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Items: 1 to 20 of 103

1.

Multivariate Deep Learning Classification of Alzheimer's Disease Based on Hierarchical Partner Matching Independent Component Analysis.

Qiao J, Lv Y, Cao C, Wang Z, Li A.

Front Aging Neurosci. 2018 Dec 17;10:417. doi: 10.3389/fnagi.2018.00417. eCollection 2018.

2.

Learning Brain Connectivity Sub-networks by Group- Constrained Sparse Inverse Covariance Estimation for Alzheimer's Disease Classification.

Li Y, Liu J, Huang J, Li Z, Liang P.

Front Neuroinform. 2018 Sep 7;12:58. doi: 10.3389/fninf.2018.00058. eCollection 2018.

3.

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.

PMID:
25907414
4.

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
5.

Aberrant Functional Network Connectivity as a Biomarker of Generalized Anxiety Disorder.

Qiao J, Li A, Cao C, Wang Z, Sun J, Xu G.

Front Hum Neurosci. 2017 Dec 19;11:626. doi: 10.3389/fnhum.2017.00626. eCollection 2017.

6.

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
7.

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases.

Rondina JM, Ferreira LK, de Souza Duran FL, Kubo R, Ono CR, Leite CC, Smid J, Nitrini R, Buchpiguel CA, Busatto GF.

Neuroimage Clin. 2017 Nov 9;17:628-641. doi: 10.1016/j.nicl.2017.10.026. eCollection 2018.

8.

Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM.

Wang Y, Li TQ.

Front Hum Neurosci. 2015 May 8;9:259. doi: 10.3389/fnhum.2015.00259. eCollection 2015.

9.

Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease.

Jie B, Liu M, Shen D.

Med Image Anal. 2018 Jul;47:81-94. doi: 10.1016/j.media.2018.03.013. Epub 2018 Apr 4.

PMID:
29702414
10.

Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease.

Guo H, Zhang F, Chen J, Xu Y, Xiang J.

Front Neurosci. 2017 Nov 21;11:615. doi: 10.3389/fnins.2017.00615. eCollection 2017.

11.

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.

13.

Differentiating Alzheimer's Disease from Dementia with Lewy Bodies Using a Deep Learning Technique Based on Structural Brain Connectivity.

Wada A, Tsuruta K, Irie R, Kamagata K, Maekawa T, Fujita S, Koshino S, Kumamaru K, Suzuki M, Nakanishi A, Hori M, Aoki S.

Magn Reson Med Sci. 2018 Dec 3. doi: 10.2463/mrms.mp.2018-0091. [Epub ahead of print]

14.

Exploring multifractal-based features for mild Alzheimer's disease classification.

Ni H, Zhou L, Ning X, Wang L; Alzheimer's Disease Neuroimaging Initiative (ADNI).

Magn Reson Med. 2016 Jul;76(1):259-69. doi: 10.1002/mrm.25853. Epub 2015 Jul 20.

PMID:
26193379
15.

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis.

Liu M, Cheng D, Wang K, Wang Y; Alzheimer’s Disease Neuroimaging Initiative.

Neuroinformatics. 2018 Oct;16(3-4):295-308. doi: 10.1007/s12021-018-9370-4.

PMID:
29572601
16.

Functional neural circuits that underlie developmental stuttering.

Qiao J, Wang Z, Zhao G, Huo Y, Herder CL, Sikora CO, Peterson BS.

PLoS One. 2017 Jul 31;12(7):e0179255. doi: 10.1371/journal.pone.0179255. eCollection 2017.

17.

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.

18.

Analysis of structural brain MRI and multi-parameter classification for Alzheimer's disease.

Zhang Y, Liu S.

Biomed Tech (Berl). 2018 Jul 26;63(4):427-437. doi: 10.1515/bmt-2016-0239.

PMID:
28622141
19.

Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction.

Rahim M, Thirion B, Comtat C, Varoquaux G; Alzheimer’s Disease Neuroimaging Initiative.

IEEE J Sel Top Signal Process. 2016 Oct;10(7):120-1213. doi: 10.1109/JSTSP.2016.2600400. Epub 2016 Aug 15.

20.

A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease.

de Vos F, Koini M, Schouten TM, Seiler S, van der Grond J, Lechner A, Schmidt R, de Rooij M, Rombouts SARB.

Neuroimage. 2018 Feb 15;167:62-72. doi: 10.1016/j.neuroimage.2017.11.025. Epub 2017 Nov 14.

PMID:
29155080

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