These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

107 related articles for article (PubMed ID: 38466115)

  • 1. Utilizing graph convolutional networks for identification of mild cognitive impairment from single modal fMRI data: a multiconnection pattern combination approach.
    He J; Wang P; He J; Sun C; Xu X; Zhang L; Wang X; Gao X
    Cereb Cortex; 2024 Mar; 34(3):. PubMed ID: 38466115
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks.
    Liu J; Tan G; Lan W; Wang J
    BMC Bioinformatics; 2020 Nov; 21(Suppl 6):123. PubMed ID: 33203351
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Early prediction of dementia using fMRI data with a graph convolutional network approach.
    Han S; Sun Z; Zhao K; Duan F; Caiafa CF; Zhang Y; Solé-Casals J
    J Neural Eng; 2024 Jan; 21(1):. PubMed ID: 38215493
    [No Abstract]   [Full Text] [Related]  

  • 4. A survey on applications and analysis methods of functional magnetic resonance imaging for Alzheimer's disease.
    Forouzannezhad P; Abbaspour A; Fang C; Cabrerizo M; Loewenstein D; Duara R; Adjouadi M
    J Neurosci Methods; 2019 Apr; 317():121-140. PubMed ID: 30593787
    [TBL] [Abstract][Full Text] [Related]  

  • 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. PubMed ID: 26363784
    [TBL] [Abstract][Full Text] [Related]  

  • 6. High-sensitivity neuroimaging biomarkers for the identification of amnestic mild cognitive impairment based on resting-state fMRI and a triple network model.
    Yu E; Liao Z; Tan Y; Qiu Y; Zhu J; Han Z; Wang J; Wang X; Wang H; Chen Y; Zhang Q; Li Y; Mao D; Ding Z
    Brain Imaging Behav; 2019 Feb; 13(1):1-14. PubMed ID: 28466439
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer's Disease and Cognitive Impairment.
    Kolla S; Falakshahi H; Abrol A; Fu Z; Calhoun VD
    Sensors (Basel); 2024 Jan; 24(3):. PubMed ID: 38339531
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Temporal and Spatial Analysis of Alzheimer's Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network.
    Sun H; Wang A; He S
    Int J Environ Res Public Health; 2022 Apr; 19(8):. PubMed ID: 35457373
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Quantitative Assessment of Resting-State Functional Connectivity MRI to Differentiate Amnestic Mild Cognitive Impairment, Late-Onset Alzheimer's Disease From Normal Subjects.
    Mohammadian F; Zare Sadeghi A; Noroozian M; Malekian V; Abbasi Sisara M; Hashemi H; Mobarak Salari H; Valizadeh G; Samadi F; Sodaei F; Saligheh Rad H
    J Magn Reson Imaging; 2023 Jun; 57(6):1702-1712. PubMed ID: 36226735
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease.
    Spasov S; Passamonti L; Duggento A; Liò P; Toschi N;
    Neuroimage; 2019 Apr; 189():276-287. PubMed ID: 30654174
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer's disease.
    Zhang Y; Xue L; Zhang S; Yang J; Zhang Q; Wang M; Wang L; Zhang M; Jiang J; Li Y;
    Alzheimers Res Ther; 2024 Mar; 16(1):60. PubMed ID: 38481280
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Multi-dimensional persistent feature analysis identifies connectivity patterns of resting-state brain networks in Alzheimer's disease.
    Li J; Bian C; Luo H; Chen D; Cao L; Liang H
    J Neural Eng; 2021 Feb; 18(1):. PubMed ID: 33152713
    [No Abstract]   [Full Text] [Related]  

  • 13. Alzheimer's disease diagnosis framework from incomplete multimodal data using convolutional neural networks.
    Abdelaziz M; Wang T; Elazab A
    J Biomed Inform; 2021 Sep; 121():103863. PubMed ID: 34229061
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Multi-label classification of Alzheimer's disease stages from resting-state fMRI-based correlation connectivity data and deep learning.
    Alorf A; Khan MUG
    Comput Biol Med; 2022 Dec; 151(Pt A):106240. PubMed ID: 36423532
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease.
    Liu M; Li F; Yan H; Wang K; Ma Y; ; Shen L; Xu M
    Neuroimage; 2020 Mar; 208():116459. PubMed ID: 31837471
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Personalized Functional Connectivity Based Spatio-Temporal Aggregated Attention Network for MCI Identification.
    Cui W; Ma Y; Ren J; Liu J; Ma G; Liu H; Li Y
    IEEE Trans Neural Syst Rehabil Eng; 2023; 31():2257-2267. PubMed ID: 37104108
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM.
    Hojjati SH; Ebrahimzadeh A; Khazaee A; Babajani-Feremi A;
    J Neurosci Methods; 2017 Apr; 282():69-80. PubMed ID: 28286064
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Random walks on B distributed resting-state functional connectivity to identify Alzheimer's disease and Mild Cognitive Impairment.
    Rahimiasl M; Moghadam Charkari N; Ghaderi F;
    Clin Neurophysiol; 2021 Oct; 132(10):2540-2550. PubMed ID: 34455312
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Multi-level fusion network for mild cognitive impairment identification using multi-modal neuroimages.
    Xu H; Zhong S; Zhang Y
    Phys Med Biol; 2023 Apr; 68(9):. PubMed ID: 37019116
    [No Abstract]   [Full Text] [Related]  

  • 20. Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network.
    Bae J; Stocks J; Heywood A; Jung Y; Jenkins L; Hill V; Katsaggelos A; Popuri K; Rosen H; Beg MF; Wang L;
    Neurobiol Aging; 2021 Mar; 99():53-64. PubMed ID: 33422894
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.