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).
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IEEE DOI
0907
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
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Prados, E.[Emmanuel],
Pons, J.P.[Jean-Philippe],
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Faugeras, O.D.[Olivier D.],
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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
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equation; intrinsic distance function; brain connectivity mapping;
diffusion tensor imaging
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Prados, E.[Emmanuel],
Soatto, S.[Stefano],
Lenglet, C.[Christophe],
Pons, J.P.[Jean-Philippe],
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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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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
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
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
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
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
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.
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1411
Brain modeling
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
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
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
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
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
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
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
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
Sahoo, D.,
Satterthwaite, T.D.,
Davatzikos, C.,
Hierarchical Extraction of Functional Connectivity Components in
Human Brain Using Resting-State fMRI,
MedImg(40), No. 3, March 2021, pp. 940-950.
IEEE DOI
2103
Correlation, Cognitive neuroscience, Organizations,
Functional magnetic resonance imaging, Sparse matrices,
fMRI
BibRef
Balachandrasekaran, A.[Arvind],
Cohen, A.L.[Alexander L.],
Afacan, O.[Onur],
Warfield, S.K.[Simon K.],
Gholipour, A.[Ali],
Reducing the Effects of Motion Artifacts in fMRI: A Structured Matrix
Completion Approach,
MedImg(41), No. 1, January 2022, pp. 172-185.
IEEE DOI
2201
Functional magnetic resonance imaging, Time series analysis,
Correlation, Volume measurement,
fcMRI
BibRef
Ji, J.Z.[Jun-Zhong],
Zhang, Y.Q.[Ya-Qin],
Functional Brain Network Classification Based on Deep Graph Hashing
Learning,
MedImg(41), No. 10, October 2022, pp. 2891-2902.
IEEE DOI
2210
Feature extraction, Diseases, Semantics, Codes, Hash functions,
Binary codes, Brain modeling, Brain network classification,
semantic space
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
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
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
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
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
Baker, M.[Mary],
Kapse, K.[Kushal],
McMahon, A.[Allison],
O'Boyle, M.[Michael],
Connectivity in math-gifted adolescents: Comparing structural equation
modeling, granger causality, and dynamic causal modeling,
Southwest12(93-96).
IEEE DOI
1205
fMRI analysis.
BibRef
Eklund, A.[Anders],
Andersson, M.[Mats],
Knutsson, H.[Hans],
A functional connectivity inspired approach to non-local fMRI analysis,
ICIP12(1245-1248).
IEEE DOI
1302
BibRef
Eklund, A.[Anders],
Friman, O.[Ola],
Andersson, M.[Mats],
Knutsson, H.[Hans],
A GPU accelerated interactive interface for exploratory functional
connectivity analysis of FMRI data,
ICIP11(1589-1592).
IEEE DOI
1201
BibRef
Chen, X.H.[Xiao-Hui],
Wang, Z.J.[Z. Jane],
McKeown, M.J.[Martin J.],
FMRI group studies of brain connectivity via a group robust Lasso,
ICIP10(589-592).
IEEE DOI
1009
BibRef
Emeriau, S.,
Blanchard, F.,
Poline, JB.,
Pierot, L.,
Bittar, E.,
Connectivity feature extraction for spatio-functional clustering of
fMRI data,
IPTA10(38-43).
IEEE DOI
1007
BibRef
Lashkari, D.[Danial],
Sridharan, R.[Ramesh],
Vul, E.[Edward],
Hsieh, P.J.[Po-Jang],
Kanwisher, N.[Nancy],
Golland, P.[Polina],
Nonparametric hierarchical Bayesian model for functional brain
parcellation,
MMBIA10(15-22).
IEEE DOI
1006
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
EEG-MRI, EEG-fMRI, Combined Analysis .