386 related articles for article (PubMed ID: 31816152)
21. Retrieving and reconstructing conceptually similar images from fMRI with latent diffusion models and a neuro-inspired brain decoding model.
Ferrante M; Boccato T; Passamonti L; Toschi N
J Neural Eng; 2024 Jun; 21(4):. PubMed ID: 38885689
[No Abstract] [Full Text] [Related]
22. Utilizing wavelet deep learning network to classify different states of task-fMRI for verifying activation regions.
Gui S; Gui R
Int J Neurosci; 2020 Jun; 130(6):583-594. PubMed ID: 31778088
[No Abstract] [Full Text] [Related]
23. Functional annotation of human cognitive states using deep graph convolution.
Zhang Y; Tetrel L; Thirion B; Bellec P
Neuroimage; 2021 May; 231():117847. PubMed ID: 33582272
[TBL] [Abstract][Full Text] [Related]
24. Multimodal deep neural decoding reveals highly resolved spatiotemporal profile of visual object representation in humans.
Watanabe N; Miyoshi K; Jimura K; Shimane D; Keerativittayayut R; Nakahara K; Takeda M
Neuroimage; 2023 Jul; 275():120164. PubMed ID: 37169115
[TBL] [Abstract][Full Text] [Related]
25. Working memory load-dependent changes in cortical network connectivity estimated by machine learning.
Eryilmaz H; Dowling KF; Hughes DE; Rodriguez-Thompson A; Tanner A; Huntington C; Coon WG; Roffman JL
Neuroimage; 2020 Aug; 217():116895. PubMed ID: 32360929
[TBL] [Abstract][Full Text] [Related]
26. Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.
Jang H; Plis SM; Calhoun VD; Lee JH
Neuroimage; 2017 Jan; 145(Pt B):314-328. PubMed ID: 27079534
[TBL] [Abstract][Full Text] [Related]
27. Characterization of deep neural network features by decodability from human brain activity.
Horikawa T; Aoki SC; Tsukamoto M; Kamitani Y
Sci Data; 2019 Feb; 6():190012. PubMed ID: 30747910
[TBL] [Abstract][Full Text] [Related]
28. Decoding fMRI data with support vector machines and deep neural networks.
Liang Y; Bo K; Meyyappan S; Ding M
J Neurosci Methods; 2024 Jan; 401():110004. PubMed ID: 37914001
[TBL] [Abstract][Full Text] [Related]
29. Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics.
He T; Kong R; Holmes AJ; Nguyen M; Sabuncu MR; Eickhoff SB; Bzdok D; Feng J; Yeo BTT
Neuroimage; 2020 Feb; 206():116276. PubMed ID: 31610298
[TBL] [Abstract][Full Text] [Related]
30. Explainable fMRI-based brain decoding via spatial temporal-pyramid graph convolutional network.
Ye Z; Qu Y; Liang Z; Wang M; Liu Q
Hum Brain Mapp; 2023 May; 44(7):2921-2935. PubMed ID: 36852610
[TBL] [Abstract][Full Text] [Related]
31. Classification of Task-State fMRI Data Based on Circle-EMD and Machine Learning.
Gui R; Chen T; Nie H
Comput Intell Neurosci; 2020; 2020():7691294. PubMed ID: 32802027
[TBL] [Abstract][Full Text] [Related]
32. A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification.
Hu J; Kuang Y; Liao B; Cao L; Dong S; Li P
Comput Intell Neurosci; 2019; 2019():5065214. PubMed ID: 32082370
[TBL] [Abstract][Full Text] [Related]
33. Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age.
Wahlang I; Maji AK; Saha G; Chakrabarti P; Jasinski M; Leonowicz Z; Jasinska E
Sensors (Basel); 2022 Feb; 22(5):. PubMed ID: 35270913
[TBL] [Abstract][Full Text] [Related]
34. An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.
Wu W; Li D; Du J; Gao X; Gu W; Zhao F; Feng X; Yan H
Comput Math Methods Med; 2020; 2020():6789306. PubMed ID: 32733596
[TBL] [Abstract][Full Text] [Related]
35. Hierarchical multi-resolution mesh networks for brain decoding.
Onal Ertugrul I; Ozay M; Yarman Vural FT
Brain Imaging Behav; 2018 Aug; 12(4):1067-1083. PubMed ID: 28980144
[TBL] [Abstract][Full Text] [Related]
36. Test-retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing, and resting state.
Cao H; Plichta MM; Schäfer A; Haddad L; Grimm O; Schneider M; Esslinger C; Kirsch P; Meyer-Lindenberg A; Tost H
Neuroimage; 2014 Jan; 84():888-900. PubMed ID: 24055506
[TBL] [Abstract][Full Text] [Related]
37. Deep attentive spatio-temporal feature learning for automatic resting-state fMRI denoising.
Heo KS; Shin DH; Hung SC; Lin W; Zhang H; Shen D; Kam TE
Neuroimage; 2022 Jul; 254():119127. PubMed ID: 35337965
[TBL] [Abstract][Full Text] [Related]
38. A visual encoding model based on deep neural networks and transfer learning for brain activity measured by functional magnetic resonance imaging.
Zhang C; Qiao K; Wang L; Tong L; Hu G; Zhang RY; Yan B
J Neurosci Methods; 2019 Sep; 325():108318. PubMed ID: 31255596
[TBL] [Abstract][Full Text] [Related]
39. Identifying Brain Networks at Multiple Time Scales via Deep Recurrent Neural Network.
Cui Y; Zhao S; Wang H; Xie L; Chen Y; Han J; Guo L; Zhou F; Liu T
IEEE J Biomed Health Inform; 2019 Nov; 23(6):2515-2525. PubMed ID: 30475739
[TBL] [Abstract][Full Text] [Related]
40. A study of decoding human brain activities from simultaneous data of EEG and fMRI using MVPA.
Zafar R; Kamel N; Naufal M; Malik AS; Dass SC; Ahmad RF; Abdullah JM; Reza F
Australas Phys Eng Sci Med; 2018 Sep; 41(3):633-645. PubMed ID: 29948968
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
