visa, A.,
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0110
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Freire, L.,
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What is the best similarity measure for motion correction in fMRI time
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0206
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Lohmann, G.,
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0206
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Polydorides, N.,
Lionheart, W.R.B.,
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0208
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0301
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Hossein-Zadeh, G.A.,
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0306
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Hossein-Zadeh, G.A.,
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0308
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Jafari-Khouzani, K.,
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0505
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Jafari-Khouzani, K.,
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Penny, W.D.,
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0306
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Gautama, T.,
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0307
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Deleus, F.,
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0907
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Lindquist, M.A.[Martin A.],
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Lindquist, M.A.[Martin A.],
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0306
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Meyer, F.G.,
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0308
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Meyer, F.G.[Francois G.],
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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.,
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Benali, H.,
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0310
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Orchard, J.,
Greif, C.,
Golub, G.H.,
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0311
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Chen, S.,
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0403
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Woolrich, M.W.,
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0403
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Woolrich, M.W.,
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0501
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Woolrich, M.W.,
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Variational Bayes Inference of Spatial Mixture Models for Segmentation,
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IEEE DOI
0609
BibRef
Soltanian-Zadeh, H.,
Peck, D.J.,
Hearshen, D.O.,
Lajiness-O'Neill, R.R.,
Model-Independent Method for fMRI Analysis,
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0403
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Wink, A.M.,
Roerdink, J.B.J.M.,
Denoising Functional MR Images:
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MedImg(23), No. 3, March 2004, pp. 374-387.
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0403
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Beckmann, C.F.,
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0403
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Marrelec, G.,
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0409
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Liao, R.[Rui],
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0501
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CVBIA05(346-355).
Springer DOI
0601
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0501
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Faisan, S.,
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Unsupervised Learning and Mapping of Active Brain Functional MRI
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0501
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Earlier: A1, A2, A3, A5, Only:
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ICIP02(I: 880-883).
IEEE DOI
0210
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Brucher, M.[Matthieu],
Heinrich, C.[Christian],
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Armspach, J.P.[Jean-Paul],
Unsupervised Nonlinear Manifold Learning,
ICIP07(II: 109-112).
IEEE DOI
0709
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Bogorodzki, P.,
Rogowska, J.,
Yurgelun-Todd, D.A.,
Structural Group Classification Technique Based on Regional fMRI BOLD
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0501
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Sijbers, J.[Jan],
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Generalized Likelihood Ratio Tests for Complex fMRI Data: A Simulation
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0505
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den Dekker, A.J.,
Poot, D.H.J.,
Bos, R.,
Sijbers, J.,
Likelihood-Based Hypothesis Tests for Brain Activation Detection From
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0902
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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
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Turner, G.H.,
Twieg, D.B.,
Study of Temporal Stationarity and Spatial Consistency of fMRI Noise
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MedImg(24), No. 6, June 2005, pp. 712-718.
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0506
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Voultsidou, M.,
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Herrmann, J.M.,
Neural Networks Approach to Clustering of Activity in fMRI Data,
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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
Peeters, R.R.[Ronald R.],
Kornprobst, P.[Pierre],
Nikolova, M.[Mila],
Sunaert, S.[Stefan],
Vieville, T.[Thierry],
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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
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IJIST(14), No. 3, 2004, pp. 131-138.
DOI Link
0408
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Shijie, W.[Wang],
Limin, L.[Luo],
Weiping, Z.[Zhou],
Robust Ordering of Independent Spatial Components of fMRI Data Using
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ICIAR06(II: 672-679).
Springer DOI
0610
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Bannister, P.R.[Peter R.],
Brady, J.M.[J. Michael],
Jenkinson, M.[Mark],
Integrating temporal information with a non-rigid method of motion
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IVC(25), No. 3, March 2007, pp. 311-320.
Elsevier DOI
0701
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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:
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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.],
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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
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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.],
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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
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Thirion, B.,
Pinel, P.,
Tucholka, A.,
Roche, A.,
Ciuciu, P.,
Mangin, J.F.,
Poline, J.B.,
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IEEE DOI
0710
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Michel, V.[Vincent],
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Thirion, B.[Bertrand],
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MMBIA10(7-14).
