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Items: 1 to 20 of 101

1.

Identification of Subclinical Language Deficit using Machine Learning Classification based on Post-stroke Functional Connectivity derived from Low Frequency Oscillations.

Mohanty R, Nair VA, Tellapragada N, Wiliams LM Jr, Kang TJ, Prabhakaran V.

Brain Connect. 2018 Nov 6. doi: 10.1089/brain.2018.0597. [Epub ahead of print]

PMID:
30398379
2.

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.

3.

Frequency-specific alternations in the amplitude of low-frequency fluctuations in chronic tinnitus.

Chen YC, Xia W, Luo B, Muthaiah VP, Xiong Z, Zhang J, Wang J, Salvi R, Teng GJ.

Front Neural Circuits. 2015 Oct 29;9:67. doi: 10.3389/fncir.2015.00067. eCollection 2015.

5.

Frequency-dependent changes in local intrinsic oscillations in chronic primary insomnia: A study of the amplitude of low-frequency fluctuations in the resting state.

Zhou F, Huang S, Zhuang Y, Gao L, Gong H.

Neuroimage Clin. 2016 May 26;15:458-465. doi: 10.1016/j.nicl.2016.05.011. eCollection 2017.

6.

Diagnosis of Autism Spectrum Disorders Using Multi-Level High-Order Functional Networks Derived From Resting-State Functional MRI.

Zhao F, Zhang H, Rekik I, An Z, Shen D.

Front Hum Neurosci. 2018 May 14;12:184. doi: 10.3389/fnhum.2018.00184. eCollection 2018.

7.

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification.

Chen X, Zhang H, Zhang L, Shen C, Lee SW, Shen D.

Hum Brain Mapp. 2017 Oct;38(10):5019-5034. doi: 10.1002/hbm.23711. Epub 2017 Jun 30.

8.

Implication of the Slow-5 Oscillations in the Disruption of the Default-Mode Network in Healthy Aging and Stroke.

La C, Nair VA, Mossahebi P, Young BM, Chacon M, Jensen M, Birn RM, Meyerand ME, Prabhakaran V.

Brain Connect. 2016 Jul;6(6):482-95. doi: 10.1089/brain.2015.0375.

9.

Early Findings on Functional Connectivity Correlates of Behavioral Outcomes of Brain-Computer Interface Stroke Rehabilitation Using Machine Learning.

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 Sep 11;12:624. doi: 10.3389/fnins.2018.00624. eCollection 2018.

10.

Frequency-Dependent Altered Functional Connections of Default Mode Network in Alzheimer's Disease.

Li Y, Yao H, Lin P, Zheng L, Li C, Zhou B, Wang P, Zhang Z, Wang L, An N, Wang J, Zhang X.

Front Aging Neurosci. 2017 Aug 3;9:259. doi: 10.3389/fnagi.2017.00259. eCollection 2017.

11.

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.

PMID:
26363784
12.

Higher frequency network activity flow predicts lower frequency node activity in intrinsic low-frequency BOLD fluctuations.

Bajaj S, Adhikari BM, Dhamala M.

PLoS One. 2013 May 15;8(5):e64466. doi: 10.1371/journal.pone.0064466. Print 2013.

13.

Abnormal functional connectivity of amygdala in late-onset depression was associated with cognitive deficits.

Yue Y, Yuan Y, Hou Z, Jiang W, Bai F, Zhang Z.

PLoS One. 2013 Sep 10;8(9):e75058. doi: 10.1371/journal.pone.0075058. eCollection 2013.

14.

Functional connectivity changes in the language network during stroke recovery.

Nair VA, Young BM, La C, Reiter P, Nadkarni TN, Song J, Vergun S, Addepally NS, Mylavarapu K, Swartz JL, Jensen MB, Chacon MR, Sattin JA, Prabhakaran V.

Ann Clin Transl Neurol. 2015 Feb;2(2):185-95. doi: 10.1002/acn3.165. Epub 2015 Jan 15.

15.

Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity--A multi-center study.

Chen H, Duan X, Liu F, Lu F, Ma X, Zhang Y, Uddin LQ, Chen H.

Prog Neuropsychopharmacol Biol Psychiatry. 2016 Jan 4;64:1-9. doi: 10.1016/j.pnpbp.2015.06.014. Epub 2015 Jul 4.

PMID:
26148789
16.

Classification of obsessive-compulsive disorder from resting-state fMRI.

Sen B, Bernstein GA, Tingting Xu, Mueller BA, Schreiner MW, Cullen KR, Parhi KK.

Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:3606-3609. doi: 10.1109/EMBC.2016.7591508.

PMID:
28269076
17.

The contribution of different frequency bands of fMRI data to the correlation with EEG alpha rhythm.

Zhan Z, Xu L, Zuo T, Xie D, Zhang J, Yao L, Wu X.

Brain Res. 2014 Jan 16;1543:235-43. doi: 10.1016/j.brainres.2013.11.016. Epub 2013 Nov 22.

PMID:
24275197
18.

Characteristics of Resting-State Functional Connectivity in Intractable Unilateral Temporal Lobe Epilepsy Patients with Impaired Executive Control Function.

Zhang C, Yang H, Qin W, Liu C, Qi Z, Chen N, Li K.

Front Hum Neurosci. 2017 Dec 13;11:609. doi: 10.3389/fnhum.2017.00609. eCollection 2017.

19.

Structurofunctional resting-state networks correlate with motor function in chronic stroke.

Kalinosky BT, Berrios Barillas R, Schmit BD.

Neuroimage Clin. 2017 Jul 29;16:610-623. doi: 10.1016/j.nicl.2017.07.002. eCollection 2017.

20.

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