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Items: 1 to 20 of 101

1.

Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models.

Shappell H, Caffo BS, Pekar JJ, Lindquist MA.

Neuroimage. 2019 Feb 10;191:243-257. doi: 10.1016/j.neuroimage.2019.02.013. [Epub ahead of print]

PMID:
30753927
2.

Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI.

Taghia J, Ryali S, Chen T, Supekar K, Cai W, Menon V.

Neuroimage. 2017 Jul 15;155:271-290. doi: 10.1016/j.neuroimage.2017.02.083. Epub 2017 Mar 4.

3.

A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data.

Warnick R, Guindani M, Erhardt E, Allen E, Calhoun V, Vannucci M.

J Am Stat Assoc. 2018;113(521):134-151. doi: 10.1080/01621459.2017.1379404. Epub 2018 May 16.

PMID:
30853734
4.

Interpreting temporal fluctuations in resting-state functional connectivity MRI.

Liégeois R, Laumann TO, Snyder AZ, Zhou J, Yeo BTT.

Neuroimage. 2017 Dec;163:437-455. doi: 10.1016/j.neuroimage.2017.09.012. Epub 2017 Sep 12. Review.

PMID:
28916180
5.

Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models.

Ting CM, Ombao H, Samdin SB, Salleh SH.

IEEE Trans Med Imaging. 2018 Apr;37(4):1011-1023. doi: 10.1109/TMI.2017.2780185.

PMID:
29610078
6.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

Suk HI, Wee CY, Lee SW, Shen D.

Neuroimage. 2016 Apr 1;129:292-307. doi: 10.1016/j.neuroimage.2016.01.005. Epub 2016 Jan 14.

7.

Capturing Dynamic Connectivity from Resting State fMRI using Time-Varying Graphical Lasso.

Cai B, Zhang G, Zhang A, Stephen JM, Wilson TW, Calhoun VD, Wang Y.

IEEE Trans Biomed Eng. 2018 Nov 9. doi: 10.1109/TBME.2018.2880428. [Epub ahead of print]

PMID:
30418876
8.

Spatiotemporal Modeling of Brain Dynamics Using Resting-State Functional Magnetic Resonance Imaging with Gaussian Hidden Markov Model.

Chen S, Langley J, Chen X, Hu X.

Brain Connect. 2016 May;6(4):326-34. doi: 10.1089/brain.2015.0398. Epub 2016 Mar 23.

PMID:
27008543
9.

Time-dependence of graph theory metrics in functional connectivity analysis.

Chiang S, Cassese A, Guindani M, Vannucci M, Yeh HJ, Haneef Z, Stern JM.

Neuroimage. 2016 Jan 15;125:601-615. doi: 10.1016/j.neuroimage.2015.10.070. Epub 2015 Oct 27.

10.

Hidden Markov models: the best models for forager movements?

Joo R, Bertrand S, Tam J, Fablet R.

PLoS One. 2013 Aug 23;8(8):e71246. doi: 10.1371/journal.pone.0071246. eCollection 2013.

11.

Resting state networks in empirical and simulated dynamic functional connectivity.

Glomb K, Ponce-Alvarez A, Gilson M, Ritter P, Deco G.

Neuroimage. 2017 Oct 1;159:388-402. doi: 10.1016/j.neuroimage.2017.07.065. Epub 2017 Aug 3.

PMID:
28782678
12.

Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity.

Chiang S, Vankov ER, Yeh HJ, Guindani M, Vannucci M, Haneef Z, Stern JM.

PLoS One. 2018 Jan 10;13(1):e0190220. doi: 10.1371/journal.pone.0190220. eCollection 2018.

13.

Predictive assessment of models for dynamic functional connectivity.

Nielsen SFV, Schmidt MN, Madsen KH, Mørup M.

Neuroimage. 2018 May 1;171:116-134. doi: 10.1016/j.neuroimage.2017.12.084. Epub 2017 Dec 30.

PMID:
29292135
14.

Identifying Dynamic Functional Connectivity Changes in Dementia with Lewy Bodies Based on Product Hidden Markov Models.

Sourty M, Thoraval L, Roquet D, Armspach JP, Foucher J, Blanc F.

Front Comput Neurosci. 2016 Jun 23;10:60. doi: 10.3389/fncom.2016.00060. eCollection 2016.

15.

Resting state dynamics meets anatomical structure: Temporal multiple kernel learning (tMKL) model.

Surampudi SG, Misra J, Deco G, Bapi RS, Sharma A, Roy D.

Neuroimage. 2019 Jan 1;184:609-620. doi: 10.1016/j.neuroimage.2018.09.054. Epub 2018 Sep 27.

PMID:
30267857
16.

Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models.

Ou J, Xie L, Jin C, Li X, Zhu D, Jiang R, Chen Y, Zhang J, Li L, Liu T.

Brain Topogr. 2015 Sep;28(5):666-679. doi: 10.1007/s10548-014-0406-2. Epub 2014 Oct 21.

17.

Principal States of Dynamic Functional Connectivity Reveal the Link Between Resting-State and Task-State Brain: An fMRI Study.

Cheng L, Zhu Y, Sun J, Deng L, He N, Yang Y, Ling H, Ayaz H, Fu Y, Tong S.

Int J Neural Syst. 2018 Sep;28(7):1850002. doi: 10.1142/S0129065718500028. Epub 2018 Jan 25.

PMID:
29607681
18.

Dynamic functional connectivity using state-based dynamic community structure: method and application to opioid analgesia.

Robinson LF, Atlas LY, Wager TD.

Neuroimage. 2015 Mar;108:274-91. doi: 10.1016/j.neuroimage.2014.12.034. Epub 2014 Dec 20.

PMID:
25534114
19.

Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information.

Yaesoubi M, Allen EA, Miller RL, Calhoun VD.

Neuroimage. 2015 Oct 15;120:133-42. doi: 10.1016/j.neuroimage.2015.07.002. Epub 2015 Jul 8.

20.

Hidden Markov event sequence models: toward unsupervised functional MRI brain mapping.

Faisan S, Thoraval L, Armspach JP, Foucher JR, Metz-Lutz MN, Heitz F.

Acad Radiol. 2005 Jan;12(1):25-36.

PMID:
15691723

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