IEEE DOI
1006
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Thirion, B.[Bertrand],
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IEEE DOI
0609
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Roche, A.[Alexis],
A Four-Dimensional Registration Algorithm With Application to Joint
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IEEE DOI
1108
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Tabelow, K.,
Polzehl, J.,
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Dyke, J.P.,
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Heier, L.A.,
Voss, H.U.,
Accurate Localization of Brain Activity in Presurgical fMRI by
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MedImg(27), No. 4, April 2008, pp. 531-537.
IEEE DOI
0804
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Rajapakse, J.C.,
Wang, Y.,
Zheng, X.,
Zhou, J.,
Probabilistic Framework for Brain Connectivity From Functional MR
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MedImg(27), No. 6, June 2008, pp. 825-833.
IEEE DOI
0711
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Tuan, A.S.[August S.],
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Differential transient MEG and fMRI responses to visual stimulation
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DOI Link
0806
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0804
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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
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IJIST(18), No. 1, 2008, pp. 29-41.
DOI Link
0806
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Yoo, S.S.[Seung-Schik],
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IJIST(18), No. 1, 2008, pp. 69-78.
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0806
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Novatnack, J.[John],
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Shokoufandeh, A.[Ali],
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Dickinson, S.J.[Sven J.],
Kantor, P.[Paul],
Bai, B.[Bing],
A generalized family of fixed-radius distribution-based distance
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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
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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,
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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
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CVPR06(I: 1076-1083).
IEEE DOI
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And: A1, A3, A4, A5, A6, A7, A2:
Control Theory and Fast Marching Methods for Brain Connectivity Mapping,
INRIARR-5845, 2006.
HTML Version.
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Thirion, B.[Bertrand],
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Application to the study of monkey vision,
INRIARR-4213, June 2001.
HTML Version.
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And:
Revisiting Non-Parametric Activation Detection on fMRI Time Series,
MMBIA01(xx-yy).
0110
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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
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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.
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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
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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
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IJIST(21), No. 2, June 2011, pp. 201-210.
DOI Link
1101
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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
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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
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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
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3DIM03(103-109).
IEEE DOI
0311
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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
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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
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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
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Khaliq, A.A.[Amir A.],
Qureshi, I.M.[Ijaz M.],
Shah, J.A.[Jawad A.],
Unmixing functional magnetic resonance imaging data using matrix
factorization,
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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
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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.M.[Xiao-Mu],
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
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
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
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
BibRef
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.,
Objective analysis and bit rate analysis of HEVC compressed 4D-fMRI
images,
IJIST(29), No. 3, September 2019, pp. 283-296.
DOI Link
1908
BibRef
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
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,
MVA(31), No. 3, March 2020, pp. Article21.
Springer DOI
2004
BibRef
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,
MedImg(39), No. 7, July 2020, pp. 2363-2373.
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
Kassani, P.H.[P. Hosseinzadeh],
Xiao, L.,
Zhang, G.,
Stephen, J.M.,
Wilson, T.W.,
Calhoun, V.D.,
Wang, Y.P.,
Causality-Based Feature Fusion for Brain Neuro-Developmental Analysis,
MedImg(39), No. 11, November 2020, pp. 3290-3299.
IEEE DOI
2011
Functional magnetic resonance imaging, Brain modeling,
Feature extraction, Time series analysis, History, Correlation,
polynomial neural network
BibRef
Hu, D.,
Zhang, H.,
Wu, Z.,
Wang, F.,
Wang, L.,
Smith, J.K.,
Lin, W.,
Li, G.,
Shen, D.,
Disentangled-Multimodal Adversarial Autoencoder: Application to
Infant Age Prediction With Incomplete Multimodal Neuroimages,
MedImg(39), No. 12, December 2020, pp. 4137-4149.
