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Items: 1 to 20 of 105

1.

Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI.

Zhang T, Zhao Z, Zhang C, Zhang J, Jin Z, Li L.

Front Psychiatry. 2019 Aug 27;10:572. doi: 10.3389/fpsyt.2019.00572. eCollection 2019.

2.

A novel joint HCPMMP method for automatically classifying Alzheimer's and different stage MCI patients.

Sheng J, Wang B, Zhang Q, Liu Q, Ma Y, Liu W, Shao M, Chen B.

Behav Brain Res. 2019 Jun 3;365:210-221. doi: 10.1016/j.bbr.2019.03.004. Epub 2019 Mar 2.

PMID:
30836158
3.

Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM.

Hojjati SH, Ebrahimzadeh A, Khazaee A, Babajani-Feremi A; Alzheimer’s Disease Neuroimaging Initiative.

J Neurosci Methods. 2017 Apr 15;282:69-80. doi: 10.1016/j.jneumeth.2017.03.006. Epub 2017 Mar 9.

PMID:
28286064
4.

Integrating the Local Property and Topological Structure in the Minimum Spanning Tree Brain Functional Network for Classification of Early Mild Cognitive Impairment.

Cui X, Xiang J, Wang B, Xiao J, Niu Y, Chen J.

Front Neurosci. 2018 Oct 8;12:701. doi: 10.3389/fnins.2018.00701. eCollection 2018.

5.

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
6.

Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using MRI and Structural Network Features.

Wei R, Li C, Fogelson N, Li L.

Front Aging Neurosci. 2016 Apr 19;8:76. doi: 10.3389/fnagi.2016.00076. eCollection 2016.

7.

Changes in thalamic connectivity in the early and late stages of amnestic mild cognitive impairment: a resting-state functional magnetic resonance study from ADNI.

Cai S, Huang L, Zou J, Jing L, Zhai B, Ji G, von Deneen KM, Ren J, Ren A; Alzheimer’s Disease Neuroimaging Initiative.

PLoS One. 2015 Feb 13;10(2):e0115573. doi: 10.1371/journal.pone.0115573. eCollection 2015.

8.

Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI.

Hojjati SH, Ebrahimzadeh A, Babajani-Feremi A.

Front Neurol. 2019 Aug 30;10:904. doi: 10.3389/fneur.2019.00904. eCollection 2019.

9.

Enriched white matter connectivity networks for accurate identification of MCI patients.

Wee CY, Yap PT, Li W, Denny K, Browndyke JN, Potter GG, Welsh-Bohmer KA, Wang L, Shen D.

Neuroimage. 2011 Feb 1;54(3):1812-22. doi: 10.1016/j.neuroimage.2010.10.026. Epub 2010 Oct 21.

10.

Brain connectivity hyper-network for MCI classification.

Jie B, Shen D, Zhang D.

Med Image Comput Comput Assist Interv. 2014;17(Pt 2):724-32.

PMID:
25485444
11.

Hyper-connectivity of functional networks for brain disease diagnosis.

Jie B, Wee CY, Shen D, Zhang D.

Med Image Anal. 2016 Aug;32:84-100. doi: 10.1016/j.media.2016.03.003. Epub 2016 Mar 24.

12.

Altered amplitude of low-frequency fluctuations in early and late mild cognitive impairment and Alzheimer's disease.

Liang P, Xiang J, Liang H, Qi Z, Li K, Alzheimer's Disease NeuroImaging Initiative.

Curr Alzheimer Res. 2014 May;11(4):389-98.

PMID:
24720892
13.

Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm.

Beheshti I, Demirel H, Matsuda H; Alzheimer's Disease Neuroimaging Initiative.

Comput Biol Med. 2017 Apr 1;83:109-119. doi: 10.1016/j.compbiomed.2017.02.011. Epub 2017 Feb 27.

PMID:
28260614
14.

Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI.

Challis E, Hurley P, Serra L, Bozzali M, Oliver S, Cercignani M.

Neuroimage. 2015 May 15;112:232-243. doi: 10.1016/j.neuroimage.2015.02.037. Epub 2015 Feb 28.

15.

A Deep Learning approach for Diagnosis of Mild Cognitive Impairment Based on MRI Images.

Gorji HT, Kaabouch N.

Brain Sci. 2019 Aug 28;9(9). pii: E217. doi: 10.3390/brainsci9090217.

16.

Frequency-Dependent Changes in the Amplitude of Low-Frequency Fluctuations in Mild Cognitive Impairment with Mild Depression.

Li Y, Jing B, Liu H, Li Y, Gao X, Li Y, Mu B, Yu H, Cheng J, Barker PB, Wang H, Han Y.

J Alzheimers Dis. 2017;58(4):1175-1187. doi: 10.3233/JAD-161282.

PMID:
28550250
17.

Fused Sparse Network Learning for Longitudinal Analysis of Mild Cognitive Impairment.

Yang P, Zhou F, Ni D, Xu Y, Chen S, Wang T, Lei B.

IEEE Trans Cybern. 2019 Sep 30. doi: 10.1109/TCYB.2019.2940526. [Epub ahead of print]

PMID:
31567112
18.

Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI.

Khazaee A, Ebrahimzadeh A, Babajani-Feremi A; Alzheimer’s Disease Neuroimaging Initiative.

Behav Brain Res. 2017 Mar 30;322(Pt B):339-350. doi: 10.1016/j.bbr.2016.06.043. Epub 2016 Jun 23.

PMID:
27345822
19.

Resting-state whole-brain functional connectivity networks for MCI classification using L2-regularized logistic regression.

Zhang X, Hu B, Ma X, Xu L.

IEEE Trans Nanobioscience. 2015 Mar;14(2):237-47. doi: 10.1109/TNB.2015.2403274. Epub 2015 Feb 12.

PMID:
25700453
20.

Inclusion of Neuropsychological Scores in Atrophy Models Improves Diagnostic Classification of Alzheimer's Disease and Mild Cognitive Impairment.

Goryawala M, Zhou Q, Barker W, Loewenstein DA, Duara R, Adjouadi M.

Comput Intell Neurosci. 2015;2015:865265. doi: 10.1155/2015/865265. Epub 2015 May 25.

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