21.9.7.5 Functional Magnetic Resonance, fMRI

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

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

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

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

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.F.[Jie-Feng], 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

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

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

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

Sen, B., Parhi, K.K.,
Graph-Theoretic Properties of Sub-Graph Entropy,
SPLetters(28), 2021, pp. 135-139.
IEEE DOI 2101
Entropy, Measurement, Functional magnetic resonance imaging, Noise measurement, Upper bound, Task analysis, Stability criteria, sub-graph entropy BibRef

Zhang, J.J.[Jian-Jia], Wang, L.[Lei], Zhou, L.P.[Lu-Ping], Li, W.Q.[Wan-Qing],
Beyond Covariance: SICE and Kernel Based Visual Feature Representation,
IJCV(129), No. 2, February 2021, pp. 300-320.
Springer DOI 2102
BibRef
And: A1, A3, A2, A4:
Exploring Compact Representation of SICE Matrices for Functional Brain Network Classification,
MLMI14(59-67).
Springer DOI 1410
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

Ting, C.M., Samdin, S.B., Tang, M., Ombao, H.,
Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach,
MedImg(40), No. 2, February 2021, pp. 468-480.
IEEE DOI 2102
Nonhomogeneous media, Hidden Markov models, Task analysis, Brain modeling, Switches, Functional magnetic resonance imaging, fMRI BibRef

Chen, C.M.[Chun-Ming], Yang, H.C.[Hui-Chieh], Hsieh, H.H.[Hsin-Hua], Liao, T.Y.[Tsai-Ying], Huang, Y.C.[Yen-Chih], Peng, S.L.[Shin-Lei],
Characterization of regional differences in cerebral vascular response to breath holding using BOLD fMRI,
IJIST(31), No. 1, 2021, pp. 180-188.
DOI Link 2102
autoregulation, cerebral blood flow, hypercapnia, reactivity, sex BibRef

Bai, Y., Gong, Y., Bai, J., Liu, J., Deng, H.W., Calhoun, V., Wang, Y.P.,
A Joint Analysis of Multi-Paradigm fMRI Data With Its Application to Cognitive Study,
MedImg(40), No. 3, March 2021, pp. 951-962.
IEEE DOI 2103
Functional magnetic resonance imaging, Collaboration, Task analysis, Data models, Correlation, Imaging, Feature extraction, feature selection BibRef

Hu, R.Y.[Rong-Yao], Peng, Z.[Ziwen], Zhu, X.F.[Xiao-Feng], Gan, J.Z.[Jiang-Zhang], Zhu, Y.H.[Yong-Hua], Ma, J.[Junbo], Wu, G.R.[Guo-Rong],
Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification,
MedImg(40), No. 12, December 2021, pp. 3843-3855.
IEEE DOI 2112
Functional magnetic resonance imaging, Correlation, High frequency, Brain, Medical diagnosis, Biomedical imaging, resting state fMRI BibRef

Ji, J.Z.[Jun-Zhong], Liu, J.[Jinduo], Han, L.[Lu], Wang, F.[Feipeng],
Estimating Effective Connectivity by Recurrent Generative Adversarial Networks,
MedImg(40), No. 12, December 2021, pp. 3326-3336.
IEEE DOI 2112
Functional magnetic resonance imaging, Time series analysis, Generators, Data models, Brain modeling, Logic gates, fMRI time series BibRef

Gobbi, S.[Susanna], Lee, Y.[Yoojin], Homolya, I.[István], Tobler, P.N.[Philippe N.], Hare, T.A.[Todd A.], Nagy, Z.[Zoltan],
On the reproducibility of in vivo temporal signal-to-noise ratio and its utility as a predictor of subject-level t-values in a functional magnetic resonance imaging study,
IJIST(31), No. 4, 2021, pp. 1849-1860.
DOI Link 2112
fMRI, quality assurance, reliability, reproducibility, temporal SNR BibRef