IEEE DOI
2012
Functional magnetic resonance imaging, Brain modeling,
Predictive models, Data models, Biological system modeling,
magnetic resonance imaging
BibRef
Guo, S.,
Fessler, J.A.,
Noll, D.C.,
High-Resolution Oscillating Steady-State fMRI Using Patch-Tensor
Low-Rank Reconstruction,
MedImg(39), No. 12, December 2020, pp. 4357-4368.
IEEE DOI
2012
Tensors, Functional magnetic resonance imaging,
Image reconstruction, Signal to noise ratio, Oscillators,
prospective undersampling
BibRef
Lin, Q.A.[Qi-Ang],
Man, Z.X.[Zheng-Xing],
Cao, Y.C.[Yong-Chun],
Deng, T.[Tao],
Han, C.C.[Cheng-Cheng],
Cao, C.G.[Chuan-Gui],
Zhang, L.J.[Lin-Jun],
Zeng, S.[Sitao],
Gao, R.T.[Rui-Ting],
Wang, W.[Weilan],
Ji, J.S.[Jin-Shui],
Huang, X.D.[Xiao-Di],
Classifying functional nuclear images with convolutional neural
networks: A survey,
IET-IPR(14), No. 14, December 2020, pp. 3300-3313.
DOI Link
2012
Survey, Nuclear Imaging.
BibRef
Takada, S.,
Togo, R.,
Ogawa, T.,
Haseyama, M.,
Estimation Of Visual Contents Based On Question Answering From Human
Brain Activity,
ICIP20(61-65)
IEEE DOI
2011
Functional magnetic resonance imaging, Visualization,
Feature extraction, Decoding, Brain, Training, Data models,
recurrent neural network (RNN)
BibRef
Takada, S.,
Togo, R.,
Ogawa, T.,
Haseyama, M.,
Generation of Viewed Image Captions From Human Brain Activity Via
Unsupervised Text Latent Space,
ICIP20(2521-2525)
IEEE DOI
2011
Functional magnetic resonance imaging, Semantics, Training,
Feature extraction, Brain modeling, Computer architecture,
functional magnetic resonance imaging (fMRI).
BibRef
Vergara, V.M.,
Calhoun, V.D.,
Nicotine Addiction Decreases Dynamic Connectivity Frequency In
Functional Magnetic Resonance Imaging,
SSIAI20(34-37)
IEEE DOI
2009
biomedical MRI, brain, medical disorders, neurophysiology,
dysfunctional frequency spectrum, nicotine addiction,
nicotine addiction
BibRef
Miller, R.L.,
Calhoun, V.D.,
Transient Spectral Peak Analysis Reveals Distinct Temporal Activation
Profiles for Different Functional Brain Networks,
SSIAI20(108-111)
IEEE DOI
2009
biomedical MRI, brain, independent component analysis,
medical image processing, neurophysiology,
network connectivity
BibRef
Sendi, M.S.E.,
Zendehrouh, E.,
Fu, Z.,
Mahmoudi, B.,
Miller, R.L.,
Calhoun, V.D.,
A Machine Learning Model for Exploring Aberrant Functional Network
Connectivity Transition in Schizophrenia,
SSIAI20(112-115)
IEEE DOI
2009
biomedical MRI, brain, learning (artificial intelligence),
medical disorders, medical image processing, neurophysiology,
feature learning
BibRef
Kumar, N.[Nishant],
Hoffmann, N.[Nico],
Oelschlägel, M.[Martin],
Koch, E.[Edmund],
Kirsch, M.[Matthias],
Gumhold, S.[Stefan],
Structural Similarity Based Anatomical and Functional Brain Imaging
Fusion,
MBIA19(121-129).
Springer DOI
1912
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
BibRef
Wang, Z.[Ze],
Mapping the Spatio-temporal Functional Coherence in the Resting Brain,
MBIA19(39-48).
Springer DOI
1912
BibRef
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
BibRef
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
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
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
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
And:
fMRI Activation Network Analysis Using Bose-Einstein Entropy,
SSSPR16(218-228).
Springer DOI
1611
See also Network Edge Entropy from Maxwell-Boltzmann Statistics.
BibRef
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
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
EEG-MRI, EEG-fMRI, Combined Analysis .