20.9.7.7 fMRI for Brain Connectivity Analysis

Chapter Contents (Back)
Magnetic Resonance. fMRI. Brain Connectivity.

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).

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
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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

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
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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

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.
IEEE DOI 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


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

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EEG-MRI, EEG-fMRI, Combined Analysis .


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