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Machine learning in resting-state fMRI analysis.

Khosla M, Jamison K, Ngo GH, Kuceyeski A, Sabuncu MR.

Magn Reson Imaging. 2019 Jun 4. pii: S0730-725X(18)30685-4. doi: 10.1016/j.mri.2019.05.031. [Epub ahead of print] Review.


Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State (Hunger/Satiety).

Al-Zubaidi A, Mertins A, Heldmann M, Jauch-Chara K, Münte TF.

Front Hum Neurosci. 2019 May 28;13:164. doi: 10.3389/fnhum.2019.00164. eCollection 2019.


Resting-state functional magnetic resonance imaging for surgical planning in pediatric patients: a preliminary experience.

Roland JL, Griffin N, Hacker CD, Vellimana AK, Akbari SH, Shimony JS, Smyth MD, Leuthardt EC, Limbrick DD.

J Neurosurg Pediatr. 2017 Dec;20(6):583-590. doi: 10.3171/2017.6.PEDS1711. Epub 2017 Sep 29.


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.


Characterizing Functional Connectivity Differences in Aging Adults using Machine Learning on Resting State fMRI Data.

Vergun S, Deshpande AS, Meier TB, Song J, Tudorascu DL, Nair VA, Singh V, Biswal BB, Meyerand ME, Birn RM, Prabhakaran V.

Front Comput Neurosci. 2013 Apr 25;7:38. doi: 10.3389/fncom.2013.00038. eCollection 2013.


Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease.

Khazaee A, Ebrahimzadeh A, Babajani-Feremi A.

Brain Imaging Behav. 2016 Sep;10(3):799-817. doi: 10.1007/s11682-015-9448-7.


Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory.

Khazaee A, Ebrahimzadeh A, Babajani-Feremi A.

Clin Neurophysiol. 2015 Nov;126(11):2132-41. doi: 10.1016/j.clinph.2015.02.060. Epub 2015 Apr 1.


Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments.

De Marco M, Beltrachini L, Biancardi A, Frangi AF, Venneri A.

Alzheimer Dis Assoc Disord. 2017 Oct-Dec;31(4):278-286. doi: 10.1097/WAD.0000000000000208.


How restful is it with all that noise? Comparison of Interleaved silent steady state (ISSS) and conventional imaging in resting-state fMRI.

Andoh J, Ferreira M, Leppert IR, Matsushita R, Pike B, Zatorre RJ.

Neuroimage. 2017 Feb 15;147:726-735. doi: 10.1016/j.neuroimage.2016.11.065. Epub 2016 Nov 27.


Spatially regularized machine learning for task and resting-state fMRI.

Song X, Panych LP, Chen NK.

J Neurosci Methods. 2016 Jan 15;257:214-28. doi: 10.1016/j.jneumeth.2015.10.001. Epub 2015 Oct 16.


The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features.

Cui Z, Gong G.

Neuroimage. 2018 Sep;178:622-637. doi: 10.1016/j.neuroimage.2018.06.001. Epub 2018 Jun 2.


Manipulating brain connectivity with δ⁹-tetrahydrocannabinol: a pharmacological resting state FMRI study.

Klumpers LE, Cole DM, Khalili-Mahani N, Soeter RP, Te Beek ET, Rombouts SA, van Gerven JM.

Neuroimage. 2012 Nov 15;63(3):1701-11. doi: 10.1016/j.neuroimage.2012.07.051. Epub 2012 Aug 1.


Concurrent tACS-fMRI Reveals Causal Influence of Power Synchronized Neural Activity on Resting State fMRI Connectivity.

Bächinger M, Zerbi V, Moisa M, Polania R, Liu Q, Mantini D, Ruff C, Wenderoth N.

J Neurosci. 2017 May 3;37(18):4766-4777. doi: 10.1523/JNEUROSCI.1756-16.2017. Epub 2017 Apr 6.


Erroneous Resting-State fMRI Connectivity Maps Due to Prolonged Arterial Arrival Time and How to Fix Them.

Jahanian H, Christen T, Moseley ME, Zaharchuk G.

Brain Connect. 2018 Aug;8(6):362-370. doi: 10.1089/brain.2018.0610.


Classification and Extraction of Resting State Networks Using Healthy and Epilepsy fMRI Data.

Vergun S, Gaggl W, Nair VA, Suhonen JI, Birn RM, Ahmed AS, Meyerand ME, Reuss J, DeYoe EA, Prabhakaran V.

Front Neurosci. 2016 Sep 27;10:440. eCollection 2016.


Correlating Resting-State Functional Magnetic Resonance Imaging Connectivity by Independent Component Analysis-Based Epileptogenic Zones with Intracranial Electroencephalogram Localized Seizure Onset Zones and Surgical Outcomes in Prospective Pediatric Intractable Epilepsy Study.

Boerwinkle VL, Mohanty D, Foldes ST, Guffey D, Minard CG, Vedantam A, Raskin JS, Lam S, Bond M, Mirea L, Adelson PD, Wilfong AA, Curry DJ.

Brain Connect. 2017 Sep;7(7):424-442. doi: 10.1089/brain.2016.0479.


ReStNeuMap: a tool for automatic extraction of resting-state functional MRI networks in neurosurgical practice.

Zacà D, Jovicich J, Corsini F, Rozzanigo U, Chioffi F, Sarubbo S.

J Neurosurg. 2018 Oct 1:1-8. doi: 10.3171/2018.4.JNS18474. [Epub ahead of print]


A unified machine learning method for task-related and resting state fMRI data analysis.

Song X, Chen NK.

Conf Proc IEEE Eng Med Biol Soc. 2014;2014:6426-9. doi: 10.1109/EMBC.2014.6945099.


Effects of Field-Map Distortion Correction on Resting State Functional Connectivity MRI.

Togo H, Rokicki J, Yoshinaga K, Hisatsune T, Matsuda H, Haga N, Hanakawa T.

Front Neurosci. 2017 Dec 1;11:656. doi: 10.3389/fnins.2017.00656. eCollection 2017.


Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity.

Mohanty R, Sinha AM, Remsik AB, Dodd KC, Young BM, Jacobson T, McMillan M, Thoma J, Advani H, Nair VA, Kang TJ, Caldera K, Edwards DF, Williams JC, Prabhakaran V.

Front Neurosci. 2018 May 29;12:353. doi: 10.3389/fnins.2018.00353. eCollection 2018.

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