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M-GCN: A Multimodal Graph Convolutional Network to Integrate Functional and Structural Connectomics Data to Predict Multidimensional Phenotypic Characterizations.

, , , , , и . MIDL, том 143 из Proceedings of Machine Learning Research, стр. 119-130. PMLR, (2021)

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Другие публикации лиц с тем же именем

Psilocybin induces spatially constrained alterations in thalamic functional organizaton and connectivity., , , , , , и . NeuroImage, (2022)Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data., , , , и . MICCAI (3), том 11766 из Lecture Notes in Computer Science, стр. 709-717. Springer, (2019)A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes., , , , , и . MICCAI (7), том 12907 из Lecture Notes in Computer Science, стр. 625-636. Springer, (2021)A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data., , , , и . MICCAI (3), том 11072 из Lecture Notes in Computer Science, стр. 163-171. Springer, (2018)Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage., , , , , , и . NeuroImage, (2018)A Unified Bayesian Approach to Extract Network-Based Functional Differences from a Heterogeneous Patient Cohort., , , и . CNI@MICCAI, том 10511 из Lecture Notes in Computer Science, стр. 60-69. Springer, (2017)Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI., , , , , , , , и . NeuroImage, (2014)Corrigendum to 'Psilocybin induces spatially constrained alterations in thalamic functional organizaton and connectivity': Neuroimage 2022 Oct 15;260:119434., , , , , , и . NeuroImage, (июля 2023)A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes., , , , , , и . CoRR, (2021)M-GCN: A Multimodal Graph Convolutional Network to Integrate Functional and Structural Connectomics Data to Predict Multidimensional Phenotypic Characterizations., , , , , и . MIDL, том 143 из Proceedings of Machine Learning Research, стр. 119-130. PMLR, (2021)