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Items: 1 to 20 of 101

1.

Comparing Cyclicity Analysis With Pre-established Functional Connectivity Methods to Identify Individuals and Subject Groups Using Resting State fMRI.

Shahsavarani S, Abraham IT, Zimmerman BJ, Baryshnikov YM, Husain FT.

Front Comput Neurosci. 2020 Jan 20;13:94. doi: 10.3389/fncom.2019.00094. eCollection 2019.

2.

Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping.

Meszlényi RJ, Hermann P, Buza K, Gál V, Vidnyánszky Z.

Front Neurosci. 2017 Feb 17;11:75. doi: 10.3389/fnins.2017.00075. eCollection 2017.

3.

Dissociating tinnitus patients from healthy controls using resting-state cyclicity analysis and clustering.

Zimmerman BJ, Abraham I, Schmidt SA, Baryshnikov Y, Husain FT.

Netw Neurosci. 2018 Oct 1;3(1):67-89. doi: 10.1162/netn_a_00053. eCollection 2019.

4.

Optimization of rs-fMRI Pre-processing for Enhanced Signal-Noise Separation, Test-Retest Reliability, and Group Discrimination.

Shirer WR, Jiang H, Price CM, Ng B, Greicius MD.

Neuroimage. 2015 Aug 15;117:67-79. doi: 10.1016/j.neuroimage.2015.05.015. Epub 2015 May 15.

PMID:
25987368
5.

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture.

Meszlényi RJ, Buza K, Vidnyánszky Z.

Front Neuroinform. 2017 Oct 17;11:61. doi: 10.3389/fninf.2017.00061. eCollection 2017.

6.

Tinnitus- and Task-Related Differences in Resting-State Networks.

Lanting C, WoźAniak A, van Dijk P, Langers DRM.

Adv Exp Med Biol. 2016;894:175-187. doi: 10.1007/978-3-319-25474-6_19.

PMID:
27080658
7.

Spatio-temporal dynamics of resting-state brain networks improve single-subject prediction of schizophrenia diagnosis.

Kottaram A, Johnston L, Ganella E, Pantelis C, Kotagiri R, Zalesky A.

Hum Brain Mapp. 2018 Sep;39(9):3663-3681. doi: 10.1002/hbm.24202. Epub 2018 May 10.

PMID:
29749660
8.

Replicability of Neural and Behavioral Measures of Tinnitus Handicap in Civilian and Military Populations: Preliminary Results.

Husain FT, Schmidt SA, Tai Y, Granato EC, Ramos P, Sherman P, Esquivel C.

Am J Audiol. 2019 Apr 22;28(1S):191-208. doi: 10.1044/2019_AJA-TTR17-18-0039.

PMID:
31022364
9.

Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification.

Wee CY, Yang S, Yap PT, Shen D; Alzheimer’s Disease Neuroimaging Initiative.

Brain Imaging Behav. 2016 Jun;10(2):342-56. doi: 10.1007/s11682-015-9408-2.

10.

Default mode, dorsal attention and auditory resting state networks exhibit differential functional connectivity in tinnitus and hearing loss.

Schmidt SA, Akrofi K, Carpenter-Thompson JR, Husain FT.

PLoS One. 2013 Oct 2;8(10):e76488. doi: 10.1371/journal.pone.0076488. eCollection 2013.

11.

Correcting for Blood Arrival Time in Global Mean Regression Enhances Functional Connectivity Analysis of Resting State fMRI-BOLD Signals.

Erdoğan SB, Tong Y, Hocke LM, Lindsey KP, deB Frederick B.

Front Hum Neurosci. 2016 Jun 28;10:311. doi: 10.3389/fnhum.2016.00311. eCollection 2016.

12.

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
13.

Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object tracking compared to resting-state.

Dørum ES, Kaufmann T, Alnæs D, Andreassen OA, Richard G, Kolskår KK, Nordvik JE, Westlye LT.

Neuroimage. 2017 Mar 1;148:364-372. doi: 10.1016/j.neuroimage.2017.01.048. Epub 2017 Jan 20.

PMID:
28111190
14.

Using Deep Learning and Resting-State fMRI to Classify Chronic Pain Conditions.

Santana AN, Cifre I, de Santana CN, Montoya P.

Front Neurosci. 2019 Dec 17;13:1313. doi: 10.3389/fnins.2019.01313. eCollection 2019.

15.

Increased Resting-State Cerebellar-Cerebral Functional Connectivity Underlying Chronic Tinnitus.

Feng Y, Chen YC, Lv H, Xia W, Mao CN, Bo F, Chen H, Xu JJ, Yin X.

Front Aging Neurosci. 2018 Mar 5;10:59. doi: 10.3389/fnagi.2018.00059. eCollection 2018.

16.

Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI.

Zhu Q, Huang J, Xu X.

Biomed Eng Online. 2018 Mar 13;17(1):32. doi: 10.1186/s12938-018-0464-x.

17.

ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging.

Griffanti L, Salimi-Khorshidi G, Beckmann CF, Auerbach EJ, Douaud G, Sexton CE, Zsoldos E, Ebmeier KP, Filippini N, Mackay CE, Moeller S, Xu J, Yacoub E, Baselli G, Ugurbil K, Miller KL, Smith SM.

Neuroimage. 2014 Jul 15;95:232-47. doi: 10.1016/j.neuroimage.2014.03.034. Epub 2014 Mar 21.

18.

Resting State Functional Connectivity MRI among Spectral MEG Current Sources in Children on the Autism Spectrum.

Datko M, Gougelet R, Huang MX, Pineda JA.

Front Neurosci. 2016 Jun 9;10:258. doi: 10.3389/fnins.2016.00258. eCollection 2016.

19.

Abnormal Spontaneous Neural Activity of the Central Auditory System Changes the Functional Connectivity in the Tinnitus Brain: A Resting-State Functional MRI Study.

Cai WW, Li ZC, Yang QT, Zhang T.

Front Neurosci. 2019 Dec 20;13:1314. doi: 10.3389/fnins.2019.01314. eCollection 2019.

20.

Alteration of functional connectivity in tinnitus brain revealed by resting-state fMRI? A pilot study.

Kim JY, Kim YH, Lee S, Seo JH, Song HJ, Cho JH, Chang Y.

Int J Audiol. 2012 May;51(5):413-7. doi: 10.3109/14992027.2011.652677. Epub 2012 Jan 30.

PMID:
22283490

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