Format
Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 101

1.

Ten simple rules for predictive modeling of individual differences in neuroimaging.

Scheinost D, Noble S, Horien C, Greene AS, Lake EM, Salehi M, Gao S, Shen X, O'Connor D, Barron DS, Yip SW, Rosenberg MD, Constable RT.

Neuroimage. 2019 Jun;193:35-45. doi: 10.1016/j.neuroimage.2019.02.057. Epub 2019 Mar 1. Review.

2.

Using connectome-based predictive modeling to predict individual behavior from brain connectivity.

Shen X, Finn ES, Scheinost D, Rosenberg MD, Chun MM, Papademetris X, Constable RT.

Nat Protoc. 2017 Mar;12(3):506-518. doi: 10.1038/nprot.2016.178. Epub 2017 Feb 9.

3.

Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets.

Yoo K, Rosenberg MD, Hsu WT, Zhang S, Li CR, Scheinost D, Constable RT, Chun MM.

Neuroimage. 2018 Feb 15;167:11-22. doi: 10.1016/j.neuroimage.2017.11.010. Epub 2017 Nov 6.

4.

Task modulations and clinical manifestations in the brain functional connectome in 1615 fMRI datasets.

Kaufmann T, Alnæs D, Brandt CL, Doan NT, Kauppi K, Bettella F, Lagerberg TV, Berg AO, Djurovic S, Agartz I, Melle IS, Ueland T, Andreassen OA, Westlye LT.

Neuroimage. 2017 Feb 15;147:243-252. doi: 10.1016/j.neuroimage.2016.11.073. Epub 2016 Dec 1.

PMID:
27916665
5.

Predicting individual brain functional connectivity using a Bayesian hierarchical model.

Dai T, Guo Y; Alzheimer's Disease Neuroimaging Initiative.

Neuroimage. 2017 Feb 15;147:772-787. doi: 10.1016/j.neuroimage.2016.11.048. Epub 2016 Dec 1.

6.

Transfer learning improves resting-state functional connectivity pattern analysis using convolutional neural networks.

Vakli P, Deák-Meszlényi RJ, Hermann P, Vidnyánszky Z.

Gigascience. 2018 Dec 1;7(12). doi: 10.1093/gigascience/giy130.

7.

A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes.

Li H, Parikh NA, He L.

Front Neurosci. 2018 Jul 24;12:491. doi: 10.3389/fnins.2018.00491. eCollection 2018.

8.

Robust prediction of individual creative ability from brain functional connectivity.

Beaty RE, Kenett YN, Christensen AP, Rosenberg MD, Benedek M, Chen Q, Fink A, Qiu J, Kwapil TR, Kane MJ, Silvia PJ.

Proc Natl Acad Sci U S A. 2018 Jan 30;115(5):1087-1092. doi: 10.1073/pnas.1713532115. Epub 2018 Jan 16.

9.

Quantifying functional connectivity in multi-subject fMRI data using component models.

Madsen KH, Churchill NW, Mørup M.

Hum Brain Mapp. 2017 Feb;38(2):882-899. doi: 10.1002/hbm.23425. Epub 2016 Oct 14.

PMID:
27739635
10.

Pattern classification of large-scale functional brain networks: identification of informative neuroimaging markers for epilepsy.

Zhang J, Cheng W, Wang Z, Zhang Z, Lu W, Lu G, Feng J.

PLoS One. 2012;7(5):e36733. doi: 10.1371/journal.pone.0036733. Epub 2012 May 17.

11.

Methylphenidate Modulates Functional Network Connectivity to Enhance Attention.

Rosenberg MD, Zhang S, Hsu WT, Scheinost D, Finn ES, Shen X, Constable RT, Li CS, Chun MM.

J Neurosci. 2016 Sep 14;36(37):9547-57. doi: 10.1523/JNEUROSCI.1746-16.2016.

12.

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
13.

Connectome-scale assessments of structural and functional connectivity in MCI.

Zhu D, Li K, Terry DP, Puente AN, Wang L, Shen D, Miller LS, Liu T.

Hum Brain Mapp. 2014 Jul;35(7):2911-23. doi: 10.1002/hbm.22373. Epub 2013 Sep 30.

14.

Machine learning classification of resting state functional connectivity predicts smoking status.

Pariyadath V, Stein EA, Ross TJ.

Front Hum Neurosci. 2014 Jun 16;8:425. doi: 10.3389/fnhum.2014.00425. eCollection 2014.

15.

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.

16.

Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework.

He L, Li H, Holland SK, Yuan W, Altaye M, Parikh NA.

Neuroimage Clin. 2018 Jan 31;18:290-297. doi: 10.1016/j.nicl.2018.01.032. eCollection 2018.

17.

Resting-state functional magnetic resonance imaging in clade C HIV: within-group association with neurocognitive function.

du Plessis L, Paul RH, Hoare J, Stein DJ, Taylor PA, Meintjes EM, Joska JA.

J Neurovirol. 2017 Dec;23(6):875-885. doi: 10.1007/s13365-017-0581-5. Epub 2017 Oct 2.

18.

Structural Basis of Large-Scale Functional Connectivity in the Mouse.

Grandjean J, Zerbi V, Balsters JH, Wenderoth N, Rudin M.

J Neurosci. 2017 Aug 23;37(34):8092-8101. doi: 10.1523/JNEUROSCI.0438-17.2017. Epub 2017 Jul 17.

19.

A multivariate distance-based analytic framework for connectome-wide association studies.

Shehzad Z, Kelly C, Reiss PT, Cameron Craddock R, Emerson JW, McMahon K, Copland DA, Castellanos FX, Milham MP.

Neuroimage. 2014 Jun;93 Pt 1:74-94. doi: 10.1016/j.neuroimage.2014.02.024. Epub 2014 Feb 28.

20.

Toward discovery science of human brain function.

Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, Beckmann CF, Adelstein JS, Buckner RL, Colcombe S, Dogonowski AM, Ernst M, Fair D, Hampson M, Hoptman MJ, Hyde JS, Kiviniemi VJ, Kötter R, Li SJ, Lin CP, Lowe MJ, Mackay C, Madden DJ, Madsen KH, Margulies DS, Mayberg HS, McMahon K, Monk CS, Mostofsky SH, Nagel BJ, Pekar JJ, Peltier SJ, Petersen SE, Riedl V, Rombouts SA, Rypma B, Schlaggar BL, Schmidt S, Seidler RD, Siegle GJ, Sorg C, Teng GJ, Veijola J, Villringer A, Walter M, Wang L, Weng XC, Whitfield-Gabrieli S, Williamson P, Windischberger C, Zang YF, Zhang HY, Castellanos FX, Milham MP.

Proc Natl Acad Sci U S A. 2010 Mar 9;107(10):4734-9. doi: 10.1073/pnas.0911855107. Epub 2010 Feb 22.

Supplemental Content

Support Center