Mahankali, N.S.[Naga Sailaja], Raghavan, M.[Mohan], Channappayya, S.S.[Sumohana S.],
No-Reference Video Quality Assessment Using Voxel-Wise fMRI Models of the Visual Cortex,
SPLetters(29), 2022, pp. 319-323.
IEEE DOI 2202
Visualization, Functional magnetic resonance imaging, Brain modeling, Predictive models, Prediction algorithms, Encoding, video quality assessment (VQA) BibRef

Han, Y.[Yue], Lin, Q.H.[Qiu-Hua], Kuang, L.D.[Li-Dan], Gong, X.F.[Xiao-Feng], Cong, F.Y.[Feng-Yu], Wang, Y.P.[Yu-Ping], Calhoun, V.D.[Vince D.],
Low-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition With Spatial Sparsity Constraint,
MedImg(41), No. 3, March 2022, pp. 667-679.
IEEE DOI 2203
Tensors, Functional magnetic resonance imaging, Matrix decomposition, Feature extraction, Sparse matrices, core tensor BibRef

Ting, C.M.[Chee-Ming], Skipper, J.I.[Jeremy I.], Noman, F.[Fuad], Small, S.L.[Steven L.], Ombao, H.[Hernando],
Separating Stimulus-Induced and Background Components of Dynamic Functional Connectivity in Naturalistic fMRI,
MedImg(41), No. 6, June 2022, pp. 1431-1442.
IEEE DOI 2206
Functional magnetic resonance imaging, Sparse matrices, Correlation, Matrix decomposition, Motion pictures, Task analysis, fMRI BibRef

Karakasis, P.A.[Paris A.], Liavas, A.P.[Athanasios P.], Sidiropoulos, N.D.[Nicholas D.], Simos, P.G.[Panagiotis G.], Papadaki, E.[Efrosini],
Multisubject Task-Related fMRI Data Processing via a Two-Stage Generalized Canonical Correlation Analysis,
IP(31), 2022, pp. 4011-4022.
IEEE DOI 2206
Task analysis, Functional magnetic resonance imaging, Data models, Estimation, Data processing, Compounds, MAX-VAR BibRef

Zhao, Y.[Yu], Gao, Y.R.[Yu-Rui], Li, M.[Muwei], Anderson, A.W.[Adam W.], Ding, Z.H.[Zhao-Hua], Gore, J.C.[John C.],
Functional Parcellation of Human Brain Using Localized Topo-Connectivity Mapping,
MedImg(41), No. 10, October 2022, pp. 2670-2680.
IEEE DOI 2210
Functional magnetic resonance imaging, Image reconstruction, White matter, Filtering algorithms, Biomedical engineering, functional parcellation BibRef

Dan, T.T.[Ting-Ting], Huang, Z.[Zhuobin], Cai, H.M.[Hong-Min], Laurienti, P.J.[Paul J.], Wu, G.R.[Guo-Rong],
Learning Brain Dynamics of Evolving Manifold Functional MRI Data Using Geometric-Attention Neural Network,
MedImg(41), No. 10, October 2022, pp. 2752-2763.
IEEE DOI 2210
Manifolds, Convolution, Symmetric matrices, Biological neural networks, Trajectory, Matrix decomposition, functional connectivity BibRef

Zhang, H.[Hao], Song, R.[Ran], Wang, L.P.[Li-Ping], Zhang, L.[Lin], Wang, D.W.[Da-Wei], Wang, C.[Cong], Zhang, W.[Wei],
Classification of Brain Disorders in rs-fMRI via Local-to-Global Graph Neural Networks,
MedImg(42), No. 2, February 2023, pp. 444-455.
IEEE DOI 2302
Diseases, Feature extraction, Biomarkers, Functional magnetic resonance imaging, Deep learning, attention BibRef

Peng, L.[Liang], Wang, N.[Nan], Xu, J.[Jie], Zhu, X.F.[Xiao-Feng], Li, X.X.[Xiao-Xiao],
GATE: Graph CCA for Temporal Self-Supervised Learning for Label-Efficient fMRI Analysis,
MedImg(42), No. 2, February 2023, pp. 391-402.
IEEE DOI 2302
Functional magnetic resonance imaging, Brain modeling, Sociology, Image reconstruction, Task analysis, Logic gates, self-supervised learning BibRef

Huang, Z.Y.[Zhong-Yu], Du, C.D.[Chang-De], Wang, Y.H.[Ying-Heng], Fu, K.C.[Kai-Cheng], He, H.G.[Hui-Guang],
Graph-Enhanced Emotion Neural Decoding,
MedImg(42), No. 8, August 2023, pp. 2262-2273.
IEEE DOI 2308
Decoding, Task analysis, Bipartite graph, Functional magnetic resonance imaging, Recording, Encoding, representation BibRef

Hu, Y.[Yao], Huang, Z.A.[Zhi-An], Liu, R.[Rui], Xue, X.M.[Xiao-Ming], Sun, X.Y.[Xiao-Yan], Song, L.Q.[Lin-Qi], Tan, K.C.[Kay Chen],
Source Free Semi-Supervised Transfer Learning for Diagnosis of Mental Disorders on fMRI Scans,
PAMI(45), No. 11, November 2023, pp. 13778-13795.
IEEE DOI 2310
BibRef

Qu, G.[Gang], Orlichenko, A.[Anton], Wang, J.Q.[Jun-Qi], Zhang, G.[Gemeng], Xiao, L.[Li], Zhang, K.[Kun], Wilson, T.W.[Tony W.], Stephen, J.M.[Julia M.], Calhoun, V.D.[Vince D.], Wang, Y.P.[Yu-Ping],
Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks,
MedImg(43), No. 4, April 2024, pp. 1568-1578.
IEEE DOI 2404
Transformers, Brain modeling, Logic gates, Neuroimaging, Functional magnetic resonance imaging, Computational modeling, human cognition BibRef

Guo, S.[Shouchang], Fessler, J.A.[Jeffrey A.], Noll, D.C.[Douglas C.],
Manifold Regularizer for High-Resolution fMRI Joint Reconstruction and Dynamic Quantification,
MedImg(43), No. 8, August 2024, pp. 2937-2948.
IEEE DOI 2408
Manifolds, Functional magnetic resonance imaging, Image reconstruction, Physics, Steady-state, Signal to noise ratio, joint reconstruction and quantification BibRef

Liu, J.[Jinduo], Han, L.[Lu], Ji, J.Z.[Jun-Zhong],
MCAN: Multimodal Causal Adversarial Networks for Dynamic Effective Connectivity Learning From fMRI and EEG Data,
MedImg(43), No. 8, August 2024, pp. 2913-2923.
IEEE DOI 2408
Functional magnetic resonance imaging, Electroencephalography, Generators, Time series analysis, Task analysis, Learning systems, adversarial training BibRef

Kuang, L.D.[Li-Dan], He, Z.M.[Zhi-Ming],
Coupled Shift-Invariant Tensorial Spatial ICA Applied to Multi-Group Complex-Valued Task-Related and Resting-State fMRI Data,
ICIVC22(468-472)
IEEE DOI 2301
Delay effects, Network analyzers, Independent component analysis, Functional magnetic resonance imaging, Data models, multi-group analysis BibRef

Valenti, S.[Simone], Sparacino, L.[Laura], Pernice, R.[Riccardo], Marinazzo, D.[Daniele], Almgren, H.[Hannes], Comelli, A.[Albert], Faes, L.[Luca],
Assessing High-Order Interdependencies Through Static O-Information Measures Computed on Resting State fMRI Intrinsic Component Networks,
AIRCAD22(386-397).
Springer DOI 2208
BibRef